From 47ae045241462ab8441d8ea43db02c74716e9bd3 Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Thu, 12 Sep 2019 18:03:11 -0400 Subject: [PATCH 01/31] cpu --- Project2-Stream-Compaction/src/main.cpp | 2 +- .../stream_compaction/CMakeLists.txt | 2 +- .../stream_compaction/cpu.cu | 45 +++++++++-- .../stream_compaction/testing_helpers.hpp | 76 +++++++++++++++++++ 4 files changed, 118 insertions(+), 7 deletions(-) create mode 100644 Project2-Stream-Compaction/stream_compaction/testing_helpers.hpp diff --git a/Project2-Stream-Compaction/src/main.cpp b/Project2-Stream-Compaction/src/main.cpp index d016553..fd98f07 100644 --- a/Project2-Stream-Compaction/src/main.cpp +++ b/Project2-Stream-Compaction/src/main.cpp @@ -13,7 +13,7 @@ #include #include "testing_helpers.hpp" -const int SIZE = 1 << 8; // feel free to change the size of array +const int SIZE = 1 << 3; // feel free to change the size of array const int NPOT = SIZE - 3; // Non-Power-Of-Two int *a = new int[SIZE]; int *b = new int[SIZE]; diff --git a/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt b/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt index cdbef77..272a7e8 100644 --- a/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt +++ b/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt @@ -13,5 +13,5 @@ set(SOURCE_FILES cuda_add_library(stream_compaction ${SOURCE_FILES} - OPTIONS -arch=sm_20 + OPTIONS -arch=sm_70 ) diff --git a/Project2-Stream-Compaction/stream_compaction/cpu.cu b/Project2-Stream-Compaction/stream_compaction/cpu.cu index a2d3e6c..6d591fa 100644 --- a/Project2-Stream-Compaction/stream_compaction/cpu.cu +++ b/Project2-Stream-Compaction/stream_compaction/cpu.cu @@ -18,9 +18,19 @@ namespace StreamCompaction { * (Optional) For better understanding before starting moving to GPU, you can simulate your GPU scan in this function first. */ void scan(int n, int *odata, const int *idata) { - timer().startCpuTimer(); - // TODO - timer().endCpuTimer(); + bool started_timer = true; + try { + timer().startCpuTimer(); + } catch (const std::exception& e) { + started_timer = false; + } + + // TODO + odata[0] = 0; + for (int i = 1; i < n; i++) + odata[i] = odata[i - 1] + idata[i-1]; + if(started_timer) + timer().endCpuTimer(); } /** @@ -31,10 +41,26 @@ namespace StreamCompaction { int compactWithoutScan(int n, int *odata, const int *idata) { timer().startCpuTimer(); // TODO + int j = 0; + for (int i = 0; i < n; i++) + if (idata[i] != 0) + odata[j++] = idata[i]; timer().endCpuTimer(); - return -1; + return j; } + void printArray(int n, int *a, bool abridged = false) { + printf(" [ "); + for (int i = 0; i < n; i++) { + if (abridged && i + 2 == 15 && n > 16) { + i = n - 2; + printf("... "); + } + printf("%3d ", a[i]); + } + printf("]\n"); + } + /** * CPU stream compaction using scan and scatter, like the parallel version. * @@ -43,8 +69,17 @@ namespace StreamCompaction { int compactWithScan(int n, int *odata, const int *idata) { timer().startCpuTimer(); // TODO + int *binary_idata = new int[n]; + int *final_index = new int[n]; + for (int i = 0; i < n; i++) + binary_idata[i] = idata[i] > 0 ? 1 : 0; + scan(n, final_index, binary_idata); + for (int i = 0; i < n; i++) + if (binary_idata[i] == 1) + odata[final_index[i]] = idata[i]; timer().endCpuTimer(); - return -1; + return final_index[n-1] + (idata[n-1] > 0); } } + } diff --git a/Project2-Stream-Compaction/stream_compaction/testing_helpers.hpp b/Project2-Stream-Compaction/stream_compaction/testing_helpers.hpp new file mode 100644 index 0000000..b28a8d2 --- /dev/null +++ b/Project2-Stream-Compaction/stream_compaction/testing_helpers.hpp @@ -0,0 +1,76 @@ +#pragma once + +#include +#include +#include +#include +#include + +template +int cmpArrays(int n, T *a, T *b) { + for (int i = 0; i < n; i++) { + if (a[i] != b[i]) { + printf(" a[%d] = %d, b[%d] = %d\n", i, a[i], i, b[i]); + return 1; + } + } + return 0; +} + +void printDesc(const char *desc) { + printf("==== %s ====\n", desc); +} + +template +void printCmpResult(int n, T *a, T *b) { + printf(" %s \n", + cmpArrays(n, a, b) ? "FAIL VALUE" : "passed"); +} + +template +void printCmpLenResult(int n, int expN, T *a, T *b) { + if (n != expN) { + printf(" expected %d elements, got %d\n", expN, n); + } + printf(" %s \n", + (n == -1 || n != expN) ? "FAIL COUNT" : + cmpArrays(n, a, b) ? "FAIL VALUE" : "passed"); +} + +void zeroArray(int n, int *a) { + for (int i = 0; i < n; i++) { + a[i] = 0; + } +} + +void onesArray(int n, int *a) { + for (int i = 0; i < n; i++) { + a[i] = 1; + } +} + +void genArray(int n, int *a, int maxval) { + srand(time(nullptr)); + + for (int i = 0; i < n; i++) { + a[i] = rand() % maxval; + } +} + +void printArray(int n, int *a, bool abridged = false) { + printf(" [ "); + for (int i = 0; i < n; i++) { + if (abridged && i + 2 == 15 && n > 16) { + i = n - 2; + printf("... "); + } + printf("%3d ", a[i]); + } + printf("]\n"); +} + +template +void printElapsedTime(T time, std::string note = "") +{ + std::cout << " elapsed time: " << time << "ms " << note << std::endl; +} \ No newline at end of file From e5db042ac35de58e9942b953b9e9dfda92071bd4 Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Thu, 12 Sep 2019 23:50:01 -0400 Subject: [PATCH 02/31] Naive, Efficient and Thrust scans --- Project2-Stream-Compaction/src/main.cpp | 6 +- .../stream_compaction/CMakeLists.txt | 2 +- .../stream_compaction/efficient.cu | 68 ++++++++++++++++++- .../stream_compaction/naive.cu | 68 ++++++++++++++----- .../stream_compaction/thrust.cu | 4 ++ 5 files changed, 125 insertions(+), 23 deletions(-) diff --git a/Project2-Stream-Compaction/src/main.cpp b/Project2-Stream-Compaction/src/main.cpp index fd98f07..8e2cae0 100644 --- a/Project2-Stream-Compaction/src/main.cpp +++ b/Project2-Stream-Compaction/src/main.cpp @@ -51,7 +51,7 @@ int main(int argc, char* argv[]) { printDesc("naive scan, power-of-two"); StreamCompaction::Naive::scan(SIZE, c, a); printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); + printArray(SIZE, c, true); printCmpResult(SIZE, b, c); /* For bug-finding only: Array of 1s to help find bugs in stream compaction or scan @@ -64,14 +64,14 @@ int main(int argc, char* argv[]) { printDesc("naive scan, non-power-of-two"); StreamCompaction::Naive::scan(NPOT, c, a); printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); + printArray(SIZE, c, true); printCmpResult(NPOT, b, c); zeroArray(SIZE, c); printDesc("work-efficient scan, power-of-two"); StreamCompaction::Efficient::scan(SIZE, c, a); printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); + printArray(SIZE, c, true); printCmpResult(SIZE, b, c); zeroArray(SIZE, c); diff --git a/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt b/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt index 272a7e8..4bb0dc2 100644 --- a/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt +++ b/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt @@ -13,5 +13,5 @@ set(SOURCE_FILES cuda_add_library(stream_compaction ${SOURCE_FILES} - OPTIONS -arch=sm_70 + OPTIONS -arch=sm_61 ) diff --git a/Project2-Stream-Compaction/stream_compaction/efficient.cu b/Project2-Stream-Compaction/stream_compaction/efficient.cu index 2db346e..a769e5a 100644 --- a/Project2-Stream-Compaction/stream_compaction/efficient.cu +++ b/Project2-Stream-Compaction/stream_compaction/efficient.cu @@ -1,7 +1,9 @@ #include #include #include "common.h" +#include "device_launch_parameters.h" #include "efficient.h" +#define blockSize 128 namespace StreamCompaction { namespace Efficient { @@ -12,13 +14,76 @@ namespace StreamCompaction { return timer; } + __global__ void kernelUpSweepStep(int n, int d, int* cdata) { + int k = (blockIdx.x * blockDim.x) + threadIdx.x; + if (k > n) + return; + int cur_step_size = 1 << (d + 1); + int prev_step_size = 1 << d; + if (k % cur_step_size == 0) + cdata[k + cur_step_size - 1] += cdata[k + prev_step_size - 1]; + } + + __global__ void kernelDownSweepStep(int n, int d, int* cdata) { + int k = (blockIdx.x * blockDim.x) + threadIdx.x; + if (k > n) + return; + int cur_step = 1 << (d + 1); + int left_step = 1 << d; + + if (k % cur_step == 0) { + int temp = cdata[k + left_step - 1]; + cdata[k + left_step - 1] = cdata[k + cur_step - 1]; + cdata[k + cur_step - 1] += temp; + } + } + void printArray(int n, int *a, bool abridged = false) { + printf(" [ "); + for (int i = 0; i < n; i++) { + if (abridged && i + 2 == 15 && n > 16) { + i = n - 2; + printf("... "); + } + printf("%3d ", a[i]); + } + printf("]\n"); + } + + void printCudaArray(int size, int* cdata) { + int *d_data = new int[size]; + cudaMemcpy(d_data, cdata, size * sizeof(int), cudaMemcpyDeviceToHost); + printArray(size, d_data, true); + } + /** * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ void scan(int n, int *odata, const int *idata) { timer().startGpuTimer(); // TODO - timer().endGpuTimer(); + int power_size = pow(2, ilog2ceil(n)); + int *cdata; + cudaMalloc((void**)&cdata, power_size * sizeof(int)); + checkCUDAErrorFn("cudaMalloc adata failed!"); + + cudaMemset(cdata, 0, power_size * sizeof(int)); + cudaMemcpy(cdata, idata, n * sizeof(int), cudaMemcpyHostToDevice); + dim3 fullBlocksPerGrid((power_size + blockSize - 1) / blockSize); + + //Up Sweep + for (int d = 0; d < ilog2ceil(power_size) ; d++) { + kernelUpSweepStep <<>> (power_size, d, cdata); + } + + //Down Sweep + cudaMemset(cdata + power_size - 1, 0, sizeof(int)); + + for (int d = ilog2(power_size) - 1; d >= 0; d--) { + kernelDownSweepStep << > > (power_size, d, cdata); + } + cudaMemcpy(odata, cdata, sizeof(int) * power_size, cudaMemcpyDeviceToHost); + cudaFree(cdata); + timer().endGpuTimer(); } /** @@ -33,6 +98,7 @@ namespace StreamCompaction { int compact(int n, int *odata, const int *idata) { timer().startGpuTimer(); // TODO + timer().endGpuTimer(); return -1; } diff --git a/Project2-Stream-Compaction/stream_compaction/naive.cu b/Project2-Stream-Compaction/stream_compaction/naive.cu index 4308876..aca1cc9 100644 --- a/Project2-Stream-Compaction/stream_compaction/naive.cu +++ b/Project2-Stream-Compaction/stream_compaction/naive.cu @@ -2,24 +2,56 @@ #include #include "common.h" #include "naive.h" +#include "device_launch_parameters.h" + +#define blockSize 128 namespace StreamCompaction { - namespace Naive { - using StreamCompaction::Common::PerformanceTimer; - PerformanceTimer& timer() - { - static PerformanceTimer timer; - return timer; - } - // TODO: __global__ + namespace Naive { + using StreamCompaction::Common::PerformanceTimer; + PerformanceTimer& timer() + { + static PerformanceTimer timer; + return timer; + } + // TODO: __global__ + __global__ void kernelUpdateStep(int n, int d, int* dest_data, int* src_data) { + int k = (blockIdx.x * blockDim.x) + threadIdx.x; + if (k > n) + return; + int offset = 1 << (d - 1); + if (k >= offset) + dest_data[k] = src_data[k] + src_data[k - offset]; + else + dest_data[k] = src_data[k]; + + } + + /** + * Performs prefix-sum (aka scan) on idata, storing the result into odata. + */ + void scan(int n, int *odata, const int *idata) { + timer().startGpuTimer(); + // TODO + int *adata; + int *bdata; + cudaMalloc((void**)&adata, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc adata failed!"); + cudaMalloc((void**)&bdata, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc bdata failed!"); + cudaMemcpy(adata, idata, sizeof(int) * n, cudaMemcpyHostToDevice); - /** - * Performs prefix-sum (aka scan) on idata, storing the result into odata. - */ - void scan(int n, int *odata, const int *idata) { - timer().startGpuTimer(); - // TODO - timer().endGpuTimer(); - } - } -} + dim3 fullBlocksPerGrid((n + blockSize - 1) / blockSize); + for (int d = 1; d <= ilog2ceil(n); d++) { + kernelUpdateStep <<>> (n, d, bdata, adata); + std::swap(adata, bdata); + //cudaMemcpy(adata, bdata, sizeof(int) * n, cudaMemcpyDeviceToDevice); // Add this comparison + } + cudaMemcpy(odata + 1, adata, sizeof(int) * (n - 1), cudaMemcpyDeviceToHost); + odata[0] = 0; + cudaFree(adata); + cudaFree(bdata); + timer().endGpuTimer(); + } + } +} \ No newline at end of file diff --git a/Project2-Stream-Compaction/stream_compaction/thrust.cu b/Project2-Stream-Compaction/stream_compaction/thrust.cu index 1def45e..e31b1b0 100644 --- a/Project2-Stream-Compaction/stream_compaction/thrust.cu +++ b/Project2-Stream-Compaction/stream_compaction/thrust.cu @@ -22,6 +22,10 @@ namespace StreamCompaction { // TODO use `thrust::exclusive_scan` // example: for device_vectors dv_in and dv_out: // thrust::exclusive_scan(dv_in.begin(), dv_in.end(), dv_out.begin()); + thrust::device_vector dev_idata(idata, idata + n); + thrust::device_vector dev_odata(odata, odata + n); + thrust::exclusive_scan(dev_idata.begin(), dev_idata.end(), dev_odata.begin()); + thrust:copy(dev_odata.begin(), dev_odata.end(), odata); timer().endGpuTimer(); } } From 8cde5f6f02a99b4c85a81a7a43cb4f847f2b8e5b Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Sat, 14 Sep 2019 00:13:57 -0400 Subject: [PATCH 03/31] Stream Compaction --- .../character_recognition/CMakeLists.txt | 2 +- .../character_recognition/mlp.cu | 82 +++++++++- .../character_recognition/mlp.h | 2 +- Project2-Character-Recognition/src/main.cpp | 143 +----------------- Project2-Stream-Compaction/src/main.cpp | 17 ++- .../stream_compaction/common.cu | 11 ++ .../stream_compaction/efficient.cu | 138 +++++++++++++++-- .../stream_compaction/efficient.h | 4 +- .../stream_compaction/naive.cu | 12 +- 9 files changed, 244 insertions(+), 167 deletions(-) diff --git a/Project2-Character-Recognition/character_recognition/CMakeLists.txt b/Project2-Character-Recognition/character_recognition/CMakeLists.txt index 7446175..9e487b9 100644 --- a/Project2-Character-Recognition/character_recognition/CMakeLists.txt +++ b/Project2-Character-Recognition/character_recognition/CMakeLists.txt @@ -7,5 +7,5 @@ set(SOURCE_FILES cuda_add_library(character_recognition ${SOURCE_FILES} - OPTIONS -arch=sm_20 + OPTIONS -arch=sm_60 ) diff --git a/Project2-Character-Recognition/character_recognition/mlp.cu b/Project2-Character-Recognition/character_recognition/mlp.cu index 5a3ed7f..7387737 100644 --- a/Project2-Character-Recognition/character_recognition/mlp.cu +++ b/Project2-Character-Recognition/character_recognition/mlp.cu @@ -1,9 +1,24 @@ #include #include +#include +#include #include "common.h" #include "mlp.h" + +#define INPUT_LAYER_SIZE 4 +#define HIDDEN_LAYER_SIZE 3 +#define OUTPUT_LAYER_SIZE 2 +float weights_IH[INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE]; +float weights_HO[HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE]; + +float* hidden; +float* hidden_sigmoid; +float* output; +float* output_softmax; + namespace CharacterRecognition { + using Common::PerformanceTimer; PerformanceTimer& timer() { @@ -23,5 +38,70 @@ namespace CharacterRecognition { } */ - // TODO: implement required elements for MLP sections 1 and 2 here + + int forwardPass(float* idata, float** weights) { + // Matrix Multiply Input Layer and Weights 1 + // Add Bias + // Apply ReLU + // Matrix Multiply Hidden layer and Weights 2 + // Add Bias + // Apply Softmax + // Max probability + return -1; + } + + void backwardPropagation() { + + } + + float calculateLoss(int* label, int* prediction) { + return -1; + } + + void train(int* idata, int* ilabel) { + // Create Device Buffers for Input and Output + // Create Weight and Bias Buffers + // Forward Pass + // Loss calculation + // Back Propagation + } + + void test(int* idata, int* ilabel, int* olabel) { + + } + + void printArray(int n, float *a, bool abridged = false) { + printf(" [ "); + for (int i = 0; i < n; i++) { + if (abridged && i + 2 == 15 && n > 16) { + i = n - 2; + printf("... "); + } + printf("%3d ", a[i]); + } + printf("]\n"); + } + + void printCudaArray(int size, float* data) { + float *d_data = new float[size]; + cudaMemcpy(d_data, data, size * sizeof(float), cudaMemcpyDeviceToHost); + printArray(size, d_data, true); + } + + void CharacterRecognition::init() { + printf("Here"); + // Create a handle for CUBLAS + cublasHandle_t handle; + cublasCreate(&handle); + //curandGenerator_t prng; + //curandCreateGenerator(&prng, CURAND_RNG_PSEUDO_DEFAULT); + //// Set the seed for the random number generator using the system clock + //curandSetPseudoRandomGeneratorSeed(prng, (unsigned long long) clock()); + // // Fill the array with random numbers on the device + //curandGenerateUniform(prng, weights_IH, INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE); + //curandGenerateUniform(prng, weights_HO, HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE); + + //printCudaArray(INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE, weights_IH); + //printCudaArray(HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE, weights_HO); + } } diff --git a/Project2-Character-Recognition/character_recognition/mlp.h b/Project2-Character-Recognition/character_recognition/mlp.h index 2096228..2840ab0 100644 --- a/Project2-Character-Recognition/character_recognition/mlp.h +++ b/Project2-Character-Recognition/character_recognition/mlp.h @@ -4,6 +4,6 @@ namespace CharacterRecognition { Common::PerformanceTimer& timer(); - + void init(); // TODO: implement required elements for MLP sections 1 and 2 here } diff --git a/Project2-Character-Recognition/src/main.cpp b/Project2-Character-Recognition/src/main.cpp index 11dd534..29cd9f3 100644 --- a/Project2-Character-Recognition/src/main.cpp +++ b/Project2-Character-Recognition/src/main.cpp @@ -11,142 +11,11 @@ #include #include "testing_helpers.hpp" -const int SIZE = 1 << 8; // feel free to change the size of array -const int NPOT = SIZE - 3; // Non-Power-Of-Two -int *a = new int[SIZE]; -int *b = new int[SIZE]; -int *c = new int[SIZE]; - int main(int argc, char* argv[]) { - // Scan tests - - printf("\n"); - printf("****************\n"); - printf("** SCAN TESTS **\n"); - printf("****************\n"); - - genArray(SIZE - 1, a, 50); // Leave a 0 at the end to test that edge case - a[SIZE - 1] = 0; - printArray(SIZE, a, true); - - // initialize b using StreamCompaction::CPU::scan you implement - // We use b for further comparison. Make sure your StreamCompaction::CPU::scan is correct. - // At first all cases passed because b && c are all zeroes. - zeroArray(SIZE, b); - printDesc("cpu scan, power-of-two"); - StreamCompaction::CPU::scan(SIZE, b, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(SIZE, b, true); - - zeroArray(SIZE, c); - printDesc("cpu scan, non-power-of-two"); - StreamCompaction::CPU::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(NPOT, b, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("naive scan, power-of-two"); - StreamCompaction::Naive::scan(SIZE, c, a); - printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - /* For bug-finding only: Array of 1s to help find bugs in stream compaction or scan - onesArray(SIZE, c); - printDesc("1s array for finding bugs"); - StreamCompaction::Naive::scan(SIZE, c, a); - printArray(SIZE, c, true); */ - - zeroArray(SIZE, c); - printDesc("naive scan, non-power-of-two"); - StreamCompaction::Naive::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient scan, power-of-two"); - StreamCompaction::Efficient::scan(SIZE, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient scan, non-power-of-two"); - StreamCompaction::Efficient::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(NPOT, c, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("thrust scan, power-of-two"); - StreamCompaction::Thrust::scan(SIZE, c, a); - printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - zeroArray(SIZE, c); - printDesc("thrust scan, non-power-of-two"); - StreamCompaction::Thrust::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(NPOT, c, true); - printCmpResult(NPOT, b, c); - - printf("\n"); - printf("*****************************\n"); - printf("** STREAM COMPACTION TESTS **\n"); - printf("*****************************\n"); - - // Compaction tests - - genArray(SIZE - 1, a, 4); // Leave a 0 at the end to test that edge case - a[SIZE - 1] = 0; - printArray(SIZE, a, true); - - int count, expectedCount, expectedNPOT; - - // initialize b using StreamCompaction::CPU::compactWithoutScan you implement - // We use b for further comparison. Make sure your StreamCompaction::CPU::compactWithoutScan is correct. - zeroArray(SIZE, b); - printDesc("cpu compact without scan, power-of-two"); - count = StreamCompaction::CPU::compactWithoutScan(SIZE, b, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - expectedCount = count; - printArray(count, b, true); - printCmpLenResult(count, expectedCount, b, b); - - zeroArray(SIZE, c); - printDesc("cpu compact without scan, non-power-of-two"); - count = StreamCompaction::CPU::compactWithoutScan(NPOT, c, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - expectedNPOT = count; - printArray(count, c, true); - printCmpLenResult(count, expectedNPOT, b, c); - - zeroArray(SIZE, c); - printDesc("cpu compact with scan"); - count = StreamCompaction::CPU::compactWithScan(SIZE, c, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(count, c, true); - printCmpLenResult(count, expectedCount, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient compact, power-of-two"); - count = StreamCompaction::Efficient::compact(SIZE, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(count, c, true); - printCmpLenResult(count, expectedCount, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient compact, non-power-of-two"); - count = StreamCompaction::Efficient::compact(NPOT, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(count, c, true); - printCmpLenResult(count, expectedNPOT, b, c); - - system("pause"); // stop Win32 console from closing on exit - delete[] a; - delete[] b; - delete[] c; + // Initialize Arrays + CharacterRecognition::init(); + // Load Dataset + // Train + // Test + } diff --git a/Project2-Stream-Compaction/src/main.cpp b/Project2-Stream-Compaction/src/main.cpp index 8e2cae0..c65cd3a 100644 --- a/Project2-Stream-Compaction/src/main.cpp +++ b/Project2-Stream-Compaction/src/main.cpp @@ -13,7 +13,7 @@ #include #include "testing_helpers.hpp" -const int SIZE = 1 << 3; // feel free to change the size of array +const int SIZE = 1 << 20; // feel free to change the size of array const int NPOT = SIZE - 3; // Non-Power-Of-Two int *a = new int[SIZE]; int *b = new int[SIZE]; @@ -66,10 +66,17 @@ int main(int argc, char* argv[]) { printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); printArray(SIZE, c, true); printCmpResult(NPOT, b, c); + zeroArray(SIZE, c); + printDesc("work-efficient scan, power-of-two"); + StreamCompaction::Efficient::scan(SIZE, c, a); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(SIZE, c, true); + printCmpResult(SIZE, b, c); zeroArray(SIZE, c); - printDesc("work-efficient scan, power-of-two"); - StreamCompaction::Efficient::scan(SIZE, c, a); + + printDesc("Optimized: work-efficient scan, power-of-two"); + StreamCompaction::Efficient::scanEfficient(SIZE, c, a); printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); printArray(SIZE, c, true); printCmpResult(SIZE, b, c); @@ -137,14 +144,14 @@ int main(int argc, char* argv[]) { printDesc("work-efficient compact, power-of-two"); count = StreamCompaction::Efficient::compact(SIZE, c, a); printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(count, c, true); + printArray(count, c, true); printCmpLenResult(count, expectedCount, b, c); zeroArray(SIZE, c); printDesc("work-efficient compact, non-power-of-two"); count = StreamCompaction::Efficient::compact(NPOT, c, a); printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(count, c, true); + printArray(count, c, true); printCmpLenResult(count, expectedNPOT, b, c); system("pause"); // stop Win32 console from closing on exit diff --git a/Project2-Stream-Compaction/stream_compaction/common.cu b/Project2-Stream-Compaction/stream_compaction/common.cu index 2ed6d63..f9dd0c7 100644 --- a/Project2-Stream-Compaction/stream_compaction/common.cu +++ b/Project2-Stream-Compaction/stream_compaction/common.cu @@ -1,4 +1,6 @@ #include "common.h" +#include "device_launch_parameters.h" + void checkCUDAErrorFn(const char *msg, const char *file, int line) { cudaError_t err = cudaGetLastError(); @@ -24,6 +26,10 @@ namespace StreamCompaction { */ __global__ void kernMapToBoolean(int n, int *bools, const int *idata) { // TODO + int idx = (blockIdx.x * blockDim.x) + threadIdx.x; + if (idx > n) + return; + bools[idx] = idata[idx] > 0 ? 