Project2 Finished Jiawei Wang

README.md

@@ -1,82 +1,7 @@
 Project-2
 =========
 
-A Study in Parallel Algorithms : Stream Compaction
 
-# INTRODUCTION
-Many of the algorithms you have learned thus far in your career have typically
-been developed from a serial standpoint.  When it comes to GPUs, we are mainly
-looking at massively parallel work.  Thus, it is necessary to reorient our
-thinking.  In this project, we will be implementing a couple different versions
-of prefix sum.  We will start with a simple single thread serial CPU version,
-and then move to a naive GPU version.  Each part of this homework is meant to
-follow the logic of the previous parts, so please do not do this homework out of
-order.
-
-This project will serve as a stream compaction library that you may use (and
-will want to use) in your
-future projects.  For that reason, we suggest you create proper header and CUDA
-files so that you can reuse this code later.  You may want to create a separate
-cpp file that contains your main function so that you can test the code you
-write.
-
-# OVERVIEW
-Stream compaction is broken down into two parts: (1) scan, and (2) scatter.
-
-## SCAN
-Scan or prefix sum is the summation of the elements in an array such that the
-resulting array is the summation of the terms before it.  Prefix sum can either
-be inclusive, meaning the current term is a summation of all the elements before
-it and itself, or exclusive, meaning the current term is a summation of all
-elements before it excluding itself. 
-
-Inclusive:
-
-In : [ 3 4 6 7 9 10 ]
-
-Out : [ 3 7 13 20 29 39 ]
-
-Exclusive
-
-In : [ 3 4 6 7 9 10 ]
-
-Out : [ 0 3 7 13 20 29 ]
-
-Note that the resulting prefix sum will always be n + 1 elements if the input
-array is of length n.  Similarly, the first element of the exclusive prefix sum
-will always be 0.  In the following sections, all references to prefix sum will
-be to the exclusive version of prefix sum.
-
-## SCATTER
-The scatter section of stream compaction takes the results of the previous scan
-in order to reorder the elements to form a compact array.
-
-For example, let's say we have the following array:
-[ 0 0 3 4 0 6 6 7 0 1 ]
-
-We would only like to consider the non-zero elements in this zero, so we would
-like to compact it into the following array:
-[ 3 4 6 6 7 1 ]
-
-We can perform a transform on input array to transform it into a boolean array:
-
-In :  [ 0 0 3 4 0 6 6 7 0 1 ]
-
-Out : [ 0 0 1 1 0 1 1 1 0 1 ]
-
-Performing a scan on the output, we get the following array :
-
-In :  [ 0 0 1 1 0 1 1 1 0 1 ]
-
-Out : [ 0 0 0 1 2 2 3 4 5 5 ]
-
-Notice that the output array produces a corresponding index array that we can
-use to create the resulting array for stream compaction. 
-
-# PART 1 : REVIEW OF PREFIX SUM
-Given the definition of exclusive prefix sum, please write a serial CPU version
-of prefix sum.  You may write this in the cpp file to separate this from the
-CUDA code you will be writing in your .cu file. 
 
 # PART 2 : NAIVE PREFIX SUM
 We will now parallelize this the previous section's code.  Recall from lecture
@@ -89,45 +14,22 @@ you are NOT allowed to use shared memory.
 * Plot a graph of the comparison and write a short explanation of the phenomenon you
   see here.
 
-# PART 3 : OPTIMIZING PREFIX SUM
-In the previous section we did not take into account shared memory.  In the
-previous section, we kept everything in global memory, which is much slower than
-shared memory.
-
-## PART 3a : Write prefix sum for a single block
-Shared memory is accessible to threads of a block. Please write a version of
-prefix sum that works on a single block.  
+Solution: I just printed the data table for comparing the serial CPU prefix sum and GPU Naive version.
+From the data table we can see in the beginning when size is pretty small(less than 10^4), the CPU version cost very little time for the prefix sum. But GPU naive one cost much more. However, with the size growing to 10^6 or 10^7, the GPU naive one seems cost as same as the CPU version. We can predict the CPU prefix sum will cost more time when the size is big enough.
+It is because the CPU version use only one thread but GPU version use multiple threads. When size is big, CPU will be much slower.
 
-## PART 3b : Generalizing to arrays of any length.
-Taking the previous portion, please write a version that generalizes prefix sum
-to arbitrary length arrays, this includes arrays that will not fit on one block.
 
