|
| 1 | +#!/usr/bin/env python |
| 2 | + |
| 3 | +""" |
| 4 | +OCCRI Performance Demonstration |
| 5 | +
|
| 6 | +This example demonstrates OCCRI's performance characteristics and shows |
| 7 | +how to benchmark it against standard FFTDF. OCCRI provides significant |
| 8 | +speedup while maintaining chemical accuracy. |
| 9 | +
|
| 10 | +Key topics covered: |
| 11 | +- Timing OCCRI vs FFTDF calculations |
| 12 | +- Performance scaling considerations |
| 13 | +- When OCCRI provides the most benefit |
| 14 | +- How to optimize OCCRI performance |
| 15 | +""" |
| 16 | + |
| 17 | +import time |
| 18 | + |
| 19 | +import numpy |
| 20 | + |
| 21 | +from pyscf.occri import OCCRI |
| 22 | +from pyscf.pbc import df, gto, scf |
| 23 | + |
| 24 | +# Set up a moderately sized system for performance comparison |
| 25 | +cell = gto.Cell() |
| 26 | +cell.atom = """ |
| 27 | + C 0.000000 0.000000 1.780373 |
| 28 | + C 0.890186 0.890186 2.670559 |
| 29 | + C 0.000000 1.780373 0.000000 |
| 30 | + C 0.890186 2.670559 0.890186 |
| 31 | +""" |
| 32 | +cell.basis = "gth-cc-tzvp" |
| 33 | +cell.pseudo = "gth-pbe" |
| 34 | +cell.a = numpy.array( |
| 35 | + [ |
| 36 | + [3.560745, 0.000000, 0.000000], |
| 37 | + [0.000000, 3.560745, 0.000000], |
| 38 | + [0.000000, 0.000000, 3.560745], |
| 39 | + ] |
| 40 | +) |
| 41 | +cell.mesh = [25] * 3 |
| 42 | +cell.verbose = 0 |
| 43 | +cell.build() |
| 44 | +kmesh = [1,1,1] |
| 45 | +kpts = cell.make_kpts(kmesh) |
| 46 | + |
| 47 | +print("=== OCCRI Performance Comparison ===") |
| 48 | +print( |
| 49 | + f"System: {' '.join(cell.atom_symbol(i) for i in range(cell.natm))} ({cell.natm} atoms, {cell.nao} AOs)" |
| 50 | +) |
| 51 | +print(f"Basis: {cell.basis}") |
| 52 | +print(f"Mesh: {cell.mesh}") |
| 53 | + |
| 54 | +# Example 1: Compare K matrix construction: FFTDF vs OCCRI |
| 55 | +print("\n1. K matrix construction timing comparison") |
| 56 | + |
| 57 | +# Set up common density matrix for fair comparison |
| 58 | +print(" Setting up test density matrix...") |
| 59 | +mf_ref = scf.KRHF(cell, kpts=kpts) |
| 60 | +mf_ref.max_cycle = 1 # Store MO Coeff for comparison |
| 61 | +mf_ref.kernel() |
| 62 | +dm = mf_ref.make_rdm1(kpts=kpts) |
| 63 | + |
| 64 | +# Time FFTDF K matrix construction only |
| 65 | +print(" Timing FFTDF K matrix construction...") |
| 66 | +mf_ref = scf.KRHF(cell, kpts=kpts) |
| 67 | +mf_ref._is_mem_enough = lambda: False # Turn off 'incore' for small demo |
| 68 | +start_time = time.time() |
| 69 | +_, vk_fftdf = mf_ref.get_jk(dm_kpts=dm, with_j=False, with_k=True, kpts=kpts) |
| 70 | +fftdf_k_time = time.time() - start_time |
| 71 | + |
| 72 | +# Time OCCRI K matrix construction only |
| 73 | +print(" Timing OCCRI K matrix construction...") |
| 74 | +mf_occri = scf.KRHF(cell, kpts=kpts) |
| 75 | +mf_occri.with_df = OCCRI(mf_occri, disable_c=True, kmesh=kmesh) |
| 76 | +mf_occri.with_df.scf_iter = 1 # Don't rebuild MOs for timing |
| 77 | + |
| 78 | +start_time = time.time() |
| 79 | +_, vk_occri = mf_occri.get_jk(dm=dm, with_j=False, with_k=True, kpts=kpts) |
| 80 | +occri_k_time = time.time() - start_time |
| 81 | + |
| 82 | +# Results |
| 83 | +k_energy_fftdf = numpy.einsum("kij,kji", vk_fftdf, dm) * 0.5 |
| 84 | +k_energy_occri = numpy.einsum("kij,kji", vk_occri, dm) * 0.5 |
| 85 | +energy_diff = abs(k_energy_fftdf - k_energy_occri) |
| 86 | +k_speedup = fftdf_k_time / occri_k_time |
| 87 | + |
| 88 | +print(f" FFTDF K matrix: {k_energy_fftdf:.8f} Ha ({fftdf_k_time:.3f}s)") |
| 89 | +print(f" OCCRI K matrix: {k_energy_occri:.8f} Ha ({occri_k_time:.3f}s)") |
| 90 | +print(f" Energy difference: {energy_diff:.2e} Hartree") |
| 91 | +print(f" K matrix speedup: {k_speedup:.2f}x") |
| 92 | + |
| 93 | +# Example 2: K matrix timing for multiple calls (realistic usage) |
| 94 | +print("\n2. Multiple K matrix evaluations (typical in SCF)") |
| 95 | + |
| 96 | +print(" Testing with 7 K matrix evaluations...") |
| 97 | +n_calls = 7 |
| 98 | + |
| 99 | +# Time multiple FFTDF K matrix calls |
| 100 | +print(" Timing FFTDF...") |
| 101 | +start_time = time.time() |
| 102 | +for i in range(n_calls): |
| 103 | + _, vk_fftdf = mf_ref.get_jk(dm_kpts=dm, with_j=False, with_k=True, kpts=kpts) |
| 104 | +fftdf_multi_time = time.time() - start_time |
| 105 | + |
| 106 | +# Time multiple OCCRI K matrix calls |