1 : 0; } /** @@ -33,6 +39,11 @@ namespace StreamCompaction { __global__ void kernScatter(int n, int *odata, const int *idata, const int *bools, const int *indices) { // TODO + int idx = (blockIdx.x * blockDim.x) + threadIdx.x; + if (idx > n) + return; + if (bools[idx] == 1) + odata[indices[idx]] = idata[idx]; } } diff --git a/Project2-Stream-Compaction/stream_compaction/efficient.cu b/Project2-Stream-Compaction/stream_compaction/efficient.cu index a769e5a..d90a9ba 100644 --- a/Project2-Stream-Compaction/stream_compaction/efficient.cu +++ b/Project2-Stream-Compaction/stream_compaction/efficient.cu @@ -18,18 +18,28 @@ namespace StreamCompaction { int k = (blockIdx.x * blockDim.x) + threadIdx.x; if (k > n) return; - int cur_step_size = 1 << (d + 1); int prev_step_size = 1 << d; + int cur_step_size = 2 * prev_step_size; if (k % cur_step_size == 0) cdata[k + cur_step_size - 1] += cdata[k + prev_step_size - 1]; } + __global__ void kernelUpSweepStepEfficient(int n, int d, int* cdata) { + int k = (blockIdx.x * blockDim.x) + threadIdx.x; + if (k >= n) + return; + int prev_step_size = 1 << d; + int cur_step_size = 2 * prev_step_size; + int new_offset = k * cur_step_size; + cdata[new_offset + cur_step_size - 1] += cdata[new_offset + prev_step_size - 1]; + } + __global__ void kernelDownSweepStep(int n, int d, int* cdata) { int k = (blockIdx.x * blockDim.x) + threadIdx.x; if (k > n) return; - int cur_step = 1 << (d + 1); int left_step = 1 << d; + int cur_step = 2 * left_step; if (k % cur_step == 0) { int temp = cdata[k + left_step - 1]; @@ -37,6 +47,21 @@ namespace StreamCompaction { cdata[k + cur_step - 1] += temp; } } + + __global__ void kernelDownSweepStepEfficient(int n, int d, int* cdata) { + int k = (blockIdx.x * blockDim.x) + threadIdx.x; + if (k >= n) + return; + + int prev_step_size = 1 << d; + int cur_step_size = 2 * prev_step_size; + int new_offset = k * cur_step_size; + + int temp = cdata[new_offset + prev_step_size - 1]; + cdata[new_offset + prev_step_size - 1] = cdata[new_offset + cur_step_size - 1]; + cdata[new_offset + cur_step_size - 1] += temp; + } + void printArray(int n, int *a, bool abridged = false) { printf(" [ "); for (int i = 0; i < n; i++) { @@ -49,30 +74,80 @@ namespace StreamCompaction { printf("]\n"); } - void printCudaArray(int size, int* cdata) { + void printCudaArray(int size, int* data) { int *d_data = new int[size]; - cudaMemcpy(d_data, cdata, size * sizeof(int), cudaMemcpyDeviceToHost); + cudaMemcpy(d_data, data, size * sizeof(int), cudaMemcpyDeviceToHost); printArray(size, d_data, true); } /** * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ - void scan(int n, int *odata, const int *idata) { - timer().startGpuTimer(); - // TODO + void scanEfficient(int n, int *odata, const int *idata) { + // Memory Allocation and Copying int power_size = pow(2, ilog2ceil(n)); int *cdata; cudaMalloc((void**)&cdata, power_size * sizeof(int)); checkCUDAErrorFn("cudaMalloc adata failed!"); + cudaMemset(cdata, 0, power_size * sizeof(int)); + cudaMemcpy(cdata, idata, n * sizeof(int), cudaMemcpyHostToDevice); + + bool started_timer = true; + try { + timer().startGpuTimer(); + } + catch (const std::exception& e) { + started_timer = false; + } + + int numThreads; + //Up Sweep + for (int d = 0; d <= ilog2ceil(power_size) - 1 ; d++) { + numThreads = pow(2, (ilog2ceil(power_size) - 1 - d)); + dim3 fullBlocks((numThreads + blockSize - 1) / blockSize); + kernelUpSweepStepEfficient <<>> (numThreads, d, cdata); + } + + //Down Sweep + cudaMemset(cdata + power_size - 1, 0, sizeof(int)); + for (int d = ilog2(power_size) - 1; d >= 0; d--) { + numThreads = pow(2, (ilog2ceil(power_size) - 1 - d)); + dim3 fullBlocks((numThreads + blockSize - 1) / blockSize); + kernelDownSweepStepEfficient <<>> (numThreads, d, cdata); + } + + if (started_timer) + timer().endGpuTimer(); + + // Copy Back and Free Memory + cudaMemcpy(odata, cdata, sizeof(int) * power_size, cudaMemcpyDeviceToHost); + cudaFree(cdata); + } + /** + * Performs prefix-sum (aka scan) on idata, storing the result into odata. + */ + void scan(int n, int *odata, const int *idata) { + // Memory Allocation and Copying + int power_size = pow(2, ilog2ceil(n)); + int *cdata; + cudaMalloc((void**)&cdata, power_size * sizeof(int)); + checkCUDAErrorFn("cudaMalloc adata failed!"); cudaMemset(cdata, 0, power_size * sizeof(int)); cudaMemcpy(cdata, idata, n * sizeof(int), cudaMemcpyHostToDevice); + + bool started_timer = true; + try { + timer().startGpuTimer(); + } + catch (const std::exception& e) { + started_timer = false; + } dim3 fullBlocksPerGrid((power_size + blockSize - 1) / blockSize); //Up Sweep - for (int d = 0; d < ilog2ceil(power_size) ; d++) { - kernelUpSweepStep <<>> (power_size, d, cdata); + for (int d = 0; d < ilog2ceil(power_size); d++) { + kernelUpSweepStep << > > (power_size, d, cdata); } //Down Sweep @@ -81,10 +156,13 @@ namespace StreamCompaction { for (int d = ilog2(power_size) - 1; d >= 0; d--) { kernelDownSweepStep << > > (power_size, d, cdata); } + if (started_timer) + timer().endGpuTimer(); + + // Copy Back and Free Memory cudaMemcpy(odata, cdata, sizeof(int) * power_size, cudaMemcpyDeviceToHost); cudaFree(cdata); - timer().endGpuTimer(); - } + } /** * Performs stream compaction on idata, storing the result into odata. @@ -96,11 +174,39 @@ namespace StreamCompaction { * @returns The number of elements remaining after compaction. */ int compact(int n, int *odata, const int *idata) { - timer().startGpuTimer(); - // TODO - - timer().endGpuTimer(); - return -1; + // Memory Allocation and Copying + int *bools = new int[n]; + int *indices = new int[n]; + int *dev_bools; + int *dev_indices; + int *dev_idata; + int *dev_odata; + cudaMalloc((void**)&dev_bools, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc dev_bools failed!"); + cudaMalloc((void**)&dev_indices, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc dev_indices failed!"); + cudaMalloc((void**)&dev_idata, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc dev_idata failed!"); + cudaMalloc((void**)&dev_odata, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc dev_odata failed!"); + cudaMemcpy(dev_idata, idata, sizeof(int) * n, cudaMemcpyHostToDevice); + + timer().startGpuTimer(); + dim3 fullBlocksPerGrid((n + blockSize - 1) / blockSize); + StreamCompaction::Common::kernMapToBoolean << > > (n, dev_bools, dev_idata); + cudaMemcpy(bools, dev_bools, sizeof(int) * n, cudaMemcpyDeviceToHost); + scan(n, indices, bools); + cudaMemcpy(dev_indices, indices, sizeof(int) * n, cudaMemcpyHostToDevice); + StreamCompaction::Common::kernScatter << > > (n, dev_odata, dev_idata, dev_bools, dev_indices); + timer().endGpuTimer(); + + // Copy Back and Free Memory + cudaMemcpy(odata, dev_odata, sizeof(int) * n, cudaMemcpyDeviceToHost); + cudaFree(dev_bools); + cudaFree(dev_indices); + cudaFree(dev_idata); + cudaFree(dev_odata); + return indices[n-1] + bools[n-1]; } } } diff --git a/Project2-Stream-Compaction/stream_compaction/efficient.h b/Project2-Stream-Compaction/stream_compaction/efficient.h index 803cb4f..2aaa472 100644 --- a/Project2-Stream-Compaction/stream_compaction/efficient.h +++ b/Project2-Stream-Compaction/stream_compaction/efficient.h @@ -6,7 +6,9 @@ namespace StreamCompaction { namespace Efficient { StreamCompaction::Common::PerformanceTimer& timer(); - void scan(int n, int *odata, const int *idata); + void scanEfficient(int n, int *odata, const int *idata); + + void scan(int n, int *odata, const int *idata); int compact(int n, int *odata, const int *idata); } diff --git a/Project2-Stream-Compaction/stream_compaction/naive.cu b/Project2-Stream-Compaction/stream_compaction/naive.cu index aca1cc9..e0bcf2f 100644 --- a/Project2-Stream-Compaction/stream_compaction/naive.cu +++ b/Project2-Stream-Compaction/stream_compaction/naive.cu @@ -31,8 +31,7 @@ namespace StreamCompaction { * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ void scan(int n, int *odata, const int *idata) { - timer().startGpuTimer(); - // TODO + // Memory Allocation and Copying int *adata; int *bdata; cudaMalloc((void**)&adata, n * sizeof(int)); @@ -41,17 +40,20 @@ namespace StreamCompaction { checkCUDAErrorFn("cudaMalloc bdata failed!"); cudaMemcpy(adata, idata, sizeof(int) * n, cudaMemcpyHostToDevice); + timer().startGpuTimer(); + // TODO dim3 fullBlocksPerGrid((n + blockSize - 1) / blockSize); for (int d = 1; d <= ilog2ceil(n); d++) { kernelUpdateStep <<>> (n, d, bdata, adata); std::swap(adata, bdata); - //cudaMemcpy(adata, bdata, sizeof(int) * n, cudaMemcpyDeviceToDevice); // Add this comparison } - cudaMemcpy(odata + 1, adata, sizeof(int) * (n - 1), cudaMemcpyDeviceToHost); + timer().endGpuTimer(); + + // Memory De-allocation and copying odata[0] = 0; + cudaMemcpy(odata + 1, adata, sizeof(int) * (n - 1), cudaMemcpyDeviceToHost); cudaFree(adata); cudaFree(bdata); - timer().endGpuTimer(); } } } \ No newline at end of file From 4090c2955d1cb1663edad112c7bdcb20b6f1e967 Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Sun, 15 Sep 2019 00:17:57 -0400 Subject: [PATCH 04/31] NN Implementation --- Project2-Character-Recognition/CMakeLists.txt | 3 + .../character_recognition/common.cu | 131 ++++++++- .../character_recognition/common.h | 207 +++++++------- .../character_recognition/mlp.cu | 260 +++++++++++++----- .../character_recognition/mlp.h | 11 +- Project2-Character-Recognition/src/main.cpp | 15 +- 6 files changed, 448 insertions(+), 179 deletions(-) diff --git a/Project2-Character-Recognition/CMakeLists.txt b/Project2-Character-Recognition/CMakeLists.txt index 09e9198..d1a3746 100644 --- a/Project2-Character-Recognition/CMakeLists.txt +++ b/Project2-Character-Recognition/CMakeLists.txt @@ -22,6 +22,7 @@ if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin") endif() include_directories(.) +link_directories(${CUDA_TOOLKIT_ROOT_DIR}/lib/x64) add_subdirectory(character_recognition) cuda_add_executable(${CMAKE_PROJECT_NAME} @@ -32,4 +33,6 @@ cuda_add_executable(${CMAKE_PROJECT_NAME} target_link_libraries(${CMAKE_PROJECT_NAME} character_recognition ${CORELIBS} + cublas + curand ) diff --git a/Project2-Character-Recognition/character_recognition/common.cu b/Project2-Character-Recognition/character_recognition/common.cu index 2a754d4..94be4db 100644 --- a/Project2-Character-Recognition/character_recognition/common.cu +++ b/Project2-Character-Recognition/character_recognition/common.cu @@ -1,15 +1,124 @@ #include "common.h" +#include "device_launch_parameters.h" void checkCUDAErrorFn(const char *msg, const char *file, int line) { - cudaError_t err = cudaGetLastError(); - if (cudaSuccess == err) { - return; - } - - fprintf(stderr, "CUDA error"); - if (file) { - fprintf(stderr, " (%s:%d)", file, line); - } - fprintf(stderr, ": %s: %s\n", msg, cudaGetErrorString(err)); - exit(EXIT_FAILURE); + cudaError_t err = cudaGetLastError(); + if (cudaSuccess == err) { + return; + } + + fprintf(stderr, "CUDA error"); + if (file) { + fprintf(stderr, " (%s:%d)", file, line); + } + fprintf(stderr, ": %s: %s\n", msg, cudaGetErrorString(err)); + exit(EXIT_FAILURE); +} + +void printArray(int n, float *a, bool abridged = false) { + printf(" [ "); + for (int i = 0; i < n; i++) { + if (abridged && i + 2 == 15 && n > 16) { + i = n - 2; + printf("... "); + } + printf("%f ", a[i]); + } + printf("]\n"); +} + +void printCudaArray(int size, float* data) { + float *d_data = new float[size]; + cudaMemcpy(d_data, data, size * sizeof(float), cudaMemcpyDeviceToHost); + printArray(size, d_data, true); +} + +void printCuda2DArray(int height, int width, float* data) { + float *d_data = new float[width*height]; + cudaMemcpy(d_data, data, width*height * sizeof(float), cudaMemcpyDeviceToHost); + for (int i = 0; i < height; i++) + printArray(width, d_data + i * width, true); +} + +__device__ float sigmoid(float x) { + return 1.0f / (1 + exp(-x)); +} + +__global__ void Functions::sigmoidActivation(float* input, float* output, int x_dim, int y_dim) { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + output[index] = sigmoid(input[index]); + } +} + +__global__ void Functions::reluActivation(float* input, float* output, int x_dim, int y_dim) { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + output[index] = input[index] > 0 ? input[index] : 0; + } +} + +__global__ void Functions::ExponentialActivation(float* input, float* output, int x_dim, int y_dim) +{ + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + output[index] = exp(input[index]); + } +} + +__global__ void Functions::Divide(float* input, float* constant, int x_dim, int y_dim) +{ + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + if(constant > 0) + input[index] /= *constant; + } +} + +__global__ void Functions::Multiply(float* input, float* constant, int x_dim, int y_dim) +{ + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + input[index] *= *constant; + } +} + +__global__ void Functions::Subtract(float * input, float * constant, int x_dim, int y_dim) +{ + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + input[index] -= *constant; + } +} + +__global__ void Functions::ElementwiseMultiplication(float * input1, float * input2, float* output, int x_dim, int y_dim) +{ + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + output[index] = input1[index] * input2[index]; + } +} + +__global__ void Functions::ElementwiseSubtraction(float * input1, float * input2, float* output, int x_dim, int y_dim) +{ + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + output[index] = input1[index] - input2[index]; + } +} + +__global__ void Functions::KernelElementwiseMultiplySigmoid(float * input_output, float * input, int x_dim, int y_dim) +{ + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + input_output[index] *= input[index] * (1 - input[index]); + } +} + +__global__ void Functions::KernelElementwiseMultiplyRelu(float * input_output, float * input, int x_dim, int y_dim) +{ + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + input_output[index] = input[index] > 0 ? input_output[index] : 0; + } } diff --git a/Project2-Character-Recognition/character_recognition/common.h b/Project2-Character-Recognition/character_recognition/common.h index 6aede64..d852003 100644 --- a/Project2-Character-Recognition/character_recognition/common.h +++ b/Project2-Character-Recognition/character_recognition/common.h @@ -19,108 +19,119 @@ void checkCUDAErrorFn(const char *msg, const char *file = NULL, int line = -1); inline int ilog2(int x) { - int lg = 0; - while (x >>= 1) { - ++lg; - } - return lg; + int lg = 0; + while (x >>= 1) { + ++lg; + } + return lg; } inline int ilog2ceil(int x) { - return x == 1 ? 0 : ilog2(x - 1) + 1; + return x == 1 ? 0 : ilog2(x - 1) + 1; +} +namespace Functions { + __global__ void sigmoidActivation(float * input, float * output, int x_dim, int y_dim); + __global__ void reluActivation(float * input, float * output, int x_dim, int y_dim); + __global__ void ExponentialActivation(float * input, float * output, int x_dim, int y_dim); + __global__ void Divide(float* input, float* constant, int x_dim, int y_dim); + __global__ void Multiply(float* input, float* constant, int x_dim, int y_dim); + __global__ void Subtract(float* input, float* constant, int x_dim, int y_dim); + __global__ void ElementwiseMultiplication(float* input1, float* input2, float* output, int x_dim, int y_dim); + __global__ void ElementwiseSubtraction(float* input1, float* input2, float* output, int x_dim, int y_dim); + __global__ void KernelElementwiseMultiplySigmoid(float* input_output, float* input, int x_dim, int y_dim); + __global__ void KernelElementwiseMultiplyRelu(float* input_output, float* input, int x_dim, int y_dim); } - namespace Common { - /** - * This class is used for timing the performance - * Uncopyable and unmovable - * - * Adapted from WindyDarian(https://github.com/WindyDarian) - */ - class PerformanceTimer - { - public: - PerformanceTimer() - { - cudaEventCreate(&event_start); - cudaEventCreate(&event_end); - } - - ~PerformanceTimer() - { - cudaEventDestroy(event_start); - cudaEventDestroy(event_end); - } - - void startCpuTimer() - { - if (cpu_timer_started) { throw std::runtime_error("CPU timer already started"); } - cpu_timer_started = true; - - time_start_cpu = std::chrono::high_resolution_clock::now(); - } - - void endCpuTimer() - { - time_end_cpu = std::chrono::high_resolution_clock::now(); - - if (!cpu_timer_started) { throw std::runtime_error("CPU timer not started"); } - - std::chrono::duration duro = time_end_cpu - time_start_cpu; - prev_elapsed_time_cpu_milliseconds = - static_cast(duro.count()); - - cpu_timer_started = false; - } - - void startGpuTimer() - { - if (gpu_timer_started) { throw std::runtime_error("GPU timer already started"); } - gpu_timer_started = true; - - cudaEventRecord(event_start); - } - - void endGpuTimer() - { - cudaEventRecord(event_end); - cudaEventSynchronize(event_end); - - if (!gpu_timer_started) { throw std::runtime_error("GPU timer not started"); } - - cudaEventElapsedTime(&prev_elapsed_time_gpu_milliseconds, event_start, event_end); - gpu_timer_started = false; - } - - float getCpuElapsedTimeForPreviousOperation() //noexcept //(damn I need VS 2015 - { - return prev_elapsed_time_cpu_milliseconds; - } - - float getGpuElapsedTimeForPreviousOperation() //noexcept - { - return prev_elapsed_time_gpu_milliseconds; - } - - // remove copy and move functions - PerformanceTimer(const PerformanceTimer&) = delete; - PerformanceTimer(PerformanceTimer&&) = delete; - PerformanceTimer& operator=(const PerformanceTimer&) = delete; - PerformanceTimer& operator=(PerformanceTimer&&) = delete; - - private: - cudaEvent_t event_start = nullptr; - cudaEvent_t event_end = nullptr; - - using time_point_t = std::chrono::high_resolution_clock::time_point; - time_point_t time_start_cpu; - time_point_t time_end_cpu; - - bool cpu_timer_started = false; - bool gpu_timer_started = false; - - float prev_elapsed_time_cpu_milliseconds = 0.f; - float prev_elapsed_time_gpu_milliseconds = 0.f; - }; + /** + * This class is used for timing the performance + * Uncopyable and unmovable + * + * Adapted from WindyDarian(https://github.com/WindyDarian) + */ + class PerformanceTimer + { + public: + PerformanceTimer() + { + cudaEventCreate(&event_start); + cudaEventCreate(&event_end); + } + + ~PerformanceTimer() + { + cudaEventDestroy(event_start); + cudaEventDestroy(event_end); + } + + void startCpuTimer() + { + if (cpu_timer_started) { throw std::runtime_error("CPU timer already started"); } + cpu_timer_started = true; + + time_start_cpu = std::chrono::high_resolution_clock::now(); + } + + void endCpuTimer() + { + time_end_cpu = std::chrono::high_resolution_clock::now(); + + if (!cpu_timer_started) { throw std::runtime_error("CPU timer not started"); } + + std::chrono::duration duro = time_end_cpu - time_start_cpu; + prev_elapsed_time_cpu_milliseconds = + static_cast(duro.count()); + + cpu_timer_started = false; + } + + void startGpuTimer() + { + if (gpu_timer_started) { throw std::runtime_error("GPU timer already started"); } + gpu_timer_started = true; + + cudaEventRecord(event_start); + } + + void endGpuTimer() + { + cudaEventRecord(event_end); + cudaEventSynchronize(event_end); + + if (!gpu_timer_started) { throw std::runtime_error("GPU timer not started"); } + + cudaEventElapsedTime(&prev_elapsed_time_gpu_milliseconds, event_start, event_end); + gpu_timer_started = false; + } + + float getCpuElapsedTimeForPreviousOperation() //noexcept //(damn I need VS 2015 + { + return prev_elapsed_time_cpu_milliseconds; + } + + float getGpuElapsedTimeForPreviousOperation() //noexcept + { + return prev_elapsed_time_gpu_milliseconds; + } + + // remove copy and move functions + PerformanceTimer(const PerformanceTimer&) = delete; + PerformanceTimer(PerformanceTimer&&) = delete; + PerformanceTimer& operator=(const PerformanceTimer&) = delete; + PerformanceTimer& operator=(PerformanceTimer&&) = delete; + + private: + cudaEvent_t event_start = nullptr; + cudaEvent_t event_end = nullptr; + + using time_point_t = std::chrono::high_resolution_clock::time_point; + time_point_t time_start_cpu; + time_point_t time_end_cpu; + + bool cpu_timer_started = false; + bool gpu_timer_started = false; + + float prev_elapsed_time_cpu_milliseconds = 0.f; + float prev_elapsed_time_gpu_milliseconds = 0.f; + }; } diff --git a/Project2-Character-Recognition/character_recognition/mlp.cu b/Project2-Character-Recognition/character_recognition/mlp.cu index 7387737..cc27204 100644 --- a/Project2-Character-Recognition/character_recognition/mlp.cu +++ b/Project2-Character-Recognition/character_recognition/mlp.cu @@ -4,18 +4,65 @@ #include #include "common.h" #include "mlp.h" +#include "device_launch_parameters.h" +#include +#include + +#define blockSize 128 + +int INPUT_LAYER_SIZE; +int HIDDEN_LAYER_SIZE; +int OUTPUT_LAYER_SIZE; +float *weights_IH, *weights_HO, *g_weights_IH, *g_weights_HO, *hidden, *h_sigmoid, *output, *o_softmax; +cublasHandle_t cublas_handle; +curandGenerator_t prng; + +void print_matrix(const float *devA, int nr_rows_A, int nr_cols_A) { + float *A = new float[nr_rows_A*nr_cols_A]; + cudaMemcpy(A, devA, nr_rows_A*nr_cols_A * sizeof(float), cudaMemcpyDeviceToHost); + for (int i = 0; i < nr_rows_A; ++i) { + for (int j = 0; j < nr_cols_A; ++j) { + printf("%f \t", A[j * nr_rows_A + i]); + } + printf("\n"); + } + printf("\n"); +} - -#define INPUT_LAYER_SIZE 4 -#define HIDDEN_LAYER_SIZE 3 -#define OUTPUT_LAYER_SIZE 2 -float weights_IH[INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE]; -float weights_HO[HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE]; - -float* hidden; -float* hidden_sigmoid; -float* output; -float* output_softmax; +namespace StreamCompaction { + __global__ void kernelUpSweepStepEfficient(int n, int d, float* cdata) { + int k = (blockIdx.x * blockDim.x) + threadIdx.x; + if (k >= n) + return; + int prev_step_size = 1 << d; + int cur_step_size = 2 * prev_step_size; + int new_offset = k * cur_step_size; + cdata[new_offset + cur_step_size - 1] += cdata[new_offset + prev_step_size - 1]; + } + /** + * Performs prefix-sum (aka scan) on idata, storing the result into odata. + */ + void sumArray(int n, float* sum, const float *idata) { + // Memory Allocation and Copying + int power_size = pow(2, ilog2ceil(n)); + float *sumArray; + cudaMalloc((void**)&sumArray, power_size * sizeof(float)); + checkCUDAErrorFn("cudaMalloc sumArray failed!"); + cudaMemset(sumArray, 0, power_size * sizeof(float)); + cudaMemcpy(sumArray, idata, n * sizeof(float), cudaMemcpyDeviceToDevice); + + int numThreads; + //Up Sweep + for (int d = 0; d <= ilog2ceil(power_size) - 1; d++) { + numThreads = pow(2, (ilog2ceil(power_size) - 1 - d)); + dim3 fullBlocks((numThreads + blockSize - 1) / blockSize); + kernelUpSweepStepEfficient << > > (numThreads, d, sumArray); + } + // Copy Back and Free Memory + cudaMemcpy(sum, sumArray + power_size - 1, sizeof(float), cudaMemcpyDeviceToDevice); + cudaFree(sumArray); + } +} namespace CharacterRecognition { @@ -26,82 +73,163 @@ namespace CharacterRecognition { return timer; } - // TODO: __global__ - - /** - * Example of use case (follow how you did it in stream compaction) - */ - /*void scan(int n, int *odata, const int *idata) { - timer().startGpuTimer(); - // TODO - timer().endGpuTimer(); - } - */ - + // Reference: https://solarianprogrammer.com/2012/05/31/matrix-multiplication-cuda-cublas-curand-thrust/ + // Matrix Multiplication + // nr_rows_A, nr_cols_A, nr_cols_B + void gpu_blas_mmul(const float *A, const float *B, float *C, const int nr_rows_A, const int nr_cols_A, const int nr_cols_B) { + int lda = nr_rows_A, ldb = nr_cols_A, ldc = nr_rows_A; + const float alf = 1; + const float bet = 0; + const float *alpha = &alf; + const float *beta = &bet; + cublasSgemm(cublas_handle, CUBLAS_OP_N, CUBLAS_OP_N, nr_rows_A, nr_cols_B, nr_cols_A, alpha, A, lda, B, ldb, beta, C, ldc); + } - int forwardPass(float* idata, float** weights) { + /* Forward Pass for one instance + 1. Multiply input with input and hidden layer weights + 2. Apply Sigmoid + 3. Multiply hidden layer activation with hidden and output layer weights + 4. Apply Softmax and calculate ouput + */ + // TODO: Can Incorporate Bias + void forwardPass(float* idata) { // Matrix Multiply Input Layer and Weights 1 - // Add Bias - // Apply ReLU + gpu_blas_mmul(idata, weights_IH, hidden, 1, INPUT_LAYER_SIZE, HIDDEN_LAYER_SIZE); + //printf("Hidden After: "); + //print_matrix(hidden, 1, HIDDEN_LAYER_SIZE); + + // Apply Sigmoid + dim3 hiddenLayerBlocks((HIDDEN_LAYER_SIZE + blockSize - 1) / blockSize); + Functions::reluActivation<<>>(hidden, h_sigmoid, 1, HIDDEN_LAYER_SIZE); + //printf("Hidden Sigmoid After: "); + //print_matrix(h_sigmoid, 1, HIDDEN_LAYER_SIZE); + // Matrix Multiply Hidden layer and Weights 2 - // Add Bias + gpu_blas_mmul(h_sigmoid, weights_HO, output, 1, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); + //printf("Output After: "); + //print_matrix(output, 1, OUTPUT_LAYER_SIZE); + // Apply Softmax - // Max probability - return -1; + dim3 outputLayerBlocks((OUTPUT_LAYER_SIZE + blockSize - 1) / blockSize); + Functions::ExponentialActivation <<>> (output, o_softmax, 1, OUTPUT_LAYER_SIZE); + float *sum; + cudaMalloc((void**)&sum, sizeof(float)); + StreamCompaction::sumArray(OUTPUT_LAYER_SIZE, sum, o_softmax); + Functions::Divide << > > (o_softmax, sum, 1, OUTPUT_LAYER_SIZE); + printf("Output Probabilities: "); + print_matrix(o_softmax, 1, OUTPUT_LAYER_SIZE); } - void backwardPropagation() { - + void backwardPropagation(float* dev_input, float* dev_output, float* learning_rate) { + // Memory Allocation + float *temp_hidden, *temp_output; + cudaMalloc((void**)&temp_output, OUTPUT_LAYER_SIZE * sizeof(float)); + cudaMalloc((void**)&temp_hidden, HIDDEN_LAYER_SIZE * sizeof(float)); + + // Gradient of Loss w.r.t Weight2 + dim3 outputLayerBlocks((OUTPUT_LAYER_SIZE + blockSize - 1) / blockSize); + Functions::ElementwiseSubtraction << > > (o_softmax, dev_output, temp_output, 1, OUTPUT_LAYER_SIZE); + gpu_blas_mmul(h_sigmoid, temp_output, g_weights_HO, HIDDEN_LAYER_SIZE, 1, OUTPUT_LAYER_SIZE); + + // Gradient of Loss w.r.t Weight1 + gpu_blas_mmul(weights_HO, temp_output, temp_hidden, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE, 1); + dim3 hiddenLayerBlocks((HIDDEN_LAYER_SIZE + blockSize - 1) / blockSize); + Functions::KernelElementwiseMultiplyRelu << > > (temp_hidden, h_sigmoid, 1, HIDDEN_LAYER_SIZE); + gpu_blas_mmul(dev_input, temp_hidden, g_weights_IH, INPUT_LAYER_SIZE, 1, HIDDEN_LAYER_SIZE); + + // Gradient Updates + dim3 IHBlocks(((INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE) + blockSize - 1) / blockSize); + Functions::Multiply << > > (g_weights_IH, learning_rate, INPUT_LAYER_SIZE, HIDDEN_LAYER_SIZE); + Functions::ElementwiseSubtraction << > > (weights_IH, g_weights_IH, weights_IH, INPUT_LAYER_SIZE, HIDDEN_LAYER_SIZE); + dim3 HOBlocks(((HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE) + blockSize - 1) / blockSize); + Functions::Multiply << > > (g_weights_HO, learning_rate, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); + Functions::ElementwiseSubtraction << > > (weights_HO, g_weights_HO, weights_HO, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); + + // Free Memory + cudaFree(temp_hidden); + cudaFree(temp_output); } float calculateLoss(int* label, int* prediction) { return -1; } - void train(int* idata, int* ilabel) { + void train(float* idata, float* ilabel, int num_instances, int epochs, float learning_rate) { // Create Device Buffers for Input and Output - // Create Weight and Bias Buffers - // Forward Pass - // Loss calculation - // Back Propagation + float *dev_input, *dev_output, *dev_lr; + cudaMalloc((void**)&dev_input, num_instances * INPUT_LAYER_SIZE * sizeof(float)); + checkCUDAErrorFn("cudaMalloc dev_input failed!"); + cudaMemcpy(dev_input, idata, num_instances * INPUT_LAYER_SIZE * sizeof(float), cudaMemcpyHostToDevice); + + cudaMalloc((void**)&dev_output, num_instances * OUTPUT_LAYER_SIZE * sizeof(float)); + checkCUDAErrorFn("cudaMalloc dev_output failed!"); + cudaMemcpy(dev_output, ilabel, num_instances * OUTPUT_LAYER_SIZE * sizeof(float), cudaMemcpyHostToDevice); + + cudaMalloc((void**)&dev_lr, sizeof(float)); + thrust::device_ptr dev_ptr(dev_lr); + thrust::fill(dev_ptr, dev_ptr + 1, learning_rate); + + // Train + for (int e = 0; e < epochs; e++) { + for (int i = 0; i < num_instances; i++) { + printf("Input: "); + print_matrix(dev_input + (i * INPUT_LAYER_SIZE), 1, INPUT_LAYER_SIZE); + // Forward Pass + forwardPass(dev_input + (i * INPUT_LAYER_SIZE)); + // Back Propagation + backwardPropagation(dev_input + (i * INPUT_LAYER_SIZE), dev_output + (i * OUTPUT_LAYER_SIZE), dev_lr); + } + } } void test(int* idata, int* ilabel, int* olabel) { } - void printArray(int n, float *a, bool abridged = false) { - printf(" [ "); - for (int i = 0; i < n; i++) { - if (abridged && i + 2 == 15 && n > 16) { - i = n - 2; - printf("... "); - } - printf("%3d ", a[i]); - } - printf("]\n"); - } + void init(int input_size, int hidden_size, int output_size) { + printf("Init\n"); + // Initialize Layer Sizes + INPUT_LAYER_SIZE = input_size; + HIDDEN_LAYER_SIZE = hidden_size; + OUTPUT_LAYER_SIZE = output_size; + + // Memory Allocation for Weight Matrices, Gradient Matrics and Hidden Layers + cudaMalloc((void**)&weights_IH, INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE * sizeof(float)); + checkCUDAErrorFn("cudaMalloc weights_IH failed!"); + cudaMalloc((void**)&weights_HO, HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE * sizeof(float)); + checkCUDAErrorFn("cudaMalloc weights_HO failed!"); + cudaMalloc((void**)&g_weights_IH, INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE * sizeof(float)); + checkCUDAErrorFn("cudaMalloc g_weights_IH failed!"); + cudaMalloc((void**)&g_weights_HO, HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE * sizeof(float)); + checkCUDAErrorFn("cudaMalloc g_weights_HO failed!"); + cudaMalloc((void**)&hidden, HIDDEN_LAYER_SIZE * sizeof(float)); + checkCUDAErrorFn("cudaMalloc hidden failed!"); + cudaMalloc((void**)&h_sigmoid, HIDDEN_LAYER_SIZE * sizeof(float)); + checkCUDAErrorFn("cudaMalloc h_sigmoid failed!"); + cudaMalloc((void**)&output, OUTPUT_LAYER_SIZE * sizeof(float)); + checkCUDAErrorFn("cudaMalloc output failed!"); + cudaMalloc((void**)&o_softmax, OUTPUT_LAYER_SIZE * sizeof(float)); + checkCUDAErrorFn("cudaMalloc o_softmax failed!"); - void printCudaArray(int size, float* data) { - float *d_data = new float[size]; - cudaMemcpy(d_data, data, size * sizeof(float), cudaMemcpyDeviceToHost); - printArray(size, d_data, true); + // Create a handle for CUBLAS + cublasCreate(&cublas_handle); + // Curand Random Number Generator + curandCreateGenerator(&prng, CURAND_RNG_PSEUDO_DEFAULT); + // Seed for Random Number Generator + curandSetPseudoRandomGeneratorSeed(prng, (unsigned long long) clock()); + + // Initialize weight matrices with random numbers + curandGenerateUniform(prng, weights_IH, INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE); + curandGenerateUniform(prng, weights_HO, HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE); + + // Debug/Print + print_matrix(weights_IH, INPUT_LAYER_SIZE, HIDDEN_LAYER_SIZE); + print_matrix(weights_HO, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); } - void CharacterRecognition::init() { - printf("Here"); - // Create a handle for CUBLAS - cublasHandle_t handle; - cublasCreate(&handle); - //curandGenerator_t prng; - //curandCreateGenerator(&prng, CURAND_RNG_PSEUDO_DEFAULT); - //// Set the seed for the random number generator using the system clock - //curandSetPseudoRandomGeneratorSeed(prng, (unsigned long long) clock()); - // // Fill the array with random numbers on the device - //curandGenerateUniform(prng, weights_IH, INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE); - //curandGenerateUniform(prng, weights_HO, HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE); - - //printCudaArray(INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE, weights_IH); - //printCudaArray(HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE, weights_HO); + void free() { + cudaFree(weights_IH); + cudaFree(weights_HO); + cublasDestroy(cublas_handle); } } diff --git a/Project2-Character-Recognition/character_recognition/mlp.h b/Project2-Character-Recognition/character_recognition/mlp.h index 2840ab0..e94d8f5 100644 --- a/Project2-Character-Recognition/character_recognition/mlp.h +++ b/Project2-Character-Recognition/character_recognition/mlp.h @@ -4,6 +4,13 @@ namespace CharacterRecognition { Common::PerformanceTimer& timer(); - void init(); - // TODO: implement required elements for MLP sections 1 and 2 here + // Initializes + // 1. CUBLAS Handle + // 2. Weight Matrices + void init(int input_size, int hidden_size, int output_size); + + void train(float* idata, float* ilabel, int num_instances, int epochs, float learning_rate); + + // Frees memory and destroys CUBLAS handle + void free(); } diff --git a/Project2-Character-Recognition/src/main.cpp b/Project2-Character-Recognition/src/main.cpp index 29cd9f3..9e1777c 100644 --- a/Project2-Character-Recognition/src/main.cpp +++ b/Project2-Character-Recognition/src/main.cpp @@ -12,10 +12,21 @@ #include "testing_helpers.hpp" int main(int argc, char* argv[]) { - // Initialize Arrays - CharacterRecognition::init(); + // Initialize Network + CharacterRecognition::init(2, 4, 2); + // Load Dataset + int num_instances = 4; + float idata[8] = { 0, 0, 1, 0, 0, 1, 1, 1}; + //float ilabel[4] = { 0, 1, 1, 0 }; + float ilabel[8] = { 1, 0, 0, 1, 0, 1, 1, 0}; + + //Parameters + int epochs = 1500; + float learning_rate = 0.1; + // Train + CharacterRecognition::train(idata, ilabel, num_instances, epochs, learning_rate); // Test } From 8d9da6e9c812238cbb8eb58b056376b1f6da369c Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Sun, 15 Sep 2019 18:55:16 -0400 Subject: [PATCH 05/31] NN: bug fixes, cleanup, refactoring --- .../character_recognition/CMakeLists.txt | 2 + .../character_recognition/common.cu | 83 -------------- .../character_recognition/common.h | 12 --- .../character_recognition/functions.cu | 102 ++++++++++++++++++ .../character_recognition/functions.h | 34 ++++++ .../character_recognition/mlp.cu | 82 +++++++++----- Project2-Character-Recognition/src/main.cpp | 102 +++++++++++++++--- 7 files changed, 286 insertions(+), 131 deletions(-) create mode 100644 Project2-Character-Recognition/character_recognition/functions.cu create mode 100644 Project2-Character-Recognition/character_recognition/functions.h diff --git a/Project2-Character-Recognition/character_recognition/CMakeLists.txt b/Project2-Character-Recognition/character_recognition/CMakeLists.txt index 9e487b9..38b041b 100644 --- a/Project2-Character-Recognition/character_recognition/CMakeLists.txt +++ b/Project2-Character-Recognition/character_recognition/CMakeLists.txt @@ -1,6 +1,8 @@ set(SOURCE_FILES "common.h" "common.cu" + "functions.cu" + "functions.h" "mlp.h" "mlp.cu" ) diff --git a/Project2-Character-Recognition/character_recognition/common.cu b/Project2-Character-Recognition/character_recognition/common.cu index 94be4db..77f1a33 100644 --- a/Project2-Character-Recognition/character_recognition/common.cu +++ b/Project2-Character-Recognition/character_recognition/common.cu @@ -39,86 +39,3 @@ void printCuda2DArray(int height, int width, float* data) { for (int i = 0; i < height; i++) printArray(width, d_data + i * width, true); } - -__device__ float sigmoid(float x) { - return 1.0f / (1 + exp(-x)); -} - -__global__ void Functions::sigmoidActivation(float* input, float* output, int x_dim, int y_dim) { - int index = blockIdx.x * blockDim.x + threadIdx.x; - if (index < x_dim * y_dim) { - output[index] = sigmoid(input[index]); - } -} - -__global__ void Functions::reluActivation(float* input, float* output, int x_dim, int y_dim) { - int index = blockIdx.x * blockDim.x + threadIdx.x; - if (index < x_dim * y_dim) { - output[index] = input[index] > 0 ? input[index] : 0; - } -} - -__global__ void Functions::ExponentialActivation(float* input, float* output, int x_dim, int y_dim) -{ - int index = blockIdx.x * blockDim.x + threadIdx.x; - if (index < x_dim * y_dim) { - output[index] = exp(input[index]); - } -} - -__global__ void Functions::Divide(float* input, float* constant, int x_dim, int y_dim) -{ - int index = blockIdx.x * blockDim.x + threadIdx.x; - if (index < x_dim * y_dim) { - if(constant > 0) - input[index] /= *constant; - } -} - -__global__ void Functions::Multiply(float* input, float* constant, int x_dim, int y_dim) -{ - int index = blockIdx.x * blockDim.x + threadIdx.x; - if (index < x_dim * y_dim) { - input[index] *= *constant; - } -} - -__global__ void Functions::Subtract(float * input, float * constant, int x_dim, int y_dim) -{ - int index = blockIdx.x * blockDim.x + threadIdx.x; - if (index < x_dim * y_dim) { - input[index] -= *constant; - } -} - -__global__ void Functions::ElementwiseMultiplication(float * input1, float * input2, float* output, int x_dim, int y_dim) -{ - int index = blockIdx.x * blockDim.x + threadIdx.x; - if (index < x_dim * y_dim) { - output[index] = input1[index] * input2[index]; - } -} - -__global__ void Functions::ElementwiseSubtraction(float * input1, float * input2, float* output, int x_dim, int y_dim) -{ - int index = blockIdx.x * blockDim.x + threadIdx.x; - if (index < x_dim * y_dim) { - output[index] = input1[index] - input2[index]; - } -} - -__global__ void Functions::KernelElementwiseMultiplySigmoid(float * input_output, float * input, int x_dim, int y_dim) -{ - int index = blockIdx.x * blockDim.x + threadIdx.x; - if (index < x_dim * y_dim) { - input_output[index] *= input[index] * (1 - input[index]); - } -} - -__global__ void Functions::KernelElementwiseMultiplyRelu(float * input_output, float * input, int x_dim, int y_dim) -{ - int index = blockIdx.x * blockDim.x + threadIdx.x; - if (index < x_dim * y_dim) { - input_output[index] = input[index] > 0 ? input_output[index] : 0; - } -} diff --git a/Project2-Character-Recognition/character_recognition/common.h b/Project2-Character-Recognition/character_recognition/common.h index d852003..951ec06 100644 --- a/Project2-Character-Recognition/character_recognition/common.h +++ b/Project2-Character-Recognition/character_recognition/common.h @@ -29,18 +29,6 @@ inline int ilog2(int x) { inline int ilog2ceil(int x) { return x == 1 ? 0 : ilog2(x - 1) + 1; } -namespace Functions { - __global__ void sigmoidActivation(float * input, float * output, int x_dim, int y_dim); - __global__ void reluActivation(float * input, float * output, int x_dim, int y_dim); - __global__ void ExponentialActivation(float * input, float * output, int x_dim, int y_dim); - __global__ void Divide(float* input, float* constant, int x_dim, int y_dim); - __global__ void Multiply(float* input, float* constant, int x_dim, int y_dim); - __global__ void Subtract(float* input, float* constant, int x_dim, int y_dim); - __global__ void ElementwiseMultiplication(float* input1, float* input2, float* output, int x_dim, int y_dim); - __global__ void ElementwiseSubtraction(float* input1, float* input2, float* output, int x_dim, int y_dim); - __global__ void KernelElementwiseMultiplySigmoid(float* input_output, float* input, int x_dim, int y_dim); - __global__ void KernelElementwiseMultiplyRelu(float* input_output, float* input, int x_dim, int y_dim); -} namespace Common { /** diff --git a/Project2-Character-Recognition/character_recognition/functions.cu b/Project2-Character-Recognition/character_recognition/functions.cu new file mode 100644 index 0000000..40d8831 --- /dev/null +++ b/Project2-Character-Recognition/character_recognition/functions.cu @@ -0,0 +1,102 @@ +#include "functions.h" +#include "device_launch_parameters.h" + +__device__ float sigmoid(float x) { + return 1.0f / (1 + exp(-x)); +} +namespace Functions { + __global__ void sigmoidActivation(float* input, float* output, int x_dim, int y_dim) { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + output[index] = sigmoid(input[index]); + } + } + + __global__ void reluActivation(float* input, float* output, int x_dim, int y_dim) { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + output[index] = input[index] > 0 ? input[index] : 0; + } + } + + __global__ void ExponentialActivation(float* input, float* output, int x_dim, int y_dim) + { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + output[index] = exp(input[index]); + } + } + + __global__ void CrossEntropyLoss(float * target, float * predicted, float * output, int x_dim, int y_dim) + { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + output[index] = -1 * (target[index] * logf(predicted[index])); + } + } + + __global__ void Add(float * input, float * constant, int x_dim, int y_dim) + { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + input[index] += *constant; + } + } + + __global__ void Divide(float* input, float* constant, int x_dim, int y_dim) + { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + if (constant > 0) + input[index] /= *constant; + } + } + + __global__ void Multiply(float* input, float* constant, int x_dim, int y_dim) + { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + input[index] *= *constant; + } + } + + __global__ void Subtract(float * input, float * constant, int x_dim, int y_dim) + { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + input[index] -= *constant; + } + } + + __global__ void ElementwiseMultiplication(float * input1, float * input2, float* output, int x_dim, int y_dim) + { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + output[index] = input1[index] * input2[index]; + } + } + + __global__ void ElementwiseSubtraction(float * input1, float * input2, float* output, int x_dim, int y_dim) + { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + output[index] = input1[index] - input2[index]; + } + } + + __global__ void KernelElementwiseMultiplySigmoid(float * input_output, float * input, int x_dim, int y_dim) + { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + input_output[index] *= input[index] * (1 - input[index]); + } + } + + __global__ void KernelElementwiseMultiplyRelu(float * input_output, float * input, int x_dim, int y_dim) + { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + input_output[index] = input[index] > 0 ? input_output[index] : 0; + } + } +} \ No newline at end of file diff --git a/Project2-Character-Recognition/character_recognition/functions.h b/Project2-Character-Recognition/character_recognition/functions.h new file mode 100644 index 0000000..e10ef72 --- /dev/null +++ b/Project2-Character-Recognition/character_recognition/functions.h @@ -0,0 +1,34 @@ +#pragma once + +#include +#include + +#include +#include +#include +#include +#include + +#define FILENAME (strrchr(__FILE__, '/') ? strrchr(__FILE__, '/') + 1 : __FILE__) + +namespace Functions { + // Non Linear Operations + __global__ void sigmoidActivation(float * input, float * output, int x_dim, int y_dim); + __global__ void reluActivation(float * input, float * output, int x_dim, int y_dim); + __global__ void ExponentialActivation(float * input, float * output, int x_dim, int y_dim); + __global__ void CrossEntropyLoss(float * target, float * predicted, float* output, int x_dim, int y_dim); + + // Operations between an array and a constant + __global__ void Add(float* input, float* constant, int x_dim, int y_dim); + __global__ void Divide(float* input, float* constant, int x_dim, int y_dim); + __global__ void Multiply(float* input, float* constant, int x_dim, int y_dim); + __global__ void Subtract(float* input, float* constant, int x_dim, int y_dim); + + // Element wise Operations between two arrays + __global__ void ElementwiseMultiplication(float* input1, float* input2, float* output, int x_dim, int y_dim); + __global__ void ElementwiseSubtraction(float* input1, float* input2, float* output, int x_dim, int y_dim); + + // Custom Kernel for gradient descent + __global__ void KernelElementwiseMultiplySigmoid(float* input_output, float* input, int x_dim, int y_dim); + __global__ void KernelElementwiseMultiplyRelu(float* input_output, float* input, int x_dim, int y_dim); +} diff --git a/Project2-Character-Recognition/character_recognition/mlp.cu b/Project2-Character-Recognition/character_recognition/mlp.cu index cc27204..38d3588 100644 --- a/Project2-Character-Recognition/character_recognition/mlp.cu +++ b/Project2-Character-Recognition/character_recognition/mlp.cu @@ -3,6 +3,7 @@ #include #include #include "common.h" +#include "functions.h" #include "mlp.h" #include "device_launch_parameters.h" #include @@ -13,9 +14,9 @@ int INPUT_LAYER_SIZE; int HIDDEN_LAYER_SIZE; int OUTPUT_LAYER_SIZE; + float *weights_IH, *weights_HO, *g_weights_IH, *g_weights_HO, *hidden, *h_sigmoid, *output, *o_softmax; cublasHandle_t cublas_handle; -curandGenerator_t prng; void print_matrix(const float *devA, int nr_rows_A, int nr_cols_A) { float *A = new float[nr_rows_A*nr_cols_A]; @@ -92,22 +93,16 @@ namespace CharacterRecognition { 4. Apply Softmax and calculate ouput */ // TODO: Can Incorporate Bias - void forwardPass(float* idata) { + void forwardPass(float* dev_input) { // Matrix Multiply Input Layer and Weights 1 - gpu_blas_mmul(idata, weights_IH, hidden, 1, INPUT_LAYER_SIZE, HIDDEN_LAYER_SIZE); - //printf("Hidden After: "); - //print_matrix(hidden, 1, HIDDEN_LAYER_SIZE); + gpu_blas_mmul(dev_input, weights_IH, hidden, 1, INPUT_LAYER_SIZE, HIDDEN_LAYER_SIZE); // Apply Sigmoid dim3 hiddenLayerBlocks((HIDDEN_LAYER_SIZE + blockSize - 1) / blockSize); - Functions::reluActivation<<>>(hidden, h_sigmoid, 1, HIDDEN_LAYER_SIZE); - //printf("Hidden Sigmoid After: "); - //print_matrix(h_sigmoid, 1, HIDDEN_LAYER_SIZE); + Functions::sigmoidActivation<<>>(hidden, h_sigmoid, 1, HIDDEN_LAYER_SIZE); // Matrix Multiply Hidden layer and Weights 2 gpu_blas_mmul(h_sigmoid, weights_HO, output, 1, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); - //printf("Output After: "); - //print_matrix(output, 1, OUTPUT_LAYER_SIZE); // Apply Softmax dim3 outputLayerBlocks((OUTPUT_LAYER_SIZE + blockSize - 1) / blockSize); @@ -116,8 +111,7 @@ namespace CharacterRecognition { cudaMalloc((void**)&sum, sizeof(float)); StreamCompaction::sumArray(OUTPUT_LAYER_SIZE, sum, o_softmax); Functions::Divide << > > (o_softmax, sum, 1, OUTPUT_LAYER_SIZE); - printf("Output Probabilities: "); - print_matrix(o_softmax, 1, OUTPUT_LAYER_SIZE); + cudaFree(sum); } void backwardPropagation(float* dev_input, float* dev_output, float* learning_rate) { @@ -134,7 +128,7 @@ namespace CharacterRecognition { // Gradient of Loss w.r.t Weight1 gpu_blas_mmul(weights_HO, temp_output, temp_hidden, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE, 1); dim3 hiddenLayerBlocks((HIDDEN_LAYER_SIZE + blockSize - 1) / blockSize); - Functions::KernelElementwiseMultiplyRelu << > > (temp_hidden, h_sigmoid, 1, HIDDEN_LAYER_SIZE); + Functions::KernelElementwiseMultiplySigmoid << > > (temp_hidden, h_sigmoid, 1, HIDDEN_LAYER_SIZE); gpu_blas_mmul(dev_input, temp_hidden, g_weights_IH, INPUT_LAYER_SIZE, 1, HIDDEN_LAYER_SIZE); // Gradient Updates @@ -148,38 +142,70 @@ namespace CharacterRecognition { // Free Memory cudaFree(temp_hidden); cudaFree(temp_output); + //print_matrix(weights_HO, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); } - float calculateLoss(int* label, int* prediction) { - return -1; + void calculateLoss(float* dev_output, float* loss) { + // Memory Allocation + float *temp_loss, *sum; + cudaMalloc((void**)&temp_loss, OUTPUT_LAYER_SIZE * sizeof(float)); + cudaMalloc((void**)&sum, sizeof(float)); + + dim3 outputLayerBlocks((OUTPUT_LAYER_SIZE + blockSize - 1) / blockSize); + Functions::CrossEntropyLoss << > > (dev_output, o_softmax, temp_loss, 1, OUTPUT_LAYER_SIZE); + StreamCompaction::sumArray(OUTPUT_LAYER_SIZE, sum, temp_loss); + Functions::Add << <1, 1 >> > (loss, sum, 1, 1); + + cudaFree(temp_loss); + cudaFree(sum); } void train(float* idata, float* ilabel, int num_instances, int epochs, float learning_rate) { - // Create Device Buffers for Input and Output float *dev_input, *dev_output, *dev_lr; + // Device Buffer for Input cudaMalloc((void**)&dev_input, num_instances * INPUT_LAYER_SIZE * sizeof(float)); checkCUDAErrorFn("cudaMalloc dev_input failed!"); cudaMemcpy(dev_input, idata, num_instances * INPUT_LAYER_SIZE * sizeof(float), cudaMemcpyHostToDevice); - + + // Device Buffer for Output cudaMalloc((void**)&dev_output, num_instances * OUTPUT_LAYER_SIZE * sizeof(float)); checkCUDAErrorFn("cudaMalloc dev_output failed!"); cudaMemcpy(dev_output, ilabel, num_instances * OUTPUT_LAYER_SIZE * sizeof(float), cudaMemcpyHostToDevice); + // Device Buffer for Learning Rate cudaMalloc((void**)&dev_lr, sizeof(float)); thrust::device_ptr dev_ptr(dev_lr); thrust::fill(dev_ptr, dev_ptr + 1, learning_rate); // Train + float *dev_loss; + cudaMalloc((void**)&dev_loss, epochs * sizeof(float)); + cudaMemset(dev_loss, 0, epochs * sizeof(float)); + float *loss = new float[1]; for (int e = 0; e < epochs; e++) { for (int i = 0; i < num_instances; i++) { - printf("Input: "); - print_matrix(dev_input + (i * INPUT_LAYER_SIZE), 1, INPUT_LAYER_SIZE); + //if (e % 100 == 0) { + // printf("Epoch %d Input: ", e); + // print_matrix(dev_input + (i * INPUT_LAYER_SIZE), 1, INPUT_LAYER_SIZE); + //} // Forward Pass forwardPass(dev_input + (i * INPUT_LAYER_SIZE)); + // Back Propagation backwardPropagation(dev_input + (i * INPUT_LAYER_SIZE), dev_output + (i * OUTPUT_LAYER_SIZE), dev_lr); + + // Loss Calculation + calculateLoss(dev_output + (i * OUTPUT_LAYER_SIZE), dev_loss + e); + //if (e % 100 == 0) { + // printf("output probabilities: "); + // print_matrix(o_softmax, 1, OUTPUT_LAYER_SIZE); + //} } + + printf("Epoch: %d, Loss:", e); + print_matrix(dev_loss+e, 1, 1); } + //print_matrix(dev_loss, epochs, 1); } void test(int* idata, int* ilabel, int* olabel) { @@ -213,23 +239,31 @@ namespace CharacterRecognition { // Create a handle for CUBLAS cublasCreate(&cublas_handle); - // Curand Random Number Generator + + // Curand Random Number Generator and Seed + curandGenerator_t prng; curandCreateGenerator(&prng, CURAND_RNG_PSEUDO_DEFAULT); - // Seed for Random Number Generator - curandSetPseudoRandomGeneratorSeed(prng, (unsigned long long) clock()); + curandSetPseudoRandomGeneratorSeed(prng, 7); // Initialize weight matrices with random numbers curandGenerateUniform(prng, weights_IH, INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE); curandGenerateUniform(prng, weights_HO, HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE); // Debug/Print - print_matrix(weights_IH, INPUT_LAYER_SIZE, HIDDEN_LAYER_SIZE); - print_matrix(weights_HO, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); + //printf("Initial Weight Matrices\n"); + //print_matrix(weights_IH, INPUT_LAYER_SIZE, HIDDEN_LAYER_SIZE); + //print_matrix(weights_HO, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); } void free() { cudaFree(weights_IH); cudaFree(weights_HO); + cudaFree(g_weights_IH); + cudaFree(g_weights_HO); + cudaFree(hidden); + cudaFree(h_sigmoid); + cudaFree(output); + cudaFree(o_softmax); cublasDestroy(cublas_handle); } } diff --git a/Project2-Character-Recognition/src/main.cpp b/Project2-Character-Recognition/src/main.cpp index 9e1777c..ca50552 100644 --- a/Project2-Character-Recognition/src/main.cpp +++ b/Project2-Character-Recognition/src/main.cpp @@ -1,32 +1,110 @@ /** * @file main.cpp - * @brief Stream compaction test program - * @authors Kai Ninomiya - * @date 2015 + * @brief Character Recognition + * @authors Disha Jindal + * @date 2019 * @copyright University of Pennsylvania */ - +#include #include #include #include #include "testing_helpers.hpp" +#include +#include +#include +#include +#include +#include + +using namespace std; + +void loadCharacterDataset(int num_instances, int input_layer, int output_layer, float* idata, float* ilabel) { + for (int i = 0; i < num_instances * output_layer; i++) + ilabel[i] = 0; + + for (int i = 1; i <= num_instances; i++) { + ifstream infile; + std::string file_name; + std::stringstream ss; + ss << setw(2) << std::setfill('0') << i; + file_name = "C:\\Users\\djjindal\\Project2-Number-Algorithms\\Project2-Character-Recognition\\data-set\\" + ss.str() + "info.txt"; + infile.open(file_name); + int line_num = 0; + string line; + while (getline(infile, line)){ + if (line_num == 0) { + ilabel[output_layer*(i - 1) + stoi(line) - 1] = 1; + } + if (line_num == 2) { + stringstream ssin(line); + int k = 0; + while (ssin.good() && k < input_layer) { + ssin >> idata[output_layer*(i - 1) + k]; + ++k; + } + } + line_num++; + } + infile.close(); + } +} + +void loadXORDataset(float* idata, float* ilabel) { + idata[0] = 0; + idata[1] = 0; + idata[2] = 1; + idata[3] = 0; + idata[4] = 0; + idata[5] = 1; + idata[6] = 1; + idata[7] = 1; + + ilabel[0] = 1; + ilabel[1] = 0; + ilabel[2] = 0; + ilabel[3] = 1; + ilabel[4] = 0; + ilabel[5] = 1; + ilabel[6] = 1; + ilabel[7] = 0; +} int main(int argc, char* argv[]) { + // Model Architecture and Num Instances + //int input_layer = 10201; + //int hidden_layer = 4; + //int output_layer = 52; + //int num_instances = 52; + + int input_layer = 2; + int hidden_layer = 4; + int