Task03 Denis Sokolov ITMO#1062
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Забавно, Github CI на наивной реализации matrix_multiply выдал больше GFlops, чем на реализации с local_memory |
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✅ Результаты тестирования PR #1062 Логи тестирования (нажмите чтобы развернуть)=== СТАТУС: Успешно выполнены программы: main_matrix_transpose, main_matrix_multiply === === main_matrix_transpose stdout (exit code: -11 (segfault после выполнения)) === Found 1 GPUs in 8.45184 sec (CUDA: 0.115671 sec, OpenCL: 0.841202 sec, Vulkan: 7.49491 sec) Available devices: Device #0: API: CUDA+OpenCL+Vulkan. GPU. Tesla T4 (CUDA 12020). Free memory: 14822/14930 Mb. Using device #0: API: CUDA+OpenCL+Vulkan. GPU. Tesla T4 (CUDA 12020). Free memory: 14822/14930 Mb. Using OpenCL API... Matrix size: rows=H=8192 x cols=W=16384 (512 MB) ______________________________________________________ Evaluating algorithm #1/2: 01 naive transpose (non-coalesced) Kernels compilation done in 2.79815 seconds algorithm times (in seconds) - 10 values (min=0.012165 10%=0.0121714 median=0.01218 90%=2.8104 max=2.8104) median effective algorithm bandwidth: 82.1019 GB/s ______________________________________________________ Evaluating algorithm #2/2: 02 transpose via local memory (coalesced) Kernels compilation done in 0.230498 seconds algorithm times (in seconds) - 10 values (min=0.00837265 10%=0.00837601 median=0.00838911 90%=0.238957 max=0.238957) median effective algorithm bandwidth: 119.202 GB/s === main_matrix_multiply stdout (exit code: -11 (segfault после выполнения)) === Found 1 GPUs in 0.327966 sec (CUDA: 0.127407 sec, OpenCL: 0.0381992 sec, Vulkan: 0.1623 sec) Available devices: Device #0: API: CUDA+OpenCL+Vulkan. GPU. Tesla T4 (CUDA 12020). Free memory: 14822/14930 Mb. Using device #0: API: CUDA+OpenCL+Vulkan. GPU. Tesla T4 (CUDA 12020). Free memory: 14822/14930 Mb. Using OpenCL API... C = A x B, matrices size: C (rows=H=2048 x cols=W=4096) = A (rows=H=2048 x cols=K=1024) x B (rows=K=1024 x cols=W=4096) matrices data size: A - 8 MB, B - 16 MB, C - 16 MB ______________________________________________________ Evaluating algorithm #1/3: CPU with OpenMP algorithm times (in seconds) - 1 values (min=11.6236 10%=11.6236 median=11.6236 90%=11.6236 max=11.6236) algorithm GFlops: 1.47729 GFlops algorithm effective memory bandwidth: 0.00470486 GB/s ______________________________________________________ Evaluating algorithm #2/3: 01 naive Kernels compilation done in 0.069236 seconds algorithm times (in seconds) - 10 values (min=0.038186 10%=0.0382057 median=0.0391316 90%=0.143556 max=0.143556) algorithm GFlops: 438.814 GFlops algorithm effective memory bandwidth: 1.39753 GB/s relative differences with CPU: 8388608 values (min=0 10%=0 median=2.21073e-07 90%=1.12363e-06 max=2.77294) median relative difference with CPU: 2.21073e-07 99% percentile relative difference with CPU: 1.09303e-05 ______________________________________________________ Evaluating algorithm #3/3: 02 using local memory Kernels compilation done in 0.0838083 seconds algorithm times (in seconds) - 10 values (min=0.0598847 10%=0.0600926 median=0.0602318 90%=0.143199 max=0.143199) algorithm GFlops: 285.09 GFlops algorithm effective memory bandwidth: 0.90795 GB/s relative differences with CPU: 8388608 values (min=0 10%=0 median=2.21073e-07 90%=1.12363e-06 max=2.77294) median relative difference with CPU: 2.21073e-07 99% percentile relative difference with CPU: 1.09303e-05 |
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GitHub CI - на CPU, поэтому не так важно. Но на GPU у вас так же происходит замедление. Посмотрите на свою реализацию с local_memory, в чем заключается оптимизация? За счет чего она должна работать быстрее чем наивная реализация? |
added 2 commits
March 8, 2026 03:27
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9/10 баллов 👍 |
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