| 107 | +print(" Timing OCCRI...") |
| 108 | +start_time = time.time() |
| 109 | +for i in range(n_calls): |
| 110 | + _, vk_occri = mf_occri.get_jk(dm=dm, with_j=False, with_k=True, kpts=kpts) |
| 111 | +occri_multi_time = time.time() - start_time |
| 112 | + |
| 113 | +multi_speedup = fftdf_multi_time / occri_multi_time |
| 114 | + |
| 115 | +print( |
| 116 | + f" FFTDF: {n_calls} calls in {fftdf_multi_time:.3f}s ({fftdf_multi_time/n_calls:.3f}s per call)" |
| 117 | +) |
| 118 | +print( |
| 119 | + f" OCCRI: {n_calls} calls in {occri_multi_time:.3f}s ({occri_multi_time/n_calls:.3f}s per call)" |
| 120 | +) |
| 121 | +print(f" Average K speedup: {multi_speedup:.2f}x") |
| 122 | + |
| 123 | + |
| 124 | +print("\n=== Performance Summary ===") |
| 125 | +print(f"• K matrix construction speedup: {k_speedup:.1f}x (single call)") |
| 126 | +print(f"• K matrix construction speedup: {multi_speedup:.1f}x (multiple calls)") |
| 127 | +print(f"• Exchange energy accuracy: ~{energy_diff:.0e} Hartree") |
| 128 | + |
| 129 | +print("\n=== Optimization Notes ===") |
| 130 | +try: |
| 131 | + from pyscf.occri import _OCCRI_C_AVAILABLE |
| 132 | + |
| 133 | + if _OCCRI_C_AVAILABLE: |
| 134 | + print("✓ Using optimized C extension with FFTW and OpenMP") |
| 135 | + print(" - Compiled C code provides ~5-10x base speedup") |
| 136 | + print(" - FFTW optimized FFTs for best performance") |
| 137 | + print(" - OpenMP parallelization scales with CPU cores") |
| 138 | + else: |
| 139 | + print("⚠ Using Python fallback implementation") |
| 140 | + print(" - Install FFTW, BLAS, and OpenMP for optimal performance") |
| 141 | + print(" - C extension provides significant additional speedup") |
| 142 | +except ImportError: |
| 143 | + print("⚠ OCCRI module information not available") |
| 144 | + |
| 145 | +print("\n=== Benchmarking Tips ===") |
| 146 | +print("To properly benchmark OCCRI:") |
| 147 | +print("• Run multiple trials and average timings for statistical significance") |
| 148 | +print("• Use representative system sizes (OCCRI benefits scale with system size)") |
| 149 | +print("• Test both C extension and Python implementations") |
| 150 | +print("• Consider memory usage in addition to timing") |
| 151 | +print("• Verify energy accuracy remains within acceptable thresholds") |
| 152 | + |
| 153 | +print("\n=== When to Use OCCRI ===") |
| 154 | +print("OCCRI provides most benefit for:") |
| 155 | +print("• Large basis sets: cc-pVTZ, aug-cc-pVDZ, gth-cc-tzvp") |
| 156 | +print("• Systems where N_AO >> N_occ (wide band gap insulators)") |
| 157 | +print("• Hybrid DFT calculations requiring exact exchange") |
| 158 | +print("• k-point calculations (see 02-kpoint_calculations.py)") |
| 159 | +print("• Production calculations where FFTDF becomes a bottleneck") |
| 160 | +print("") |
| 161 | +print("OCCRI may be slower for:") |
| 162 | +print("• Small basis sets: STO-3G, 6-31G, gth-szv") |
| 163 | +print("• Metallic systems where N_occ ≈ N_AO/2") |
| 164 | +print("• Quick test calculations with minimal basis sets") |
| 165 | + |
| 166 | +print("\n=== Critical Performance Scaling Insight ===") |
| 167 | +print("K matrix construction complexity (the bottleneck OCCRI optimizes):") |
| 168 | +print("• FFTDF K matrix: O(N_k² × N_AO² × N_grid × log(N_grid))") |
| 169 | +print("• OCCRI K matrix: O(N_k² × N_occ² × N_grid × log(N_grid))") |
| 170 | +print( |
| 171 | + f"• Theoretical K matrix speedup: N_AO²/N_occ² = {cell.nao**2/(cell.nelectron//2)**2:.1f}x" |
| 172 | +) |
| 173 | +print("") |
| 174 | + |
| 175 | +print(f"\nCurrent system ({cell.basis}):") |
| 176 | +print(f"• {cell.nao} AOs, {cell.nelectron//2} occupied orbitals") |
| 177 | +print(f"• Theoretical K speedup limit: {cell.nao**2/(cell.nelectron//2)**2:.1f}x") |
| 178 | +print("• Practical K speedup: typically achieves 10-30% of limit") |
| 179 | + |
| 180 | +print("\n=== Additional Scaling Factors ===") |
| 181 | +print("• k-point calculations: O(N_k²) scaling favors OCCRI even more") |
| 182 | +print("• C extension: provides additional ~5-10x speedup") |
| 183 | +print("• Memory: OCCRI scales as O(N_occ) vs FFTDF O(N_AO)") |
| 184 | + |
| 185 | +print( |
| 186 | + "\nExample completed! Try different basis sets (gth-szv, gth-dzvp, gth-cc-tzvp) to see scaling." |
| 187 | +) |
0 commit comments