output_layer = 2; + int num_instances = 4; + // Initialize Network - CharacterRecognition::init(2, 4, 2); + CharacterRecognition::init(input_layer, hidden_layer, output_layer); // Load Dataset - int num_instances = 4; - float idata[8] = { 0, 0, 1, 0, 0, 1, 1, 1}; - //float ilabel[4] = { 0, 1, 1, 0 }; - float ilabel[8] = { 1, 0, 0, 1, 0, 1, 1, 0}; + //float *idata = new float[num_instances * 10201]; + //float *ilabel = new float[num_instances * output_layer]; + //loadCharacterDataset(num_instances, input_layer, output_layer, idata, ilabel); - //Parameters - int epochs = 1500; - float learning_rate = 0.1; + float *idata = new float[8]; + float *ilabel = new float[8]; + loadXORDataset(idata, ilabel); + + // Parameters + int epochs = 2000; + float learning_rate = 0.5; // Train CharacterRecognition::train(idata, ilabel, num_instances, epochs, learning_rate); + + // Test + + // Free Resources + CharacterRecognition::free(); } From 32ec5ed816268b2fc79e7cb702618ae2d5b29814 Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Mon, 16 Sep 2019 17:23:38 -0400 Subject: [PATCH 06/31] Stream Compact Metrics Collection and main script cleanup --- Project2-Stream-Compaction/src/main.cpp | 323 ++++++++++-------- .../stream_compaction/cpu.cu | 5 +- .../stream_compaction/efficient.cu | 18 +- .../stream_compaction/efficient.h | 6 +- .../stream_compaction/naive.cu | 4 +- .../stream_compaction/naive.h | 2 +- 6 files changed, 198 insertions(+), 160 deletions(-) diff --git a/Project2-Stream-Compaction/src/main.cpp b/Project2-Stream-Compaction/src/main.cpp index c65cd3a..e5c5d56 100644 --- a/Project2-Stream-Compaction/src/main.cpp +++ b/Project2-Stream-Compaction/src/main.cpp @@ -13,149 +13,188 @@ #include #include "testing_helpers.hpp" -const int SIZE = 1 << 20; // feel free to change the size of array -const int NPOT = SIZE - 3; // Non-Power-Of-Two -int *a = new int[SIZE]; -int *b = new int[SIZE]; -int *c = new int[SIZE]; +void scanTests(const int SIZE, const int NPOT, int *a, int *b , int *c, int blockSize) { + genArray(SIZE - 1, a, 50); // Leave a 0 at the end to test that edge case + a[SIZE - 1] = 0; + + //zeroArray(SIZE, b); + //StreamCompaction::CPU::scan(SIZE, b, a); + //std::cout << StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation() << " "; + + zeroArray(SIZE, c); + StreamCompaction::Naive::scan(SIZE, c, a, blockSize); + std::cout << StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation() << " "; + + zeroArray(SIZE, c); + StreamCompaction::Efficient::scan(SIZE, c, a, blockSize); + std::cout << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << " "; + + zeroArray(SIZE, c); + StreamCompaction::Efficient::scanEfficient(SIZE, c, a, blockSize); + std::cout << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << " "; + + //zeroArray(SIZE, c); + //StreamCompaction::Thrust::scan(SIZE, c, a); + //std::cout << StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation() << " "; + + //zeroArray(SIZE, c); + //StreamCompaction::CPU::scan(NPOT, c, a); + //std::cout << StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation() << " "; + + zeroArray(SIZE, c); + StreamCompaction::Naive::scan(NPOT, c, a, blockSize); + std::cout << StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation() << " "; + + zeroArray(SIZE, c); + StreamCompaction::Efficient::scan(NPOT, c, a, blockSize); + std::cout << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << " "; -int main(int argc, char* argv[]) { - // Scan tests - - printf("\n"); - printf("****************\n"); - printf("** SCAN TESTS **\n"); - printf("****************\n"); - - genArray(SIZE - 1, a, 50); // Leave a 0 at the end to test that edge case - a[SIZE - 1] = 0; - printArray(SIZE, a, true); - - // initialize b using StreamCompaction::CPU::scan you implement - // We use b for further comparison. Make sure your StreamCompaction::CPU::scan is correct. - // At first all cases passed because b && c are all zeroes. - zeroArray(SIZE, b); - printDesc("cpu scan, power-of-two"); - StreamCompaction::CPU::scan(SIZE, b, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(SIZE, b, true); - - zeroArray(SIZE, c); - printDesc("cpu scan, non-power-of-two"); - StreamCompaction::CPU::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(NPOT, b, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("naive scan, power-of-two"); - StreamCompaction::Naive::scan(SIZE, c, a); - printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - /* For bug-finding only: Array of 1s to help find bugs in stream compaction or scan - onesArray(SIZE, c); - printDesc("1s array for finding bugs"); - StreamCompaction::Naive::scan(SIZE, c, a); - printArray(SIZE, c, true); */ - - zeroArray(SIZE, c); - printDesc("naive scan, non-power-of-two"); - StreamCompaction::Naive::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - printArray(SIZE, c, true); - printCmpResult(NPOT, b, c); zeroArray(SIZE, c); + StreamCompaction::Efficient::scanEfficient(NPOT, c, a, blockSize); + std::cout << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << std::endl; + + //zeroArray(SIZE, c); + //StreamCompaction::Thrust::scan(NPOT, c, a); + //std::cout << StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation() << std::endl; +} + +void streamCompactionTests(const int SIZE, const int NPOT, int *a, int *b, int *c, int blockSize) { + genArray(SIZE - 1, a, 4); // Leave a 0 at the end to test that edge case + a[SIZE - 1] = 0; + + int count, expectedCount, expectedNPOT; - printDesc("work-efficient scan, power-of-two"); - StreamCompaction::Efficient::scan(SIZE, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - zeroArray(SIZE, c); - - printDesc("Optimized: work-efficient scan, power-of-two"); - StreamCompaction::Efficient::scanEfficient(SIZE, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient scan, non-power-of-two"); - StreamCompaction::Efficient::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(NPOT, c, true); - printCmpResult(NPOT, b, c); - - zeroArray(SIZE, c); - printDesc("thrust scan, power-of-two"); - StreamCompaction::Thrust::scan(SIZE, c, a); - printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(SIZE, c, true); - printCmpResult(SIZE, b, c); - - zeroArray(SIZE, c); - printDesc("thrust scan, non-power-of-two"); - StreamCompaction::Thrust::scan(NPOT, c, a); - printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - //printArray(NPOT, c, true); - printCmpResult(NPOT, b, c); - - printf("\n"); - printf("*****************************\n"); - printf("** STREAM COMPACTION TESTS **\n"); - printf("*****************************\n"); - - // Compaction tests - - genArray(SIZE - 1, a, 4); // Leave a 0 at the end to test that edge case - a[SIZE - 1] = 0; - printArray(SIZE, a, true); - - int count, expectedCount, expectedNPOT; - - // initialize b using StreamCompaction::CPU::compactWithoutScan you implement - // We use b for further comparison. Make sure your StreamCompaction::CPU::compactWithoutScan is correct. - zeroArray(SIZE, b); - printDesc("cpu compact without scan, power-of-two"); - count = StreamCompaction::CPU::compactWithoutScan(SIZE, b, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - expectedCount = count; - printArray(count, b, true); - printCmpLenResult(count, expectedCount, b, b); - - zeroArray(SIZE, c); - printDesc("cpu compact without scan, non-power-of-two"); - count = StreamCompaction::CPU::compactWithoutScan(NPOT, c, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - expectedNPOT = count; - printArray(count, c, true); - printCmpLenResult(count, expectedNPOT, b, c); - - zeroArray(SIZE, c); - printDesc("cpu compact with scan"); - count = StreamCompaction::CPU::compactWithScan(SIZE, c, a); - printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); - printArray(count, c, true); - printCmpLenResult(count, expectedCount, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient compact, power-of-two"); - count = StreamCompaction::Efficient::compact(SIZE, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - printArray(count, c, true); - printCmpLenResult(count, expectedCount, b, c); - - zeroArray(SIZE, c); - printDesc("work-efficient compact, non-power-of-two"); - count = StreamCompaction::Efficient::compact(NPOT, c, a); - printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); - printArray(count, c, true); - printCmpLenResult(count, expectedNPOT, b, c); - - system("pause"); // stop Win32 console from closing on exit - delete[] a; - delete[] b; - delete[] c; + //zeroArray(SIZE, b); + //count = StreamCompaction::CPU::compactWithoutScan(SIZE, b, a); + //std::cout << StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation() << " "; + //expectedCount = count; + ////printCmpLenResult(count, expectedCount, b, b); + + //zeroArray(SIZE, c); + //count = StreamCompaction::CPU::compactWithScan(SIZE, c, a); + //std::cout << StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation() << " "; + ////printCmpLenResult(count, expectedCount, b, c); + + zeroArray(SIZE, c); + count = StreamCompaction::Efficient::compact(SIZE, c, a, false, blockSize); + std::cout << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << " "; + //printCmpLenResult(count, expectedCount, b, c); + + zeroArray(SIZE, c); + count = StreamCompaction::Efficient::compact(SIZE, c, a, true, blockSize); + std::cout << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << " "; + //printCmpLenResult(count, expectedCount, b, c); + + //zeroArray(SIZE, c); + //count = StreamCompaction::CPU::compactWithoutScan(NPOT, c, a); + //std::cout << StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation() << " "; + //expectedNPOT = count; + ////printCmpLenResult(count, expectedNPOT, b, c); + + //zeroArray(SIZE, c); + //count = StreamCompaction::CPU::compactWithScan(NPOT, c, a); + //std::cout << StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation() << " "; + ////printCmpLenResult(count, expectedCount, b, c); + + zeroArray(SIZE, c); + count = StreamCompaction::Efficient::compact(NPOT, c, a, false, blockSize); + std::cout << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << " "; + //printCmpLenResult(count, expectedNPOT, b, c); + + zeroArray(SIZE, c); + count = StreamCompaction::Efficient::compact(NPOT, c, a, true, blockSize); + std::cout << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << std::endl; + //printCmpLenResult(count, expectedNPOT, b, c); +} + +void metricsForDifferentN() { + printf("\n"); + printf("****************\n"); + printf("** SCAN TESTS **\n"); + printf("****************\n"); + std::cout << "PowerOfTwo " << "N " + << "CPU:POT " << "GPU:Naive:POT " << "GPU:WorkEfficient:POT " << "GPU:Optimized:POT " << "GPU:Thrust:POT " + << "CPU:NPOT " << "GPU:Naive:NPOT " << "GPU:WorkEfficient:NPOT " << "GPU:Optimized:NPOT " << "GPU:Thrust:NPOT" << std::endl; + + for (int s = 3; s <= 30; s++) { + const int SIZE = 1 << s; // feel free to change the size of array + const int NPOT = SIZE - 3; // Non-Power-Of-Two + int *a = new int[SIZE]; + int *b = new int[SIZE]; + int *c = new int[SIZE]; + std::cout << s << " " << SIZE << " "; + scanTests(SIZE, NPOT, a, b, c, 128); + delete[] a; + delete[] b; + delete[] c; + } + + printf("\n"); + printf("*****************************\n"); + printf("** STREAM COMPACTION TESTS **\n"); + printf("*****************************\n"); + std::cout << "PowerOfTwo " << "N " + << "CPU:WithoutScan:POT " << "CPU:WithScan:POT " << "GPU:WorkEfficient:POT " << "GPU:Optimized:POT " + << "CPU:NPOT " << "CPU:WithScan:NPOT " << "GPU:WorkEfficient:NPOT " << "GPU:Optimized:NPOT " << std::endl; + for (int s = 3; s <= 30; s++) { + const int SIZE = 1 << s; // feel free to change the size of array + const int NPOT = SIZE - 3; // Non-Power-Of-Two + int *a = new int[SIZE]; + int *b = new int[SIZE]; + int *c = new int[SIZE]; + std::cout << s << " " << SIZE << " "; + streamCompactionTests(SIZE, NPOT, a, b, c, 128); + delete[] a; + delete[] b; + delete[] c; + } +} + +void metricsForDifferentBlockSize() { + printf("\n"); + printf("****************\n"); + printf("** SCAN TESTS **\n"); + printf("****************\n"); + std::cout << "BlockSize " + << "GPU:Naive:POT " << "GPU:WorkEfficient:POT " << "GPU:Optimized:POT " + << "GPU:Naive:NPOT " << "GPU:WorkEfficient:NPOT " << "GPU:Optimized:NPOT "<< std::endl; + + int blockSizes[11] = {1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024}; + for (int i = 0; i < 11; i++) { + const int SIZE = 1 << 20; // feel free to change the size of array + const int NPOT = SIZE - 3; // Non-Power-Of-Two + int *a = new int[SIZE]; + int *b = new int[SIZE]; + int *c = new int[SIZE]; + std::cout << blockSizes[i] << " "; + scanTests(SIZE, NPOT, a, b, c, blockSizes[i]); + delete[] a; + delete[] b; + delete[] c; + } + + printf("\n"); + printf("*****************************\n"); + printf("** STREAM COMPACTION TESTS **\n"); + printf("*****************************\n"); + std::cout << "BlockSize " + << "GPU:WorkEfficient:POT " << "GPU:Optimized:POT " + << "GPU:WorkEfficient:NPOT " << "GPU:Optimized:NPOT " << std::endl; + for (int i = 0; i < 11; i++) { + const int SIZE = 1 << 20; // feel free to change the size of array + const int NPOT = SIZE - 3; // Non-Power-Of-Two + int *a = new int[SIZE]; + int *b = new int[SIZE]; + int *c = new int[SIZE]; + std::cout << blockSizes[i] << " "; + streamCompactionTests(SIZE, NPOT, a, b, c, blockSizes[i]); + delete[] a; + delete[] b; + delete[] c; + } +} +int main(int argc, char* argv[]) { + //metricsForDifferentN(); + metricsForDifferentBlockSize(); + system("pause"); // stop Win32 console from closing on exit } diff --git a/Project2-Stream-Compaction/stream_compaction/cpu.cu b/Project2-Stream-Compaction/stream_compaction/cpu.cu index 6d591fa..aac9f08 100644 --- a/Project2-Stream-Compaction/stream_compaction/cpu.cu +++ b/Project2-Stream-Compaction/stream_compaction/cpu.cu @@ -68,7 +68,6 @@ namespace StreamCompaction { */ int compactWithScan(int n, int *odata, const int *idata) { timer().startCpuTimer(); - // TODO int *binary_idata = new int[n]; int *final_index = new int[n]; for (int i = 0; i < n; i++) @@ -77,8 +76,8 @@ namespace StreamCompaction { for (int i = 0; i < n; i++) if (binary_idata[i] == 1) odata[final_index[i]] = idata[i]; - timer().endCpuTimer(); - return final_index[n-1] + (idata[n-1] > 0); + timer().endCpuTimer(); + return final_index[n - 1] + (idata[n - 1] > 0); } } diff --git a/Project2-Stream-Compaction/stream_compaction/efficient.cu b/Project2-Stream-Compaction/stream_compaction/efficient.cu index d90a9ba..1e1da0e 100644 --- a/Project2-Stream-Compaction/stream_compaction/efficient.cu +++ b/Project2-Stream-Compaction/stream_compaction/efficient.cu @@ -3,7 +3,6 @@ #include "common.h" #include "device_launch_parameters.h" #include "efficient.h" -#define blockSize 128 namespace StreamCompaction { namespace Efficient { @@ -83,7 +82,7 @@ namespace StreamCompaction { /** * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ - void scanEfficient(int n, int *odata, const int *idata) { + void scanEfficient(int n, int *odata, const int *idata, int blockSize) { // Memory Allocation and Copying int power_size = pow(2, ilog2ceil(n)); int *cdata; @@ -120,14 +119,14 @@ namespace StreamCompaction { timer().endGpuTimer(); // Copy Back and Free Memory - cudaMemcpy(odata, cdata, sizeof(int) * power_size, cudaMemcpyDeviceToHost); + cudaMemcpy(odata, cdata, sizeof(int) * n, cudaMemcpyDeviceToHost); cudaFree(cdata); } /** * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ - void scan(int n, int *odata, const int *idata) { + void scan(int n, int *odata, const int *idata, int blockSize) { // Memory Allocation and Copying int power_size = pow(2, ilog2ceil(n)); int *cdata; @@ -160,7 +159,7 @@ namespace StreamCompaction { timer().endGpuTimer(); // Copy Back and Free Memory - cudaMemcpy(odata, cdata, sizeof(int) * power_size, cudaMemcpyDeviceToHost); + cudaMemcpy(odata, cdata, sizeof(int) * n, cudaMemcpyDeviceToHost); cudaFree(cdata); } @@ -173,7 +172,7 @@ namespace StreamCompaction { * @param idata The array of elements to compact. * @returns The number of elements remaining after compaction. */ - int compact(int n, int *odata, const int *idata) { + int compact(int n, int *odata, const int *idata, bool efficient, int blockSize) { // Memory Allocation and Copying int *bools = new int[n]; int *indices = new int[n]; @@ -195,7 +194,10 @@ namespace StreamCompaction { dim3 fullBlocksPerGrid((n + blockSize - 1) / blockSize); StreamCompaction::Common::kernMapToBoolean << > > (n, dev_bools, dev_idata); cudaMemcpy(bools, dev_bools, sizeof(int) * n, cudaMemcpyDeviceToHost); - scan(n, indices, bools); + if(efficient) + scanEfficient(n, indices, bools, blockSize); + else + scan(n, indices, bools, blockSize); cudaMemcpy(dev_indices, indices, sizeof(int) * n, cudaMemcpyHostToDevice); StreamCompaction::Common::kernScatter << > > (n, dev_odata, dev_idata, dev_bools, dev_indices); timer().endGpuTimer(); @@ -206,7 +208,7 @@ namespace StreamCompaction { cudaFree(dev_indices); cudaFree(dev_idata); cudaFree(dev_odata); - return indices[n-1] + bools[n-1]; + return indices[n - 1] + bools[n - 1];; } } } diff --git a/Project2-Stream-Compaction/stream_compaction/efficient.h b/Project2-Stream-Compaction/stream_compaction/efficient.h index 2aaa472..aecbe7b 100644 --- a/Project2-Stream-Compaction/stream_compaction/efficient.h +++ b/Project2-Stream-Compaction/stream_compaction/efficient.h @@ -6,10 +6,10 @@ namespace StreamCompaction { namespace Efficient { StreamCompaction::Common::PerformanceTimer& timer(); - void scanEfficient(int n, int *odata, const int *idata); + void scanEfficient(int n, int *odata, const int *idata, int blockSize); - void scan(int n, int *odata, const int *idata); + void scan(int n, int *odata, const int *idata, int blockSize); - int compact(int n, int *odata, const int *idata); + int compact(int n, int *odata, const int *idata, bool efficient, int blockSize); } } diff --git a/Project2-Stream-Compaction/stream_compaction/naive.cu b/Project2-Stream-Compaction/stream_compaction/naive.cu index e0bcf2f..f1a97ad 100644 --- a/Project2-Stream-Compaction/stream_compaction/naive.cu +++ b/Project2-Stream-Compaction/stream_compaction/naive.cu @@ -4,8 +4,6 @@ #include "naive.h" #include "device_launch_parameters.h" -#define blockSize 128 - namespace StreamCompaction { namespace Naive { using StreamCompaction::Common::PerformanceTimer; @@ -30,7 +28,7 @@ namespace StreamCompaction { /** * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ - void scan(int n, int *odata, const int *idata) { + void scan(int n, int *odata, const int *idata, int blockSize) { // Memory Allocation and Copying int *adata; int *bdata; diff --git a/Project2-Stream-Compaction/stream_compaction/naive.h b/Project2-Stream-Compaction/stream_compaction/naive.h index 37dcb06..96cd349 100644 --- a/Project2-Stream-Compaction/stream_compaction/naive.h +++ b/Project2-Stream-Compaction/stream_compaction/naive.h @@ -6,6 +6,6 @@ namespace StreamCompaction { namespace Naive { StreamCompaction::Common::PerformanceTimer& timer(); - void scan(int n, int *odata, const int *idata); + void scan(int n, int *odata, const int *idata, int blockSize); } } From c0e69054a648857e4dda744cc18b150bb6ee792b Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Mon, 16 Sep 2019 17:57:55 -0400 Subject: [PATCH 07/31] Normalization --- .../character_recognition/functions.cu | 7 ++++ .../character_recognition/functions.h | 2 ++ .../character_recognition/mlp.cu | 5 +++ Project2-Character-Recognition/src/main.cpp | 35 ++++++++++--------- 4 files changed, 33 insertions(+), 16 deletions(-) diff --git a/Project2-Character-Recognition/character_recognition/functions.cu b/Project2-Character-Recognition/character_recognition/functions.cu index 40d8831..710b06f 100644 --- a/Project2-Character-Recognition/character_recognition/functions.cu +++ b/Project2-Character-Recognition/character_recognition/functions.cu @@ -99,4 +99,11 @@ namespace Functions { input_output[index] = input[index] > 0 ? input_output[index] : 0; } } + __global__ void normalize(float * array, int x_dim, int y_dim) + { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < x_dim * y_dim) { + array[index] = ((array[index] * 2) - 1) / 10; + } + } } \ No newline at end of file diff --git a/Project2-Character-Recognition/character_recognition/functions.h b/Project2-Character-Recognition/character_recognition/functions.h index e10ef72..4a08ce5 100644 --- a/Project2-Character-Recognition/character_recognition/functions.h +++ b/Project2-Character-Recognition/character_recognition/functions.h @@ -31,4 +31,6 @@ namespace Functions { // Custom Kernel for gradient descent __global__ void KernelElementwiseMultiplySigmoid(float* input_output, float* input, int x_dim, int y_dim); __global__ void KernelElementwiseMultiplyRelu(float* input_output, float* input, int x_dim, int y_dim); + + __global__ void normalize(float* array, int x_dim, int y_dim); } diff --git a/Project2-Character-Recognition/character_recognition/mlp.cu b/Project2-Character-Recognition/character_recognition/mlp.cu index 38d3588..e61ca51 100644 --- a/Project2-Character-Recognition/character_recognition/mlp.cu +++ b/Project2-Character-Recognition/character_recognition/mlp.cu @@ -247,7 +247,12 @@ namespace CharacterRecognition { // Initialize weight matrices with random numbers curandGenerateUniform(prng, weights_IH, INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE); + dim3 ihblocks((INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE + blockSize - 1) / blockSize); + Functions::normalize << > > (weights_IH, INPUT_LAYER_SIZE, HIDDEN_LAYER_SIZE); + curandGenerateUniform(prng, weights_HO, HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE); + dim3 hoblocks((HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE + blockSize - 1) / blockSize); + Functions::normalize << > > (weights_HO, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); // Debug/Print //printf("Initial Weight Matrices\n"); diff --git a/Project2-Character-Recognition/src/main.cpp b/Project2-Character-Recognition/src/main.cpp index ca50552..a40db4b 100644 --- a/Project2-Character-Recognition/src/main.cpp +++ b/Project2-Character-Recognition/src/main.cpp @@ -32,6 +32,7 @@ void loadCharacterDataset(int num_instances, int input_layer, int output_layer, infile.open(file_name); int line_num = 0; string line; + std::string::size_type sz; while (getline(infile, line)){ if (line_num == 0) { ilabel[output_layer*(i - 1) + stoi(line) - 1] = 1; @@ -40,7 +41,9 @@ void loadCharacterDataset(int num_instances, int input_layer, int output_layer, stringstream ssin(line); int k = 0; while (ssin.good() && k < input_layer) { - ssin >> idata[output_layer*(i - 1) + k]; + std::string temp; + ssin >> temp; + idata[output_layer*(i - 1) + k] = stof(temp, &sz) / 255; ++k; } } @@ -72,31 +75,31 @@ void loadXORDataset(float* idata, float* ilabel) { int main(int argc, char* argv[]) { // Model Architecture and Num Instances - //int input_layer = 10201; - //int hidden_layer = 4; - //int output_layer = 52; - //int num_instances = 52; + int input_layer = 10201; + int hidden_layer = 50; + int output_layer = 52; + int num_instances = 52; - int input_layer = 2; - int hidden_layer = 4; - int output_layer = 2; - int num_instances = 4; + //int input_layer = 2; + //int hidden_layer = 4; + //int output_layer = 2; + //int num_instances = 4; // Initialize Network CharacterRecognition::init(input_layer, hidden_layer, output_layer); // Load Dataset - //float *idata = new float[num_instances * 10201]; - //float *ilabel = new float[num_instances * output_layer]; - //loadCharacterDataset(num_instances, input_layer, output_layer, idata, ilabel); + float *idata = new float[num_instances * 10201]; + float *ilabel = new float[num_instances * output_layer]; + loadCharacterDataset(num_instances, input_layer, output_layer, idata, ilabel); - float *idata = new float[8]; - float *ilabel = new float[8]; - loadXORDataset(idata, ilabel); + //float *idata = new float[8]; + //float *ilabel = new float[8]; + //loadXORDataset(idata, ilabel); // Parameters int epochs = 2000; - float learning_rate = 0.5; + float learning_rate = 0.1f; // Train CharacterRecognition::train(idata, ilabel, num_instances, epochs, learning_rate); From 558df6ca504f1ffd6dfa212766f45e2ada9efeaa Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Tue, 17 Sep 2019 00:51:21 -0400 Subject: [PATCH 08/31] Modularizing, Comments,Bug Fixes --- .../build/model_10201_10_52_w1.txt | 10201 ++++++++++++++++ .../character_recognition/functions.cu | 2 +- .../character_recognition/mlp.cu | 141 +- .../character_recognition/mlp.h | 27 +- Project2-Character-Recognition/src/main.cpp | 126 +- 5 files changed, 10410 insertions(+), 87 deletions(-) create mode 100644 Project2-Character-Recognition/build/model_10201_10_52_w1.txt diff --git a/Project2-Character-Recognition/build/model_10201_10_52_w1.txt b/Project2-Character-Recognition/build/model_10201_10_52_w1.txt new file mode 100644 index 0000000..dcadc08 --- /dev/null +++ b/Project2-Character-Recognition/build/model_10201_10_52_w1.txt @@ -0,0 +1,10201 @@ +-0.229452 -0.364996 -0.646251 0.213389 -0.248253 -0.0877231 0.521544 -0.900273 0.614604 0.336843 -0.616295 -0.958565 0.703495 0.693026 0.152451 0.211475 0.525007 0.137701 0.575865 -0.757177 +0.372257 0.0592687 0.261209 0.62904 -0.738631 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a/Project2-Character-Recognition/character_recognition/functions.cu b/Project2-Character-Recognition/character_recognition/functions.cu index 710b06f..064b510 100644 --- a/Project2-Character-Recognition/character_recognition/functions.cu +++ b/Project2-Character-Recognition/character_recognition/functions.cu @@ -103,7 +103,7 @@ namespace Functions { { int index = blockIdx.x * blockDim.x + threadIdx.x; if (index < x_dim * y_dim) { - array[index] = ((array[index] * 2) - 1) / 10; + array[index] = ((array[index] * 