 ### Questions
 * Compare this version to the parallel prefix sum using global memory.
 * Plot a graph of the comparison and write a short explanation of the phenomenon
   you see here.
 
-# PART 4 : ADDING SCATTER
-First create a serial version of scatter by expanding the serial version of
-prefix sum.  Then create a GPU version of scatter.  Combine the function call
-such that, given an array, you can call stream compact and it will compact the
-array for you.  Finally, write a version using thrust. 
+Solution: The global memory is used by multiple blocks, but the shared memory is only used within the single block. The threads in the single block access the shared memory much faster than accessing the global memory. So the single block version is faster.
+
 
 ### Questions
 * Compare your version of stream compact to your version using thrust.  How do
   they compare?  How might you optimize yours more, or how might thrust's stream
   compact be optimized.
 
-# EXTRA CREDIT (+10)
-For extra credit, please optimize your prefix sum for work parallelism and to
-deal with bank conflicts.  Information on this can be found in the GPU Gems
-chapter listed in the references.  
-
-# SUBMISSION
-Please answer all the questions in each of the subsections above and write your
-answers in the README by overwriting the README file.  In future projects, we
-expect your analysis to be similar to the one we have led you through in this
-project.  Like other projects, please open a pull request and email Harmony.
-
-# REFERENCES
-"Parallel Prefix Sum (Scan) with CUDA." GPU Gems 3.
+Solution: There are some compile error in the thrust method I implemented. But I guess the given thrust method of stream compact will be faster.

StreamCompaction.sln

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+
+Microsoft Visual Studio Solution File, Format Version 11.00
+# Visual Studio 2010
+Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "StreamCompaction", "StreamCompaction\StreamCompaction.vcxproj", "{2CD2760F-4832-4806-8672-CA4B72058A00}"
+EndProject
+Global
+	GlobalSection(SolutionConfigurationPlatforms) = preSolution
+		Debug|Win32 = Debug|Win32
+		Release|Win32 = Release|Win32
+	EndGlobalSection
+	GlobalSection(ProjectConfigurationPlatforms) = postSolution
+		{2CD2760F-4832-4806-8672-CA4B72058A00}.Debug|Win32.ActiveCfg = Debug|Win32
+		{2CD2760F-4832-4806-8672-CA4B72058A00}.Debug|Win32.Build.0 = Debug|Win32
+		{2CD2760F-4832-4806-8672-CA4B72058A00}.Release|Win32.ActiveCfg = Release|Win32
+		{2CD2760F-4832-4806-8672-CA4B72058A00}.Release|Win32.Build.0 = Release|Win32
+	EndGlobalSection
+	GlobalSection(SolutionProperties) = preSolution
+		HideSolutionNode = FALSE
+	EndGlobalSection
+EndGlobal

StreamCompaction/Debug/StreamCompaction.Build.CppClean.log

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+C:\Users\JiaweiWang\Desktop\cs565\cs565Project-2\StreamCompaction\StreamCompaction\Debug\streamCompact.cu.cache
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+C:\Users\JiaweiWang\Desktop\cs565\cs565Project-2\StreamCompaction\StreamCompaction\Debug\prefixSum.cu.obj
+C:\Users\JiaweiWang\Desktop\cs565\cs565Project-2\StreamCompaction\StreamCompaction\Debug\project2_kernel.cu.obj
+C:\Users\JiaweiWang\Desktop\cs565\cs565Project-2\StreamCompaction\StreamCompaction\Debug\scatter.cu.obj
+C:\Users\JiaweiWang\Desktop\cs565\cs565Project-2\StreamCompaction\StreamCompaction\Debug\streamCompact.cu.obj
+C:\Users\JiaweiWang\Desktop\cs565\cs565Project-2\StreamCompaction\StreamCompaction\Debug\StreamCompaction.cu.obj
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+C:\Users\JiaweiWang\Desktop\cs565\cs565Project-2\StreamCompaction\StreamCompaction\Debug\CL.read.1.tlog
+C:\Users\JiaweiWang\Desktop\cs565\cs565Project-2\StreamCompaction\StreamCompaction\Debug\CL.write.1.tlog
+C:\Users\JiaweiWang\Desktop\cs565\cs565Project-2\StreamCompaction\StreamCompaction\Debug\vc100.idb

StreamCompaction/Debug/StreamCompaction.cu.cache

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