2) - 1); } } } \ No newline at end of file diff --git a/Project2-Character-Recognition/character_recognition/mlp.cu b/Project2-Character-Recognition/character_recognition/mlp.cu index e61ca51..5a81b11 100644 --- a/Project2-Character-Recognition/character_recognition/mlp.cu +++ b/Project2-Character-Recognition/character_recognition/mlp.cu @@ -8,6 +8,9 @@ #include "device_launch_parameters.h" #include #include +#include +#include +#include #define blockSize 128 @@ -27,7 +30,6 @@ void print_matrix(const float *devA, int nr_rows_A, int nr_cols_A) { } printf("\n"); } - printf("\n"); } namespace StreamCompaction { @@ -66,7 +68,6 @@ namespace StreamCompaction { } namespace CharacterRecognition { - using Common::PerformanceTimer; PerformanceTimer& timer() { @@ -86,7 +87,7 @@ namespace CharacterRecognition { cublasSgemm(cublas_handle, CUBLAS_OP_N, CUBLAS_OP_N, nr_rows_A, nr_cols_B, nr_cols_A, alpha, A, lda, B, ldb, beta, C, ldc); } - /* Forward Pass for one instance + /* Forward Pass 1. Multiply input with input and hidden layer weights 2. Apply Sigmoid 3. Multiply hidden layer activation with hidden and output layer weights @@ -103,6 +104,7 @@ namespace CharacterRecognition { // Matrix Multiply Hidden layer and Weights 2 gpu_blas_mmul(h_sigmoid, weights_HO, output, 1, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); + //print_matrix(output, 1, OUTPUT_LAYER_SIZE); // Apply Softmax dim3 outputLayerBlocks((OUTPUT_LAYER_SIZE + blockSize - 1) / blockSize); @@ -114,6 +116,12 @@ namespace CharacterRecognition { cudaFree(sum); } + /* + Back Propagation + 1. Calculates gradient for the weights between the hidden and output layer + 2. Calculates gradient for the weights between the input and hidden layer + 3. Updates gradients according to the learning rate + */ void backwardPropagation(float* dev_input, float* dev_output, float* learning_rate) { // Memory Allocation float *temp_hidden, *temp_output; @@ -142,9 +150,11 @@ namespace CharacterRecognition { // Free Memory cudaFree(temp_hidden); cudaFree(temp_output); - //print_matrix(weights_HO, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); } + /* + Calculates Cross Entropy Loss and populates loss + */ void calculateLoss(float* dev_output, float* loss) { // Memory Allocation float *temp_loss, *sum; @@ -160,8 +170,50 @@ namespace CharacterRecognition { cudaFree(sum); } - void train(float* idata, float* ilabel, int num_instances, int epochs, float learning_rate) { - float *dev_input, *dev_output, *dev_lr; + /* + Saves model weights + */ + void saveModel(std::string model_file) { + std::ofstream model_w1, model_w2; + model_w1.open(model_file + "_w1.txt"); + if (!model_w1) { + std::cerr << "Error: file could not be opened" << std::endl; + exit(1); + } + float *w1 = new float[INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE]; + cudaMemcpy(w1, weights_IH, INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE * sizeof(float), cudaMemcpyDeviceToHost); + for (int i = 0; i < INPUT_LAYER_SIZE; ++i) { + for (int j = 0; j < HIDDEN_LAYER_SIZE; ++j) { + model_w1 << w1[j * INPUT_LAYER_SIZE + i] << "\t"; + } + model_w1 << "\n"; + } + model_w1.close(); + + model_w2.open(model_file + "_w2.txt"); + if (!model_w2) { + std::cerr << "Error: file could not be opened" << std::endl; + exit(1); + } + float *w2 = new float[HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE]; + cudaMemcpy(w2, weights_IH, HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE * sizeof(float), cudaMemcpyDeviceToHost); + for (int i = 0; i < HIDDEN_LAYER_SIZE; ++i) { + for (int j = 0; j < OUTPUT_LAYER_SIZE; ++j) { + model_w2 << w2[j * HIDDEN_LAYER_SIZE + i] << "\t"; + } + model_w2 << "\n"; + } + model_w2.close(); + } + + /* + Trains the model + 1. Creats reqiured device buffers + 2. Trains the model ( Forward Pass, Backward Pass, Loss calculation) epoch number of times + 3. Saves the model weights + */ + void train(float* idata, float* ilabel, int num_instances, int epochs, float learning_rate, std::string model_file) { + float *dev_input, *dev_output, *dev_lr, *dev_instances; // Device Buffer for Input cudaMalloc((void**)&dev_input, num_instances * INPUT_LAYER_SIZE * sizeof(float)); checkCUDAErrorFn("cudaMalloc dev_input failed!"); @@ -177,43 +229,76 @@ namespace CharacterRecognition { thrust::device_ptr dev_ptr(dev_lr); thrust::fill(dev_ptr, dev_ptr + 1, learning_rate); + // Device Buffer for Instances + cudaMalloc((void**)&dev_instances, sizeof(float)); + thrust::device_ptr dev_instance_ptr(dev_instances); + thrust::fill(dev_instance_ptr, dev_instance_ptr + 1, num_instances); + // Train float *dev_loss; cudaMalloc((void**)&dev_loss, epochs * sizeof(float)); cudaMemset(dev_loss, 0, epochs * sizeof(float)); float *loss = new float[1]; + std::cout << "Epoch Loss" << std::endl; for (int e = 0; e < epochs; e++) { for (int i = 0; i < num_instances; i++) { - //if (e % 100 == 0) { - // printf("Epoch %d Input: ", e); - // print_matrix(dev_input + (i * INPUT_LAYER_SIZE), 1, INPUT_LAYER_SIZE); - //} // Forward Pass forwardPass(dev_input + (i * INPUT_LAYER_SIZE)); - // Back Propagation backwardPropagation(dev_input + (i * INPUT_LAYER_SIZE), dev_output + (i * OUTPUT_LAYER_SIZE), dev_lr); - // Loss Calculation calculateLoss(dev_output + (i * OUTPUT_LAYER_SIZE), dev_loss + e); - //if (e % 100 == 0) { - // printf("output probabilities: "); - // print_matrix(o_softmax, 1, OUTPUT_LAYER_SIZE); - //} } - - printf("Epoch: %d, Loss:", e); - print_matrix(dev_loss+e, 1, 1); + Functions::Divide << < 1, 1 >>>(dev_loss + e, dev_instances, 1, 1); + std::cout << e << " "; + print_matrix(dev_loss + e, 1, 1); } - //print_matrix(dev_loss, epochs, 1); + saveModel(model_file); } - void test(int* idata, int* ilabel, int* olabel) { - + /* + Predicts ouptut for num of instances in idata variable + and logs the target variable and the predicted variable + along with the confidence (prediction probability) + */ + void test(float* idata, float* true_labels, int num_instances) { + float *dev_input, *output; + int *input = new int[num_instances]; + for (int i = 0; i < num_instances; i++) + input[i] = i; + output = new float[OUTPUT_LAYER_SIZE]; + cudaMalloc((void**)&dev_input, num_instances * INPUT_LAYER_SIZE * sizeof(float)); + checkCUDAErrorFn("cudaMalloc dev_input failed!"); + cudaMemcpy(dev_input, idata, num_instances * INPUT_LAYER_SIZE * sizeof(float), cudaMemcpyHostToDevice); + std::random_shuffle(input, input + num_instances); + for (int k = 0; k < num_instances; k++) { + int i = input[k]; + forwardPass(dev_input + (i * INPUT_LAYER_SIZE)); + cudaMemcpy(output, o_softmax, OUTPUT_LAYER_SIZE * sizeof(float), cudaMemcpyDeviceToHost); + float maxProbability = -1; + int argmax = -1; + int true_label = -1; + for (int j = 0; j < OUTPUT_LAYER_SIZE; j++) { + if (output[j] > maxProbability) { + maxProbability = output[j]; + argmax = j; + } + if (true_labels[(i * OUTPUT_LAYER_SIZE) + j] == 1) + true_label = j; + } + std::cout << "Target Variable: " << true_label << " Predicted: " << argmax << " with probability " << maxProbability << std::endl; + } + delete[] output; + delete[] input; + cudaFree(dev_input); } + /* + Initializes the model framework + 1. Allocates memory for all Weight Matrices, Gradient Matrics and Hidden Layers + 2. Initializes the weight matrices with random numbers in the range of [-1, 1] + */ void init(int input_size, int hidden_size, int output_size) { - printf("Init\n"); // Initialize Layer Sizes INPUT_LAYER_SIZE = input_size; HIDDEN_LAYER_SIZE = hidden_size; @@ -243,7 +328,7 @@ namespace CharacterRecognition { // Curand Random Number Generator and Seed curandGenerator_t prng; curandCreateGenerator(&prng, CURAND_RNG_PSEUDO_DEFAULT); - curandSetPseudoRandomGeneratorSeed(prng, 7); + curandSetPseudoRandomGeneratorSeed(prng, 70); // Initialize weight matrices with random numbers curandGenerateUniform(prng, weights_IH, INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE); @@ -253,13 +338,11 @@ namespace CharacterRecognition { curandGenerateUniform(prng, weights_HO, HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE); dim3 hoblocks((HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE + blockSize - 1) / blockSize); Functions::normalize << > > (weights_HO, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); - - // Debug/Print - //printf("Initial Weight Matrices\n"); - //print_matrix(weights_IH, INPUT_LAYER_SIZE, HIDDEN_LAYER_SIZE); - //print_matrix(weights_HO, HIDDEN_LAYER_SIZE, OUTPUT_LAYER_SIZE); } + /* + Clears all model matrices, buffers and destroys the handles + */ void free() { cudaFree(weights_IH); cudaFree(weights_HO); diff --git a/Project2-Character-Recognition/character_recognition/mlp.h b/Project2-Character-Recognition/character_recognition/mlp.h index e94d8f5..72a0f24 100644 --- a/Project2-Character-Recognition/character_recognition/mlp.h +++ b/Project2-Character-Recognition/character_recognition/mlp.h @@ -4,13 +4,30 @@ namespace CharacterRecognition { Common::PerformanceTimer& timer(); - // Initializes - // 1. CUBLAS Handle - // 2. Weight Matrices + /* + Initializes the model framework + 1. Allocates memory for all Weight Matrices, Gradient Matrics and Hidden Layers + 2. Initializes the weight matrices with random numbers in the range of [-1, 1] + */ void init(int input_size, int hidden_size, int output_size); - void train(float* idata, float* ilabel, int num_instances, int epochs, float learning_rate); + /* + Trains the model + 1. Creats reqiured device buffers + 2. Trains the model ( Forward Pass, Backward Pass, Loss calculation) epoch number of times + 3. Saves the model weights + */ + void train(float* idata, float* ilabel, int num_instances, int epochs, float learning_rate, std::string model_file); + + /* + Predicts ouptut for num of instances in idata variable + and logs the target variable and the predicted variable + along with the confidence (prediction probability) + */ + void test(float* idata, float* true_label, int num_instances); - // Frees memory and destroys CUBLAS handle + /* + Clears all model matrices, buffers and destroys the handles + */ void free(); } diff --git a/Project2-Character-Recognition/src/main.cpp b/Project2-Character-Recognition/src/main.cpp index a40db4b..11e4584 100644 --- a/Project2-Character-Recognition/src/main.cpp +++ b/Project2-Character-Recognition/src/main.cpp @@ -23,91 +23,113 @@ void loadCharacterDataset(int num_instances, int input_layer, int output_layer, for (int i = 0; i < num_instances * output_layer; i++) ilabel[i] = 0; + std::string file_name; for (int i = 1; i <= num_instances; i++) { - ifstream infile; - std::string file_name; std::stringstream ss; ss << setw(2) << std::setfill('0') << i; file_name = "C:\\Users\\djjindal\\Project2-Number-Algorithms\\Project2-Character-Recognition\\data-set\\" + ss.str() + "info.txt"; - infile.open(file_name); - int line_num = 0; + ifstream infile(file_name); string line; - std::string::size_type sz; - while (getline(infile, line)){ - if (line_num == 0) { - ilabel[output_layer*(i - 1) + stoi(line) - 1] = 1; - } - if (line_num == 2) { - stringstream ssin(line); - int k = 0; - while (ssin.good() && k < input_layer) { - std::string temp; - ssin >> temp; - idata[output_layer*(i - 1) + k] = stof(temp, &sz) / 255; - ++k; + if (infile.is_open()) { + int line_num = 0; + while (getline(infile, line)) { + std::string::size_type sz; + if (line_num == 0) { + float label = std::stof(line, &sz); + int idx = output_layer * (i - 1) + (label - 1); + ilabel[idx] = 1; + } + if (line_num == 2) { + stringstream ssin(line); + int k = 0; + while (ssin.good() && k < input_layer) { + std::string temp; + ssin >> temp; + idata[input_layer*(i - 1) + k] = stof(temp, &sz) / 255.0; + ++k; + } } + line_num++; } - line_num++; + infile.close(); } - infile.close(); } } void loadXORDataset(float* idata, float* ilabel) { + idata[0] = 0; idata[1] = 0; ilabel[0] = 1; ilabel[1] = 0; + idata[2] = 1; idata[3] = 0; ilabel[2] = 0; ilabel[3] = 1; + idata[4] = 0; idata[5] = 1; ilabel[4] = 0; ilabel[5] = 1; + idata[6] = 1; idata[7] = 1; ilabel[6] = 1; ilabel[7] = 0; +} + +void loadXORTestDataset(float* idata) { idata[0] = 0; - idata[1] = 0; - idata[2] = 1; - idata[3] = 0; - idata[4] = 0; - idata[5] = 1; - idata[6] = 1; - idata[7] = 1; - - ilabel[0] = 1; - ilabel[1] = 0; - ilabel[2] = 0; - ilabel[3] = 1; - ilabel[4] = 0; - ilabel[5] = 1; - ilabel[6] = 1; - ilabel[7] = 0; + idata[1] = 1; } -int main(int argc, char* argv[]) { +/* + Driver Function for XOR Model +*/ +void XORModel() { + // Model Architecture and Num Instances + int input_layer = 2; + int hidden_layer = 4; + int output_layer = 2; + int num_instances = 4; + + // Initialize Network + CharacterRecognition::init(input_layer, hidden_layer, output_layer); + + // Load Dataset + float *idata = new float[8]; + float *ilabel = new float[8]; + loadXORDataset(idata, ilabel); + + // Parameters + int epochs = 2000; + float learning_rate = 0.1f; + + // Train + CharacterRecognition::train(idata, ilabel, num_instances, epochs, learning_rate, "xor_model"); + + // Test + CharacterRecognition::test(idata, ilabel, num_instances); + + // Free Resources + CharacterRecognition::free(); +} +/* + Driver Function for Character Recognition Model +*/ +void CharacterRecognitionModel(int hidden_layer, float learning_rate = 0.1f) { // Model Architecture and Num Instances int input_layer = 10201; - int hidden_layer = 50; int output_layer = 52; int num_instances = 52; - //int input_layer = 2; - //int hidden_layer = 4; - //int output_layer = 2; - //int num_instances = 4; - // Initialize Network CharacterRecognition::init(input_layer, hidden_layer, output_layer); // Load Dataset - float *idata = new float[num_instances * 10201]; + float *idata = new float[num_instances * input_layer]; float *ilabel = new float[num_instances * output_layer]; loadCharacterDataset(num_instances, input_layer, output_layer, idata, ilabel); - //float *idata = new float[8]; - //float *ilabel = new float[8]; - //loadXORDataset(idata, ilabel); - // Parameters - int epochs = 2000; - float learning_rate = 0.1f; + int epochs = 500; // Train - CharacterRecognition::train(idata, ilabel, num_instances, epochs, learning_rate); - + CharacterRecognition::train(idata, ilabel, num_instances, epochs, learning_rate, "cr_model"); // Test + CharacterRecognition::test(idata, ilabel, num_instances); // Free Resources CharacterRecognition::free(); - +} + +int main(int argc, char* argv[]) { + //XORModel(); + CharacterRecognitionModel(20, 0.1); } From ab2c194c53166e6bbbcdec47db8e5db56e25be7f Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Tue, 17 Sep 2019 15:32:55 -0400 Subject: [PATCH 09/31] Stream Compaction Performance Plots --- .../img/Compaction_ArraySize.png | Bin 0 -> 132756 bytes .../img/Compaction_BlockSize.png | Bin 0 -> 87620 bytes .../img/Compaction_NPOT_ArraySize.png | Bin 0 -> 112266 bytes .../img/Scan_BlockSize.png | Bin 0 -> 112848 bytes .../img/Scan_NPOT_ArraySize.png | Bin 0 -> 113121 bytes .../img/Scan_POT_ArraySize.png | Bin 0 -> 139275 bytes 6 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 Project2-Stream-Compaction/img/Compaction_ArraySize.png create mode 100644 Project2-Stream-Compaction/img/Compaction_BlockSize.png create mode 100644 Project2-Stream-Compaction/img/Compaction_NPOT_ArraySize.png create mode 100644 Project2-Stream-Compaction/img/Scan_BlockSize.png create mode 100644 Project2-Stream-Compaction/img/Scan_NPOT_ArraySize.png create mode 100644 Project2-Stream-Compaction/img/Scan_POT_ArraySize.png diff --git 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metricsForDifferentBlockSize(); system("pause"); // stop Win32 console from closing on exit } + +const int SIZE = 1 << 8; // feel free to change the size of array +const int NPOT = SIZE - 3; // Non-Power-Of-Two +int *a = new int[SIZE]; +int *b = new int[SIZE]; +int *c = new int[SIZE]; + +int main(int argc, char* argv[]) { + // Scan tests + + printf("\n"); + printf("****************\n"); + printf("** SCAN TESTS **\n"); + printf("****************\n"); + + genArray(SIZE - 1, a, 50); // Leave a 0 at the end to test that edge case + a[SIZE - 1] = 0; + printArray(SIZE, a, true); + + // initialize b using StreamCompaction::CPU::scan you implement + // We use b for further comparison. Make sure your StreamCompaction::CPU::scan is correct. + // At first all cases passed because b && c are all zeroes. + zeroArray(SIZE, b); + printDesc("cpu scan, power-of-two"); + StreamCompaction::CPU::scan(SIZE, b, a); + printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); + printArray(SIZE, b, true); + + zeroArray(SIZE, c); + printDesc("cpu scan, non-power-of-two"); + StreamCompaction::CPU::scan(NPOT, c, a); + printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); + printArray(NPOT, b, true); + printCmpResult(NPOT, b, c); + + zeroArray(SIZE, c); + printDesc("naive scan, power-of-two"); + StreamCompaction::Naive::scan(SIZE, c, a); + printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(SIZE, c, true); + printCmpResult(SIZE, b, c); + + /* For bug-finding only: Array of 1s to help find bugs in stream compaction or scan + onesArray(SIZE, c); + printDesc("1s array for finding bugs"); + StreamCompaction::Naive::scan(SIZE, c, a); + printArray(SIZE, c, true); */ + + zeroArray(SIZE, c); + printDesc("naive scan, non-power-of-two"); + StreamCompaction::Naive::scan(NPOT, c, a); + printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(SIZE, c, true); + printCmpResult(NPOT, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient scan, power-of-two"); + StreamCompaction::Efficient::scan(SIZE, c, a); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(SIZE, c, true); + printCmpResult(SIZE, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient scan, non-power-of-two"); + StreamCompaction::Efficient::scan(NPOT, c, a); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(NPOT, c, true); + printCmpResult(NPOT, b, c); + + zeroArray(SIZE, c); + printDesc("optimized work-efficient scan, power-of-two"); + StreamCompaction::Efficient::scanEfficient(SIZE, c, a); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(SIZE, c, true); + printCmpResult(SIZE, b, c); + + zeroArray(SIZE, c); + printDesc("optimized work-efficient scan, non-power-of-two"); + StreamCompaction::Efficient::scanEfficient(NPOT, c, a); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(NPOT, c, true); + printCmpResult(NPOT, b, c); + + zeroArray(SIZE, c); + printDesc("thrust scan, power-of-two"); + StreamCompaction::Thrust::scan(SIZE, c, a); + printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(SIZE, c, true); + printCmpResult(SIZE, b, c); + + zeroArray(SIZE, c); + printDesc("thrust scan, non-power-of-two"); + StreamCompaction::Thrust::scan(NPOT, c, a); + printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(NPOT, c, true); + printCmpResult(NPOT, b, c); + + printf("\n"); + printf("*****************************\n"); + printf("** STREAM COMPACTION TESTS **\n"); + printf("*****************************\n"); + + // Compaction tests + + genArray(SIZE - 1, a, 4); // Leave a 0 at the end to test that edge case + a[SIZE - 1] = 0; + printArray(SIZE, a, true); + + int count, expectedCount, expectedNPOT; + + // initialize b using StreamCompaction::CPU::compactWithoutScan you implement + // We use b for further comparison. Make sure your StreamCompaction::CPU::compactWithoutScan is correct. + zeroArray(SIZE, b); + printDesc("cpu compact without scan, power-of-two"); + count = StreamCompaction::CPU::compactWithoutScan(SIZE, b, a); + printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); + expectedCount = count; + printArray(count, b, true); + printCmpLenResult(count, expectedCount, b, b); + + zeroArray(SIZE, c); + printDesc("cpu compact without scan, non-power-of-two"); + count = StreamCompaction::CPU::compactWithoutScan(NPOT, c, a); + printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); + expectedNPOT = count; + printArray(count, c, true); + printCmpLenResult(count, expectedNPOT, b, c); + + zeroArray(SIZE, c); + printDesc("cpu compact with scan"); + count = StreamCompaction::CPU::compactWithScan(SIZE, c, a); + printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)"); + printArray(count, c, true); + printCmpLenResult(count, expectedCount, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient compact, power-of-two"); + count = StreamCompaction::Efficient::compact(SIZE, c, a, false); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(count, c, true); + printCmpLenResult(count, expectedCount, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient compact, non-power-of-two"); + count = StreamCompaction::Efficient::compact(NPOT, c, a, false); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(count, c, true); + printCmpLenResult(count, expectedNPOT, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient compact, power-of-two"); + count = StreamCompaction::Efficient::compact(SIZE, c, a); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(count, c, true); + printCmpLenResult(count, expectedCount, b, c); + + zeroArray(SIZE, c); + printDesc("work-efficient compact, non-power-of-two"); + count = StreamCompaction::Efficient::compact(NPOT, c, a); + printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)"); + printArray(count, c, true); + printCmpLenResult(count, expectedNPOT, b, c); + + system("pause"); // stop Win32 console from closing on exit + delete[] a; + delete[] b; + delete[] c; +} + + diff --git a/Project2-Stream-Compaction/stream_compaction/efficient.h b/Project2-Stream-Compaction/stream_compaction/efficient.h index aecbe7b..efcc9d4 100644 --- a/Project2-Stream-Compaction/stream_compaction/efficient.h +++ b/Project2-Stream-Compaction/stream_compaction/efficient.h @@ -6,10 +6,10 @@ namespace StreamCompaction { namespace Efficient { StreamCompaction::Common::PerformanceTimer& timer(); - void scanEfficient(int n, int *odata, const int *idata, int blockSize); + void scanEfficient(int n, int *odata, const int *idata, int blockSize = 128); - void scan(int n, int *odata, const int *idata, int blockSize); + void scan(int n, int *odata, const int *idata, int blockSize = 128); - int compact(int n, int *odata, const int *idata, bool efficient, int blockSize); + int compact(int n, int *odata, const int *idata, bool efficient = true, int blockSize = 128); } } diff --git a/Project2-Stream-Compaction/stream_compaction/naive.h b/Project2-Stream-Compaction/stream_compaction/naive.h index 96cd349..fe06256 100644 --- a/Project2-Stream-Compaction/stream_compaction/naive.h +++ b/Project2-Stream-Compaction/stream_compaction/naive.h @@ -6,6 +6,6 @@ namespace StreamCompaction { namespace Naive { StreamCompaction::Common::PerformanceTimer& timer(); - void scan(int n, int *odata, const int *idata, int blockSize); + void scan(int n, int *odata, const int *idata, int blockSize = 128); } } From 486a851e1a750a6da931b15b6e3adef9113d7bec Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Tue, 17 Sep 2019 16:47:00 -0400 Subject: [PATCH 11/31] Performance Analysis --- .../img/Compaction_ArraySize.png | Bin 132756 -> 30350 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/Project2-Stream-Compaction/img/Compaction_ArraySize.png b/Project2-Stream-Compaction/img/Compaction_ArraySize.png index 99db44d84b7a9c499141a7ad2998b20df5b329cb..97ee026b1ff399f450ea0f9e58c083039b52bdf9 100644 GIT binary patch literal 30350 zcmeFZcU)81yDqLXJb=X3w~{oyCXz4l&fz3Y9S_j$|C zlba^``vt@VcJ11=A8c^leAlio^>*$0;@iDn1OM}ry}S_cvD?pFUuRdvyJIxq%U7;f zjj!(7Rh6)h?YIZ{{_Q;jYrkE)4z%t3+l_~pIPcmOC5{YT!>tbs za&22&8rKY4URw0Zh+B&Fd3*TnZruy67c{;;HV}JY^0|cHFR!MKUB9wN@X%L+-#H(8 zsCURsX?&=3K4>ARA!nwvt#lsdHc#UY&C(b0%1pFpZB2)^LS+4uz8VM%NHWP5vt2%l 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zs8(AuEz97zuablJETxxr+*Zmas0!SDWg(Vvn83n2?#k=Gw8f+V5u}k64HWtm*N7k~n$zrNC2O(`7fmyqh(wYR~m9PoNxgv>HY~D!>#nAtEaR z;@Y9BdXGY5Uy=S3ESfh0y!1t~RsR03Nj+^2JeU5q3XEOwu2e38^~Ebe8;<+|RlrMT zf~q>fdCV&{kjVFXaqVOo6n!iAcrWnfkKG1tRiz&w6-hsZe9K>e%;qI{e!NrWT*P0~ z0O4&3N#J#vzMDEwQPYDj782@vBO*4#1tsmXx6A-QWVFgJtdzIQ7jWrd&B^>oBq^5U zHfCitRS%zaxj*;fujiz;0Dgv3;^3w7?~(uY*+2fLTtUht*RW0B>W|&^8zW=m?5iM| zQE~8%UE~jm{u(;=O)nSV+uW!fvKaqx7yt5C^f>6a2WR|>m;U88VQZ;}5kj6byeZ(;l` zjDIl|zYNRYn(;rc8AyEKPOcdLTaR=WvQ8GeH`?P Date: Tue, 17 Sep 2019 17:26:02 -0400 Subject: [PATCH 12/31] README v1 --- Project2-Stream-Compaction/README.md | 56 +++++++++++++++++++++++++--- 1 file changed, 50 insertions(+), 6 deletions(-) diff --git a/Project2-Stream-Compaction/README.md b/Project2-Stream-Compaction/README.md index 0e38ddb..e69e347 100644 --- a/Project2-Stream-Compaction/README.md +++ b/Project2-Stream-Compaction/README.md @@ -3,12 +3,56 @@ CUDA Stream Compaction **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2** -* (TODO) YOUR NAME HERE - * (TODO) [LinkedIn](), [personal website](), [twitter](), etc. -* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab) +* Disha Jindal: [Linkedin](https://www.linkedin.com/in/disha-jindal/) +* Tested on: Windows 10 Education, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, GTX 222 222MB (Moore 100B Lab) -### (TODO: Your README) +## Description +Stream Compaction is a very widely used algorithm with path tracer as one of the applications. This project implements GPU stream compaction in CUDA and compares the following algorithms for stream compaction along with analyzing the scan module: +1. CPU +2. GPU: Naive +3. GPU: Work-Efficient +4. GPU: Optimized Work-Efficient +5. GPU: Thrust -Include analysis, etc. (Remember, this is public, so don't put -anything here that you don't want to share with the world.) +## Performance Analysis +### Array Size +#### Scan +Scan is the most important module in stream compaction. The following plots show the performance of above mentioned five algorithms with varying array size. Two variations considered are number of elements in powers of two and not in powers of two. +

+ +**Analysis** - The difference between different implementations is clearly visible for large array size starting around two rasise to power 18. As we can see from the above plots that the scan on the CPU is performing the worst which is expected. The performance of Naive GPU approach and Work Efficient is almost the same with work efficient being slighly better which is because of the extra overhead in the work efficient approach. Thurst Implementation is better than these three. But the optimized version of work efficient scan in which I am omitting all unnecessary threads and keep only the ones required to do the computation in each iteration of Up Sweep and Down Sweep. Due to these optimizations, this approaching is taking the least amount of time of all. A similar trend can be seen in non power of two. +#### Stream Compaction +The following plots show the performance of different stream compaction algorithms with array size. Two variations considered are number of elements in powers of two and not in powers of two. +

+ +**Analysis** - CPU with scan is performing the worst because of no parrallism. Followed by this is the Work efficient approach. Naive Implementation with GPU is omiitted beacuse it performed poorly in scan. It is interesting to see the sudden performance drop of Work Efficient GPU algorithm on non power of two variant after a certain point. But it performs better than the CPU approach for the most part because of the parallelism and runtime of log n as compared to n with the CPU approach. Optimized approach further improves the performance. The CPU implementation without scan is performing the best. One of the benefits of the GPU approach is that it compacts in place. + +### Block Size +Next, I did the performance analysis of both scan and stream compaction for different block sizes. +#### Scan +The following plot shows the performance of three GPU implementation with varying block sizes on the SCAN module. +

+ +**Analysis** - The trend is same for all implementations. As we increase the block size till 32, the performance increases because the warp size is 32 so keep the block size less than 32 is not optimal. The performance does not change on increasing the blocksize after 32. + +#### Stream Compaction +The following plot shows the performance of two GPU implementation with varying block sizes on the stream compaction. + +

+ +**Analysis** - The impact of the block size on the stream compaction performance is same as that the one on scan. The performance increases on increasing the blocksize till 32 and stays almost constant after that. +## Output +### Scan +Following is the screenshot of the main program for SCAN module showing the arrays and runtime of various algorthims (including power and two and not power of two variants) already given with an addition of optimized work efficient approach. + +

+ +### Stream Compaction +Following is the screenshot of the main program for Stream Compaction showing the arrays and runtime of various algorthims (including power and two and not power of two variants) already given with an addition of optimized work efficient approach. + +

+ +## Extra Credits +### Optimized Work Efficient Approach +I analyzed the performance of Work Efficient GPU approach. The occupancy of the threads keeps on dropping by a factor of two with each iteration of up and down sweep. I implemented the optimized variant of Work Efficient Approach to make sure that all threads are occupied in each iteration. It makes sure that It only lanuches the required number of threads in each iteration rather than launchng the fixed number of threads in each iteartion. It improved the performance significantly in scan as well as stream compaction, it can be seen in the plots above. From 838d3cc6fa725eb2b39452841c7b42afcecb5dd1 Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Tue, 17 Sep 2019 17:42:43 -0400 Subject: [PATCH 13/31] Learning Curves and Predictions --- .../img/LC_HiddenNodes.PNG | Bin 0 -> 13867 bytes .../img/LC_LearningRate.PNG | Bin 0 -> 17121 bytes .../img/Predictions_CharacterRecognition.PNG | Bin 0 -> 87186 bytes .../img/Predictions_XOR.PNG | Bin 0 -> 8249 bytes 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 Project2-Character-Recognition/img/LC_HiddenNodes.PNG create mode 100644 Project2-Character-Recognition/img/LC_LearningRate.PNG create mode 100644 Project2-Character-Recognition/img/Predictions_CharacterRecognition.PNG create mode 100644 Project2-Character-Recognition/img/Predictions_XOR.PNG diff --git a/Project2-Character-Recognition/img/LC_HiddenNodes.PNG b/Project2-Character-Recognition/img/LC_HiddenNodes.PNG new file mode 100644 index 0000000000000000000000000000000000000000..1615480f8dc9cdf34456f578cec570364e9f08be 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-0.144698 0.0723392 -0.47875 -0.535207 -0.964697 -0.784365 0.971532 0.686749 -0.0509476 0.456676 0.217533 -0.605717 -0.537964 -0.726898 -0.486827 -0.526213 0.694674 0.37939 0.534274 -0.962115 0.0959312 -0.231114 -0.54947 -0.387197 -0.919431 -0.958242 -0.921709 -0.866326 0.855638 diff --git a/Project2-Character-Recognition/weights/xor_model_w1.txt b/Project2-Character-Recognition/weights/xor_model_w1.txt new file mode 100644 index 0000000..e57778b --- /dev/null +++ b/Project2-Character-Recognition/weights/xor_model_w1.txt @@ -0,0 +1,2 @@ +5.05723 1.81342 -3.06671 5.24548 +5.09212 -1.13558 6.08867 -2.60959 diff --git a/Project2-Character-Recognition/weights/xor_model_w2.txt b/Project2-Character-Recognition/weights/xor_model_w2.txt new file mode 100644 index 0000000..64a294a --- /dev/null +++ b/Project2-Character-Recognition/weights/xor_model_w2.txt @@ -0,0 +1,4 @@ +5.05723 -3.06671 +5.09212 6.08867 +1.81342 5.24548 +-1.13558 -2.60959 From 78846d173b1342afc519a2de93f89b49f7c1dd93 Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Tue, 17 Sep 2019 19:33:03 -0400 Subject: [PATCH 18/31] README v1 --- Project2-Character-Recognition/README.md | 74 ++++++++++++++++++++++-- 1 file changed, 68 insertions(+), 6 deletions(-) diff --git a/Project2-Character-Recognition/README.md b/Project2-Character-Recognition/README.md index 4503fac..362ea36 100644 --- a/Project2-Character-Recognition/README.md +++ b/Project2-Character-Recognition/README.md @@ -3,12 +3,74 @@ CUDA Character Recognition **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2** -* (TODO) YOUR NAME HERE - * (TODO) [LinkedIn](), [personal website](), [twitter](), etc. -* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab) +* Disha Jindal: [Linkedin](https://www.linkedin.com/in/disha-jindal/) +* Tested on: Windows 10 Education, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, GTX 222 222MB (Moore 100B Lab) -### (TODO: Your README) +## Description +This code implements a simple neural network (Multi-layer Perceptron) in CUDA. It is tested on XOR and Character Recognition dataset. Following architechure depicts a neural network with an input layer, hidden layer and output layer. +

-Include analysis, etc. (Remember, this is public, so don't put -anything here that you don't want to share with the world.) +### Architecture +**Input Layer** : The number of nodes in the input layer are equal to the number of features in an image which is 10201 in the given dataset. + +**Hidden Layer** : I have the analyzed the convergence of model with different number of hidden nodes. It converges pretty fast for any number greater than 10, refer Performance Analysis section for more details. + +**Output Layer**: The number of nodes in the output layer are equal to the number of classes which is 52 in the case of character recognition and 2 in case of XOR. + +**Activation Functions**: I tried two activation functions for the hidden layer: Sigmoid and ReLU and the model converges using any of these two. +

+ +**Loss Function**: I have used cross entropy loss for this classfication problem setup. + +### Implementation +`main.cpp` is the orchestrator. I have written two different functions one for XOR and the other one for Character Recognition. The required fucntion can be called from the main function. +`mlp.cu` is the file contains all MLP specific functions with `init`, `train`, `test` and `free` being the ones called from the main script. +`funcions.cu` contains the required kernels for elementwise operations and others. + +**Training** `train` function in `mlp.cu` orchestrators this. The entire training process runs spoch number of times. The main componants of the training phase are forward pass, backward pass and loss calculation. Forward pass does the matrix multiplication to populte all buffers for hidden and output layers. Backward pass computes the gradients and updates the weight matrices. The loss calculation step uses the true and predicted outputs to calculate the cross entropy loss which is plotted in the following learning curves. The last step is to save the final model weights to a file. + +**Testing** `test` function in `mlp.cu` orchestrators this. This comes into picture in the inference phase which comes after the model is trained. In this phase, I pass the input through the forward pass, calculate the argmax to find the output node with the maximum probability. This gives the output along with the model's confidence in that prediction. + +**Hyperparameter Tuning** The two main hyperparameters in this model are the number of nodes in the hidden layer and the learning rate. I have tuned both of these parameters and The Performance Analsysis section below tasks about this in detail. + +## Performance Analysis +Learning curves shows the model's learning process by plotting the loss at each epoch in the training phase. Following are the learning curves of various model on different parameters. +### XOR +Following is the learning curve for XOR. +

+ +### Character Recognition +#### Hidden Nodes +Following are the learning curves of models trained using different number of nodes in the hidden layer. It goes from 5 to 100 hidden nodes. Since, with 100 nodes the loss dropped to 0 in 3 iterations, there is no point increasing the nodes even further. This clearly shows that in learning rate of the model increases on increasing the number of hidden nodes. It takes more than 500 epochs in case of 5 or 10 hidden units. After 20, the loss goes to 0 within 100 epochs. +

+ +#### Learning Rate +Following are the learning curves of models trained using learning rates. The plots values from 0.01 to 1. If you look at the curves corresponding to 0.01, 0.05 and 0.1, it shows that the learning rate of model imporves with the increase in learning rate. This is because we are making bigger updates at each step. Whereas, if we go beyond that as can be seen from the curve corresponding to 0.5 and 1, it starts to learn faster but then it does not even converge. This is because we are making very large changes in the weights at each step which makes the gradients to get stuck at various local minimas (corresponding to each character rather than learning the actual pattern). +

+ +## Model Weights +There are two weight matrices in this model. + +**W1**: Weights between input and hidden layers. The dimesnions are INPUT_LAYER_SIZE * HIDDEN_LAYER_SIZE. + +**W2**: Weights between hidden and output layers. The dimesnions are HIDDEN_LAYER_SIZE * OUTPUT_LAYER_SIZE. + +Following are the links to the weight files for both XOR and Character Recognition models + +**XOR** +[W1](https://github.com/DishaJindal/Project2-Number-Algorithms/blob/master/Project2-Character-Recognition/weights/xor_model_w1.txt) , +[W2](https://github.com/DishaJindal/Project2-Number-Algorithms/blob/master/Project2-Character-Recognition/weights/xor_model_w2.txt) + +**Character Recognition** +[W1](https://github.com/DishaJindal/Project2-Number-Algorithms/blob/master/Project2-Character-Recognition/weights/cr_model_w1.txt) , +[W2](https://github.com/DishaJindal/Project2-Number-Algorithms/blob/master/Project2-Character-Recognition/weights/cr_model_w2.txt) +## Predictions +Following are the outputs for Character Recognition and XOR from the corresponding models. This is implemented in the test function. The inputs are shuffled before testing using shuffle method from `#include` to make sure that the model is not just learning the order of inputs. And then by using the forward pass and argmax logic, the target predictions and model predictions are printed along with the probabilities. +### Character Recognition + +

+ +### XOR + +

From b0bfabf1d159d58a508d62710409d4ee52f854c1 Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Tue, 17 Sep 2019 19:36:11 -0400 Subject: [PATCH 19/31] README --- README.md | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 3a0b2fe..baea5b5 100644 --- a/README.md +++ b/README.md @@ -3,14 +3,9 @@ CUDA Number Algorithms **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2** -* (TODO) YOUR NAME HERE - * (TODO) [LinkedIn](), [personal website](), [twitter](), etc. -* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab) +* Disha Jindal: [Linkedin](https://www.linkedin.com/in/disha-jindal/) +* Tested on: Windows 10 Education, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, GTX 222 222MB (Moore 100B Lab) -### (TODO: Your README) - -Link to the readmes of the other two subprojects. - -Add anything else you think is relevant up to this point. -(Remember, this is public, so don't put anything here that you don't want to share with the world.) +[CUDA Stream Compaction](https://github.com/DishaJindal/Project2-Number-Algorithms/edit/master/Project2-Stream-Compaction/README.md) +[CUDA Character Recognition](https://github.com/DishaJindal/Project2-Number-Algorithms/edit/master/Project2-Character-Recognition/README.md) From a2bb02a2819b1eb5a21144cf61614c1f1438cffc Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Tue, 17 Sep 2019 19:37:13 -0400 Subject: [PATCH 20/31] README --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index baea5b5..bac99f2 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,8 @@ CUDA Number Algorithms * Disha Jindal: [Linkedin](https://www.linkedin.com/in/disha-jindal/) * Tested on: Windows 10 Education, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, GTX 222 222MB (Moore 100B Lab) +Following are the links to the subprojects. + [CUDA Stream Compaction](https://github.com/DishaJindal/Project2-Number-Algorithms/edit/master/Project2-Stream-Compaction/README.md) [CUDA Character Recognition](https://github.com/DishaJindal/Project2-Number-Algorithms/edit/master/Project2-Character-Recognition/README.md) From 9a9fcb5ab3fd5c5086a450a4f11982b7a5664c36 Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Tue, 17 Sep 2019 19:56:57 -0400 Subject: [PATCH 21/31] Fixing Links --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index bac99f2..cb74a15 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,6 @@ CUDA Number Algorithms Following are the links to the subprojects. -[CUDA Stream Compaction](https://github.com/DishaJindal/Project2-Number-Algorithms/edit/master/Project2-Stream-Compaction/README.md) +[CUDA Stream Compaction](https://github.com/DishaJindal/Project2-Number-Algorithms/blob/master/Project2-Stream-Compaction/README.md) -[CUDA Character Recognition](https://github.com/DishaJindal/Project2-Number-Algorithms/edit/master/Project2-Character-Recognition/README.md) +[CUDA Character Recognition](https://github.com/DishaJindal/Project2-Number-Algorithms/blob/master/Project2-Character-Recognition/README.md) From e4965331e14d4616ca62f02f66bda388e37c713f Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Tue, 17 Sep 2019 22:25:41 -0400 Subject: [PATCH 22/31] Screenshot to triple tick output --- Project2-Stream-Compaction/README.md | 87 ++++++++++++++++++++++++++-- 1 file changed, 81 insertions(+), 6 deletions(-) diff --git a/Project2-Stream-Compaction/README.md b/Project2-Stream-Compaction/README.md index e69e347..eadec50 100644 --- a/Project2-Stream-Compaction/README.md +++ b/Project2-Stream-Compaction/README.md @@ -44,14 +44,89 @@ The following plot shows the performance of two GPU implementation with varying **Analysis** - The impact of the block size on the stream compaction performance is same as that the one on scan. The performance increases on increasing the blocksize till 32 and stays almost constant after that. ## Output ### Scan -Following is the screenshot of the main program for SCAN module showing the arrays and runtime of various algorthims (including power and two and not power of two variants) already given with an addition of optimized work efficient approach. - -

+Following is the output of the main program for SCAN module showing the arrays and runtime of various algorthims (including power and two and not power of two variants) already given with an addition of optimized work efficient approach. +``` +**************** +** SCAN TESTS ** +**************** + [ 2 20 35 39 13 6 16 0 ] +==== cpu scan, power-of-two ==== + elapsed time: 0.0005ms (std::chrono Measured) + [ 0 2 22 57 96 109 115 131 ] +==== cpu scan, non-power-of-two ==== + elapsed time: 0.0002ms (std::chrono Measured) + [ 0 2 22 57 96 ] + passed +==== naive scan, power-of-two ==== + elapsed time: 0.01856ms (CUDA Measured) + [ 0 2 22 57 96 109 115 131 ] + passed +==== naive scan, non-power-of-two ==== + elapsed time: 0.017408ms (CUDA Measured) + [ 0 2 22 57 96 0 0 0 ] + passed +==== work-efficient scan, power-of-two ==== + elapsed time: 0.072192ms (CUDA Measured) + [ 0 2 22 57 96 109 115 131 ] + passed +==== work-efficient scan, non-power-of-two ==== + elapsed time: 0.057344ms (CUDA Measured) + [ 0 2 22 57 96 ] + passed +==== optimized work-efficient scan, power-of-two ==== + elapsed time: 0.070112ms (CUDA Measured) + [ 0 2 22 57 96 109 115 131 ] + passed +==== optimized work-efficient scan, non-power-of-two ==== + elapsed time: 0.036864ms (CUDA Measured) + [ 0 2 22 57 96 ] + passed +==== thrust scan, power-of-two ==== + elapsed time: 5.54496ms (CUDA Measured) + [ 0 2 22 57 96 109 115 131 ] + passed +==== thrust scan, non-power-of-two ==== + elapsed time: 1.39277ms (CUDA Measured) + [ 0 2 22 57 96 ] + passed +``` ### Stream Compaction -Following is the screenshot of the main program for Stream Compaction showing the arrays and runtime of various algorthims (including power and two and not power of two variants) already given with an addition of optimized work efficient approach. - -

+Following is the output of the main program for Stream Compaction showing the arrays and runtime of various algorthims (including power and two and not power of two variants) already given with an addition of optimized work efficient approach. +``` +***************************** +** STREAM COMPACTION TESTS ** +***************************** + [ 0 0 3 3 3 2 0 0 ] +==== cpu compact without scan, power-of-two ==== + elapsed time: 0.0005ms (std::chrono Measured) + [ 3 3 3 2 ] + passed +==== cpu compact without scan, non-power-of-two ==== + elapsed time: 0.0002ms (std::chrono Measured) + [ 3 3 3 ] + passed +==== cpu compact with scan ==== + elapsed time: 0.1199ms (std::chrono Measured) + [ 3 3 3 2 ] + passed +==== work-efficient compact, power-of-two ==== + elapsed time: 0.39424ms (CUDA Measured) + [ 3 3 3 2 ] + passed +==== work-efficient compact, non-power-of-two ==== + elapsed time: 0.270848ms (CUDA Measured) + [ 3 3 3 ] + passed +==== work-efficient compact, power-of-two ==== + elapsed time: 0.33216ms (CUDA Measured) + [ 3 3 3 2 ] + passed +==== work-efficient compact, non-power-of-two ==== + elapsed time: 0.282624ms (CUDA Measured) + [ 3 3 3 ] + passed +``` ## Extra Credits ### Optimized Work Efficient Approach From 61cfa1cf61a1d7e259635153759d941be9c8ad95 Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Tue, 17 Sep 2019 23:07:26 -0400 Subject: [PATCH 23/31] Updating README with Radix Sort --- Project2-Stream-Compaction/README.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/Project2-Stream-Compaction/README.md b/Project2-Stream-Compaction/README.md index eadec50..2de990a 100644 --- a/Project2-Stream-Compaction/README.md +++ b/Project2-Stream-Compaction/README.md @@ -131,3 +131,15 @@ Following is the output of the main program for Stream Compaction showing the ar ## Extra Credits ### Optimized Work Efficient Approach I analyzed the performance of Work Efficient GPU approach. The occupancy of the threads keeps on dropping by a factor of two with each iteration of up and down sweep. I implemented the optimized variant of Work Efficient Approach to make sure that all threads are occupied in each iteration. It makes sure that It only lanuches the required number of threads in each iteration rather than launchng the fixed number of threads in each iteartion. It improved the performance significantly in scan as well as stream compaction, it can be seen in the plots above. + +### Radix Sort +I have implemented Radix Sort using the optimized version of Work Efficient GPU Scan. Code is present in the `` radix_sort.cu `` and ``radix_sort.h `` files under stream_compaction folder. I have added a corresponding test function ``void radixSortTest()`` in the main.cpp file to test the accuracy of the my implementation. This function is also called in the beginning of main function. Following is a sample ouput from the test. + +``` +********************* +** RADIX SORT TEST ** +********************* + [ 46 7 12 24 12 24 30 0 ] + [ 0 7 12 12 24 24 30 46 ] + passed +``` From f08230a335e5dd72c4a5f6e83fd7bfe1c67887b3 Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Tue, 17 Sep 2019 23:10:21 -0400 Subject: [PATCH 24/31] Radix Sort --- .../build/model_10201_10_52_w1.txt | 10201 ---------------- Project2-Stream-Compaction/src/main.cpp | 22 +- .../stream_compaction/CMakeLists.txt | 2 + .../stream_compaction/efficient.cu | 44 + .../stream_compaction/efficient.h | 2 + .../stream_compaction/radix_sort.cu | 92 + .../stream_compaction/radix_sort.h | 9 + 7 files changed, 169 insertions(+), 10203 deletions(-) delete mode 100644 Project2-Character-Recognition/build/model_10201_10_52_w1.txt create mode 100644 Project2-Stream-Compaction/stream_compaction/radix_sort.cu create mode 100644 Project2-Stream-Compaction/stream_compaction/radix_sort.h diff --git a/Project2-Character-Recognition/build/model_10201_10_52_w1.txt b/Project2-Character-Recognition/build/model_10201_10_52_w1.txt deleted file mode 100644 index dcadc08..0000000 --- a/Project2-Character-Recognition/build/model_10201_10_52_w1.txt +++ /dev/null @@ -1,10201 +0,0 @@ --0.229452 -0.364996 -0.646251 0.213389 -0.248253 -0.0877231 0.521544 -0.900273 0.614604 0.336843 -0.616295 -0.958565 0.703495 0.693026 0.152451 0.211475 0.525007 0.137701 0.575865 -0.757177 -0.372257 0.0592687 0.261209 0.62904 -0.738631 -0.694115 0.500566 0.179052 0.725022 -0.322635 0.963817 0.51863 0.0960799 0.132922 -0.160518 -0.552958 0.667045 0.846905 0.997532 -0.916086 --0.705713 -0.43909 0.940941 -0.439786 0.964278 -0.850347 0.209875 0.43912 0.762152 0.168141 0.851124 -0.332147 0.201791 -0.624614 0.423897 -0.63787 -0.851882 -0.848481 0.781064 0.15341 --0.416923 -0.615824 -0.606729 0.643597 0.85477 0.329608 -0.327141 -0.303354 0.459426 0.269807 0.0473847 -0.687713 -0.835209 -0.652455 0.662078 -0.743593 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-0.810888 0.809389 -0.608741 0.10085 0.359994 0.468917 0.593289 0.73956 -0.2249 -0.101556 -0.0299416 -0.790508 0.556744 -0.719782 -0.0400869 0.232313 -0.942927 0.675714 -0.260797 0.763547 0.828507 -0.591175 0.536522 0.330599 -0.392257 -0.00308883 -0.593001 0.0039717 0.901963 0.742779 -0.868697 -0.294663 0.874352 -0.883461 -0.163178 0.132767 --0.285322 -0.579631 0.496252 0.732992 0.0364118 -0.640478 0.00829792 -0.236482 -0.0190157 -0.603243 -0.194229 -0.25333 -0.857035 0.866712 -0.512845 0.881541 -0.355835 0.948282 0.926304 -0.27556 -0.345696 -0.749908 0.549963 0.419255 0.806985 0.165989 0.526159 -0.247391 0.625203 0.923638 0.573406 -0.778109 -0.772762 -0.917375 0.577315 -0.557638 0.470975 -0.110538 -0.332168 0.36675 --0.621516 -0.0467575 0.814314 -0.5219 0.917927 0.465011 -0.298929 0.707119 -0.322202 0.0260571 0.930458 0.986646 -0.855228 -0.889145 -0.0563436 -0.420007 0.152226 0.793849 -0.0490914 -0.457477 -0.990743 0.760729 -0.248807 0.496585 -0.801518 -0.964954 0.715105 -0.609798 0.979819 -0.435581 0.317902 -0.494092 -0.835475 -0.296315 0.866771 0.813915 0.294551 -0.409459 0.143603 0.433711 --0.451898 0.412204 -0.786465 0.0910406 -0.394418 -0.472787 -0.421241 -0.271355 -0.625781 0.305124 0.370243 -0.713692 0.554681 -0.224067 -0.0489469 -0.605028 -0.946792 -0.516823 0.651532 -0.72515 --0.340349 -0.729858 0.166353 -0.457792 0.329109 -0.689177 0.226945 0.375558 -0.953725 -0.37019 -0.611782 -0.956854 -0.712535 -0.105104 0.623002 -0.570762 -0.931771 -0.221612 -0.66446 0.739978 -0.639124 -0.666585 -0.942441 -0.0549055 0.394162 -0.416791 0.941141 -0.487738 0.54047 0.589029 -0.985155 -0.349066 0.173691 0.0190217 -0.118981 0.448406 0.337801 0.701748 -0.790805 0.0497486 --0.497152 0.156152 0.487518 0.831301 -0.499978 -0.785215 -0.259718 0.0730497 -0.905847 0.698556 0.330026 -0.574021 0.00692373 -0.878877 -0.753575 0.739983 -0.0797668 0.771019 0.159358 -0.0938975 --0.385533 0.737496 0.69214 -0.482467 0.658432 0.808653 -0.503967 0.662292 0.939372 0.464465 -0.650299 -0.523104 0.555958 0.0800304 -0.0624301 0.300146 0.855307 0.106618 -0.141946 0.231831 --0.730814 -0.877003 0.237314 0.172554 -0.116345 0.731927 -0.0374735 0.133581 0.153398 0.681905 0.631999 0.551957 -0.158233 0.996856 -0.242109 -0.375175 0.72925 -0.080112 -0.735933 -0.0971087 -0.670307 -0.525533 -0.45049 0.0766127 -0.821172 0.502545 -0.0895947 0.328011 0.0887181 -0.777517 0.860359 -0.201553 0.763151 -0.00548822 -0.0506893 0.527687 0.869655 0.0381647 -0.870115 0.857195 --0.162195 0.831087 -0.202826 -0.340616 -0.0398197 0.0259697 -0.268199 0.862649 -0.600195 0.623588 -0.188505 -0.333481 0.885435 -0.116477 0.0823717 0.535404 -0.520065 0.349751 0.441381 -0.470549 -0.856777 -0.932116 0.696243 -0.133612 -0.455721 -0.00550544 -0.545596 -0.596963 0.211852 0.762509 0.566687 -0.596623 0.502911 -0.472343 0.0573777 -0.737517 -0.890849 0.591128 -0.216639 -0.461315 -0.818104 -0.96392 0.13672 0.76088 0.503701 -0.456326 -0.349254 0.522514 0.723324 0.247546 -0.911112 0.124682 0.834723 0.28553 0.890377 -0.39509 -0.980239 -0.443747 -0.890466 0.681676 --0.488397 -0.22218 0.516987 -0.650709 0.20483 0.385092 0.817414 0.220905 0.628113 -0.608043 -0.863671 -0.203757 0.6543 -0.410877 -0.342636 -0.74498 -0.641931 0.664182 -0.918601 0.661623 --0.707095 0.6287 -0.781019 0.705771 0.965676 -0.515587 -0.948946 0.122381 0.0823184 -0.00135463 0.258185 0.620482 0.243995 -0.198283 0.25544 0.524282 0.00439739 -0.108862 0.772279 -0.469044 --0.836448 -0.915662 -0.539536 0.408626 0.699964 0.16627 0.584021 0.16517 0.637479 0.296253 -0.291211 0.143126 0.660436 0.0242358 -0.0856378 0.349572 -0.490266 -0.252223 0.198249 -0.75019 -0.478917 -0.539308 -0.573272 -0.998463 -0.145765 -0.968396 0.183306 -0.347706 -0.526396 -0.639701 0.968396 -0.521778 0.252288 -0.789594 -0.739883 0.695646 0.200046 -0.625332 -0.53928 0.64866 --0.501309 0.61302 0.477914 -0.877295 0.284717 -0.0526652 0.022982 0.521522 -0.127705 0.228283 -0.0918385 0.215351 0.365931 0.302106 0.00417149 -0.235794 -0.6618 0.571615 0.616408 -0.585398 --0.363586 -0.981257 -0.0778707 -0.755121 -0.627828 -0.609199 0.622473 0.497464 -0.0600628 -0.139061 0.992834 0.427643 -0.597386 0.252644 0.482988 0.636355 -0.0881656 -0.226976 0.322348 -0.316012 --0.518042 -0.982341 -0.327658 0.0337571 0.384821 0.684341 0.0449537 -0.894243 0.161844 -0.305448 0.60618 0.209317 -0.106661 0.572261 0.502295 -0.820481 0.32374 0.554076 -0.00042218 -0.814596 --0.751037 -0.441389 0.092284 0.931738 -0.470823 -0.247625 -0.167768 -0.458178 -0.618352 0.36833 -0.775825 -0.259285 -0.679029 -0.982005 0.528173 0.739674 0.966576 -0.175525 -0.326449 -0.322932 --0.040466 0.445832 -0.189047 -0.130574 0.627662 0.690133 -0.616011 0.647186 -0.111372 -0.27253 -0.634269 0.714974 0.134336 -0.904974 0.846488 0.459024 0.791507 0.936732 -0.833826 -0.465539 --0.128202 0.63494 -0.522697 0.960805 -0.460969 0.317509 0.500462 0.186618 0.892791 0.298307 -0.336742 0.381064 0.542873 0.876994 -0.66842 -0.597698 -0.240424 0.532223 -0.0406184 -0.36092 --0.290187 -0.179701 0.361184 0.0075289 0.0646527 0.477855 -0.967078 -0.147456 -0.513865 0.22394 0.720295 0.544571 -0.31921 0.71527 -0.82035 0.832835 -0.284221 -0.0795393 -0.885292 -0.49356 -0.978543 0.294474 0.330602 -0.932982 -0.218597 0.476076 0.927287 -0.676241 0.834329 -0.730347 -0.857177 0.881927 -0.0605694 -0.608364 0.502539 -0.939398 0.550856 -0.55857 0.337346 -0.428588 --0.0324255 -0.424444 0.772018 0.562853 0.0529227 0.833543 -0.369327 0.976935 -0.0892599 -0.39418 -0.284194 -0.00274903 0.940486 0.991329 0.214991 -0.529567 0.6835 -0.367173 0.96726 0.221 --0.604298 -0.776342 -0.0229023 -0.341607 0.371778 -0.199573 0.377056 -0.310549 0.837591 -0.96001 -0.115731 0.332573 0.599646 -0.108381 0.907478 0.267458 -0.275863 0.589796 -0.602743 -0.986293 -0.306382 0.906218 -0.886672 0.0247267 0.0857977 -0.579882 -0.223577 -0.935488 0.574429 -0.547774 -0.72152 0.459672 0.0392568 -0.857163 0.0748152 0.374717 -0.230454 0.0192354 -0.266189 -0.102349 -0.189197 0.33944 -0.972014 0.286723 0.759889 0.937766 0.456066 -0.373759 0.462981 0.359161 0.658264 0.646531 -0.298844 0.930777 0.392756 0.545901 -0.374381 0.589201 -0.748563 -0.759195 --0.188372 0.136406 -0.938159 -0.776767 0.211258 -0.0176702 -0.870498 0.923788 -0.235343 0.344656 0.745031 0.046522 -0.553653 0.126379 0.65139 0.00536513 0.224878 -0.914871 0.0847158 0.15894 diff --git a/Project2-Stream-Compaction/src/main.cpp b/Project2-Stream-Compaction/src/main.cpp index 207d264..131c0c9 100644 --- a/Project2-Stream-Compaction/src/main.cpp +++ b/Project2-Stream-Compaction/src/main.cpp @@ -11,8 +11,9 @@ #include #include #include +#include #include "testing_helpers.hpp" - +#include void scanTests(const int SIZE, const int NPOT, int *a, int *b , int *c, int blockSize) { genArray(SIZE - 1, a, 50); // Leave a 0 at the end to test that edge case a[SIZE - 1] = 0; @@ -205,9 +206,26 @@ int *a = new int[SIZE]; int *b = new int[SIZE]; int *c = new int[SIZE]; +void radixSortTest() { + genArray(SIZE - 1, a, 50); // Leave a 0 at the end to test that edge case + a[SIZE - 1] = 0; + printArray(SIZE, a, true); + zeroArray(SIZE, b); + StreamCompaction::RadixSort::sort(SIZE, b, a, 128); + printArray(SIZE, b, true); + std::sort(a, a + SIZE); + printCmpResult(SIZE, b, a); +} + int main(int argc, char* argv[]) { - // Scan tests + // Radix Sort Test + printf("\n"); + printf("*********************\n"); + printf("** RADIX SORT TEST **\n"); + printf("*********************\n"); + radixSortTest(); + // Scan tests printf("\n"); printf("****************\n"); printf("** SCAN TESTS **\n"); diff --git a/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt b/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt index 4bb0dc2..c682a08 100644 --- a/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt +++ b/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt @@ -9,6 +9,8 @@ set(SOURCE_FILES "efficient.cu" "thrust.h" "thrust.cu" + "radix_sort.h" + "radix_sort.cu" ) cuda_add_library(stream_compaction diff --git a/Project2-Stream-Compaction/stream_compaction/efficient.cu b/Project2-Stream-Compaction/stream_compaction/efficient.cu index 1e1da0e..435a3f7 100644 --- a/Project2-Stream-Compaction/stream_compaction/efficient.cu +++ b/Project2-Stream-Compaction/stream_compaction/efficient.cu @@ -123,6 +123,50 @@ namespace StreamCompaction { cudaFree(cdata); } + /** + * Performs prefix-sum (aka scan) on idata, storing the result into odata. + */ + void scanEfficientCUDA(int n, int *odata, const int *idata, int blockSize) { + // Memory Allocation and Copying + int power_size = pow(2, ilog2ceil(n)); + int *cdata; + cudaMalloc((void**)&cdata, power_size * sizeof(int)); + checkCUDAErrorFn("cudaMalloc adata failed!"); + cudaMemset(cdata, 0, power_size * sizeof(int)); + cudaMemcpy(cdata, idata, n * sizeof(int), cudaMemcpyDeviceToDevice); + + bool started_timer = true; + try { + timer().startGpuTimer(); + } + catch (const std::exception& e) { + started_timer = false; + } + + int numThreads; + //Up Sweep + for (int d = 0; d <= ilog2ceil(power_size) - 1; d++) { + numThreads = pow(2, (ilog2ceil(power_size) - 1 - d)); + dim3 fullBlocks((numThreads + blockSize - 1) / blockSize); + kernelUpSweepStepEfficient << > > (numThreads, d, cdata); + } + + //Down Sweep + cudaMemset(cdata + power_size - 1, 0, sizeof(int)); + for (int d = ilog2(power_size) - 1; d >= 0; d--) { + numThreads = pow(2, (ilog2ceil(power_size) - 1 - d)); + dim3 fullBlocks((numThreads + blockSize - 1) / blockSize); + kernelDownSweepStepEfficient << > > (numThreads, d, cdata); + } + + if (started_timer) + timer().endGpuTimer(); + + // Copy Back and Free Memory + cudaMemcpy(odata, cdata, sizeof(int) * n, cudaMemcpyDeviceToDevice); + cudaFree(cdata); + } + /** * Performs prefix-sum (aka scan) on idata, storing the result into odata. */ diff --git a/Project2-Stream-Compaction/stream_compaction/efficient.h b/Project2-Stream-Compaction/stream_compaction/efficient.h index efcc9d4..c7b2a02 100644 --- a/Project2-Stream-Compaction/stream_compaction/efficient.h +++ b/Project2-Stream-Compaction/stream_compaction/efficient.h @@ -8,6 +8,8 @@ namespace StreamCompaction { void scanEfficient(int n, int *odata, const int *idata, int blockSize = 128); + void scanEfficientCUDA(int n, int *odata, const int *idata, int blockSize = 128); + void scan(int n, int *odata, const int *idata, int blockSize = 128); int compact(int n, int *odata, const int *idata, bool efficient = true, int blockSize = 128); diff --git a/Project2-Stream-Compaction/stream_compaction/radix_sort.cu b/Project2-Stream-Compaction/stream_compaction/radix_sort.cu new file mode 100644 index 0000000..b1a2a3c --- /dev/null +++ b/Project2-Stream-Compaction/stream_compaction/radix_sort.cu @@ -0,0 +1,92 @@ +#include +#include +#include "common.h" +#include "radix_sort.h" +#include "device_launch_parameters.h" +#include +#include + +__global__ void Compute_e(int n, int bit, int *e_array, const int *i_array) { + int idx = (blockIdx.x * blockDim.x) + threadIdx.x; + if (idx < n) { + int b = (i_array[idx] >> bit) & 1; + e_array[idx] = (b == 0 ? 1 : 0); + } +} + +__global__ void Compute_t(int n, int *totalFalses, int *t_array, const int *f_array) { + int idx = (blockIdx.x * blockDim.x) + threadIdx.x; + if (idx < n) { + t_array[idx] = idx - f_array[idx] + *totalFalses; + } +} + +__global__ void Compute_d(int n, int *d_array, const int *e_array, const int *f_array, const int *t_array) { + int idx = (blockIdx.x * blockDim.x) + threadIdx.x; + if (idx < n) { + d_array[idx] = e_array[idx] ? f_array[idx] : t_array[idx]; + } +} + +__global__ void Scatter(int n, int *o_array, const int *d_array, const int *i_array) { + int idx = (blockIdx.x * blockDim.x) + threadIdx.x; + if (idx < n) { + o_array[d_array[idx]] = i_array[idx]; + } +} + +__global__ void Add(int *arr1, int *arr2, int idx, int *totalFalses) { + *totalFalses = arr1[idx] + arr2[idx]; +} + +namespace StreamCompaction { + namespace RadixSort { + void sort(int n, int *odata, const int *idata, int blockSize) { + int *i_array, *e_array, *f_array, *t_array, *d_array, *o_array, *totalFalses; + // Memory Allocation + cudaMalloc((void**)&i_array, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc i_array failed!"); + + cudaMalloc((void**)&e_array, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc e_array failed!"); + + cudaMalloc((void**)&f_array, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc f_array failed!"); + + cudaMalloc((void**)&t_array, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc t_array failed!"); + + cudaMalloc((void**)&d_array, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc d_array failed!"); + + cudaMalloc((void**)&o_array, n * sizeof(int)); + checkCUDAErrorFn("cudaMalloc o_array failed!"); + + cudaMalloc((void**)&totalFalses, sizeof(int)); + checkCUDAErrorFn("cudaMalloc totalFalses failed!"); + + cudaMemcpy(i_array, idata, sizeof(int) * n, cudaMemcpyHostToDevice); + + dim3 fullBlocks((n + blockSize - 1) / blockSize); + for (int i = 0; i < 10; i++) { + Compute_e << < fullBlocks, blockSize >> > (n, i, e_array, i_array); + StreamCompaction::Efficient::scanEfficientCUDA(n, f_array, e_array); + Add << <1, 1 >> > (e_array, f_array, n - 1, totalFalses); + Compute_t << < fullBlocks, blockSize >> > (n, totalFalses, t_array, f_array); + Compute_d << < fullBlocks, blockSize >> > (n, d_array, e_array, f_array, t_array); + Scatter << < fullBlocks, blockSize >> > (n, o_array, d_array, i_array); + cudaMemcpy(i_array, o_array, sizeof(int) * n, cudaMemcpyDeviceToDevice); + } + cudaMemcpy(odata, o_array, sizeof(int) * n, cudaMemcpyDeviceToHost); + + // Freeing the Memory + cudaFree(i_array); + cudaFree(e_array); + cudaFree(f_array); + cudaFree(t_array); + cudaFree(d_array); + cudaFree(o_array); + } + + } +} \ No newline at end of file diff --git a/Project2-Stream-Compaction/stream_compaction/radix_sort.h b/Project2-Stream-Compaction/stream_compaction/radix_sort.h new file mode 100644 index 0000000..eb3fdbb --- /dev/null +++ b/Project2-Stream-Compaction/stream_compaction/radix_sort.h @@ -0,0 +1,9 @@ +#pragma once + +#include "common.h" + +namespace StreamCompaction { + namespace RadixSort { + void sort(int n, int *odata, const int *idata, int blockSize); + } +} From d486c93c9afd95d4592aff9eef6725a3b1b022de Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Tue, 17 Sep 2019 23:38:00 -0400 Subject: [PATCH 25/31] Refined Description --- Project2-Stream-Compaction/README.md | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/Project2-Stream-Compaction/README.md b/Project2-Stream-Compaction/README.md index 2de990a..d850e75 100644 --- a/Project2-Stream-Compaction/README.md +++ b/Project2-Stream-Compaction/README.md @@ -8,11 +8,20 @@ CUDA Stream Compaction ## Description Stream Compaction is a very widely used algorithm with path tracer as one of the applications. This project implements GPU stream compaction in CUDA and compares the following algorithms for stream compaction along with analyzing the scan module: -1. CPU -2. GPU: Naive -3. GPU: Work-Efficient -4. GPU: Optimized Work-Efficient -5. GPU: Thrust +* **CPU** + - Scan: This computes an exclusive prefix sum using a for loop. + - Compact without Scan: This does stream compaction without using the scan function. + - Compact With Scan: This does stream compaction by map the input to binaries, scanning the array using above discussed implementation and then scattering the input according to the scanned array. +* **GPU: Naive** + -- This naive implementation iterates throught the array logarithm of n times and each time it does one addition less than the previous step. So, thsi aproach has logarithm of n number of iterations and n* log n number of additions. +* **GPU: Work-Efficient** + -- This implementation also iterates logarithm of n times but the total number of additions performed in this approach are n. The number of additions in each iteration decrease by an order of 2. +* **GPU: Optimized Work-Efficient** + -- In the earlier approach, there are a lot of threads which are not doing any computation. This approach reduces the number of threads spawned and so, boosts the performance significantly. +* **GPU: Thrust** + -- This approach calls thrust library functions and is implemented for the comparison purpose. + +It also implements Radix Sort using the optimized work-efficient GPU approach. ## Performance Analysis ### Array Size From 938025703c81a257b31b8525d11f66e18e4a8649 Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Tue, 17 Sep 2019 23:55:07 -0400 Subject: [PATCH 26/31] Updated README --- Project2-Stream-Compaction/README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/Project2-Stream-Compaction/README.md b/Project2-Stream-Compaction/README.md index d850e75..952c4bb 100644 --- a/Project2-Stream-Compaction/README.md +++ b/Project2-Stream-Compaction/README.md @@ -142,13 +142,14 @@ Following is the output of the main program for Stream Compaction showing the ar I analyzed the performance of Work Efficient GPU approach. The occupancy of the threads keeps on dropping by a factor of two with each iteration of up and down sweep. I implemented the optimized variant of Work Efficient Approach to make sure that all threads are occupied in each iteration. It makes sure that It only lanuches the required number of threads in each iteration rather than launchng the fixed number of threads in each iteartion. It improved the performance significantly in scan as well as stream compaction, it can be seen in the plots above. ### Radix Sort -I have implemented Radix Sort using the optimized version of Work Efficient GPU Scan. Code is present in the `` radix_sort.cu `` and ``radix_sort.h `` files under stream_compaction folder. I have added a corresponding test function ``void radixSortTest()`` in the main.cpp file to test the accuracy of the my implementation. This function is also called in the beginning of main function. Following is a sample ouput from the test. +I have implemented Radix Sort using the optimized version of Work Efficient GPU Scan. Code is present in the `` radix_sort.cu `` and ``radix_sort.h `` files under stream_compaction folder. I have added a corresponding test function ``void radixSortTest()`` in the main.cpp file to test the accuracy of the my implementation. This function is also called in the beginning of main function. Following is a sample ouput from the test for n = 2 raise to power 20. ``` ********************* ** RADIX SORT TEST ** ********************* - [ 46 7 12 24 12 24 30 0 ] - [ 0 7 12 12 24 24 30 46 ] + [ 21 33 35 49 49 24 5 13 33 40 28 34 6 ... 24 0 ] + elapsed time: 51.3554ms + [ 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 49 49 ] passed ``` From 3fd3559f197b14d91b622d9dbca92b164eccc340 Mon Sep 17 00:00:00 2001 From: DISHA JINDAL Date: Tue, 17 Sep 2019 23:57:05 -0400 Subject: [PATCH 27/31] added timer --- Project2-Stream-Compaction/src/main.cpp | 2 ++ .../stream_compaction/radix_sort.cu | 9 +++++++++ .../stream_compaction/radix_sort.h | 2 ++ 3 files changed, 13 insertions(+) diff --git a/Project2-Stream-Compaction/src/main.cpp b/Project2-Stream-Compaction/src/main.cpp index 131c0c9..0b7ef4c 100644 --- a/Project2-Stream-Compaction/src/main.cpp +++ b/Project2-Stream-Compaction/src/main.cpp @@ -212,8 +212,10 @@ void radixSortTest() { printArray(SIZE, a, true); zeroArray(SIZE, b); StreamCompaction::RadixSort::sort(SIZE, b, a, 128); + printElapsedTime(StreamCompaction::RadixSort::timer().getGpuElapsedTimeForPreviousOperation()); printArray(SIZE, b, true); std::sort(a, a + SIZE); + //printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation()); printCmpResult(SIZE, b, a); } diff --git a/Project2-Stream-Compaction/stream_compaction/radix_sort.cu b/Project2-Stream-Compaction/stream_compaction/radix_sort.cu index b1a2a3c..c276085 100644 --- a/Project2-Stream-Compaction/stream_compaction/radix_sort.cu +++ b/Project2-Stream-Compaction/stream_compaction/radix_sort.cu @@ -41,7 +41,15 @@ __global__ void Add(int *arr1, int *arr2, int idx, int *totalFalses) { namespace StreamCompaction { namespace RadixSort { + using StreamCompaction::Common::PerformanceTimer; + StreamCompaction::Common::PerformanceTimer & timer() + { + static PerformanceTimer timer; + return timer; + } + void sort(int n, int *odata, const int *idata, int blockSize) { + timer().startGpuTimer(); int *i_array, *e_array, *f_array, *t_array, *d_array, *o_array, *totalFalses; // Memory Allocation cudaMalloc((void**)&i_array, n * sizeof(int)); @@ -86,6 +94,7 @@ namespace StreamCompaction { cudaFree(t_array); cudaFree(d_array); cudaFree(o_array); + timer().endGpuTimer(); } } diff --git a/Project2-Stream-Compaction/stream_compaction/radix_sort.h b/Project2-Stream-Compaction/stream_compaction/radix_sort.h index eb3fdbb..60ce6e1 100644 --- a/Project2-Stream-Compaction/stream_compaction/radix_sort.h +++ b/Project2-Stream-Compaction/stream_compaction/radix_sort.h @@ -4,6 +4,8 @@ namespace StreamCompaction { namespace RadixSort { + StreamCompaction::Common::PerformanceTimer& timer(); + void sort(int n, int *odata, const int *idata, int blockSize); } } From 5b3c0e158adb4c13189f5ee30450f3410a4f395d Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Tue, 17 Sep 2019 23:59:08 -0400 Subject: [PATCH 28/31] . to : --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cb74a15..a91de06 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ CUDA Number Algorithms * Disha Jindal: [Linkedin](https://www.linkedin.com/in/disha-jindal/) * Tested on: Windows 10 Education, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, GTX 222 222MB (Moore 100B Lab) -Following are the links to the subprojects. +Following are the links to the subprojects: [CUDA Stream Compaction](https://github.com/DishaJindal/Project2-Number-Algorithms/blob/master/Project2-Stream-Compaction/README.md) From 02c83609193e64485a38b68b2cf3c0b2f063f76a Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Fri, 27 Sep 2019 04:14:10 -0400 Subject: [PATCH 29/31] Readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a91de06..45136cb 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ CUDA Number Algorithms **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2** * Disha Jindal: [Linkedin](https://www.linkedin.com/in/disha-jindal/) -* Tested on: Windows 10 Education, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, GTX 222 222MB (Moore 100B Lab) +* Tested on: Windows 10 Education, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, NVIDIA Quadro P1000 @ 4GB (Moore 100B Lab) Following are the links to the subprojects: From 6dc3c029e29ec2b4c98b06b38fa72874fff6a125 Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Fri, 27 Sep 2019 04:15:06 -0400 Subject: [PATCH 30/31] readme --- Project2-Stream-Compaction/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Project2-Stream-Compaction/README.md b/Project2-Stream-Compaction/README.md index 952c4bb..ff0738e 100644 --- a/Project2-Stream-Compaction/README.md +++ b/Project2-Stream-Compaction/README.md @@ -4,7 +4,7 @@ CUDA Stream Compaction **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2** * Disha Jindal: [Linkedin](https://www.linkedin.com/in/disha-jindal/) -* Tested on: Windows 10 Education, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, GTX 222 222MB (Moore 100B Lab) +* Tested on: Windows 10 Education, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, NVIDIA Quadro P1000 @ 4GB (Moore 100B Lab) ## Description Stream Compaction is a very widely used algorithm with path tracer as one of the applications. This project implements GPU stream compaction in CUDA and compares the following algorithms for stream compaction along with analyzing the scan module: From 807497c7d1cc8896d24e72d7c8839e854f60d0b7 Mon Sep 17 00:00:00 2001 From: Disha Jindal Date: Fri, 27 Sep 2019 04:15:33 -0400 Subject: [PATCH 31/31] readme --- Project2-Character-Recognition/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Project2-Character-Recognition/README.md b/Project2-Character-Recognition/README.md index 362ea36..ad6bdc1 100644 --- a/Project2-Character-Recognition/README.md +++ b/Project2-Character-Recognition/README.md @@ -4,7 +4,7 @@ CUDA Character Recognition **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2** * Disha Jindal: [Linkedin](https://www.linkedin.com/in/disha-jindal/) -* Tested on: Windows 10 Education, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, GTX 222 222MB (Moore 100B Lab) +* Tested on: Windows 10 Education, Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, NVIDIA Quadro P1000 @ 4GB (Moore 100B Lab) ## Description This code implements a simple neural network (Multi-layer Perceptron) in CUDA. It is tested on XOR and Character Recognition dataset. Following architechure depicts a neural network with an input layer, hidden layer and output layer.

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