pytorch 2.2.1 AMD EPYC Siena

AMD EPYC 8534P 64-Core testing with a AMD Cinnabar (RCB1009C BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2403269-NE-PYTORCH2202
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pytorch 2.2.1 AMD EPYC SienaOpenBenchmarking.orgPhoronix Test SuiteAMD EPYC 8534P 64-Core @ 2.30GHz (64 Cores / 128 Threads)AMD Cinnabar (RCB1009C BIOS)AMD Device 14a46 x 32GB DRAM-4800MT/s Samsung M321R4GA0BB0-CQKMG3201GB Micron_7450_MTFDKCB3T2TFS + 2000GB Corsair MP700ASPEED2 x Broadcom NetXtreme BCM5720 PCIeUbuntu 23.106.8.1-060801-generic (x86_64)GNOME Shell 45.2X Server 1.21.1.7GCC 13.2.0ext41920x1200ProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelDesktopDisplay ServerCompilerFile-SystemScreen ResolutionPytorch 2.2.1 AMD EPYC Siena BenchmarksSystem Logs- Transparent Huge Pages: madvise- Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xaa00212 - Python 3.11.6- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + reg_file_data_sampling: Not affected + retbleed: Not affected + spec_rstack_overflow: Mitigation of Safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

abcResult OverviewPhoronix Test Suite100%101%102%102%103%PyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchPyTorchCPU - 256 - ResNet-152CPU - 32 - ResNet-50CPU - 64 - ResNet-152CPU - 1 - ResNet-152CPU - 1 - Efficientnet_v2_lCPU - 32 - Efficientnet_v2_lCPU - 512 - ResNet-152CPU - 1 - ResNet-50CPU - 64 - ResNet-50CPU - 16 - ResNet-152CPU - 256 - ResNet-50CPU - 16 - ResNet-50CPU - 64 - Efficientnet_v2_lCPU - 32 - ResNet-152CPU - 512 - ResNet-50CPU - 16 - Efficientnet_v2_lCPU - 256 - Efficientnet_v2_lCPU - 512 - Efficientnet_v2_l

pytorch 2.2.1 AMD EPYC Sienapytorch: CPU - 1 - ResNet-50pytorch: CPU - 1 - ResNet-152pytorch: CPU - 16 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: CPU - 64 - ResNet-50pytorch: CPU - 16 - ResNet-152pytorch: CPU - 256 - ResNet-50pytorch: CPU - 32 - ResNet-152pytorch: CPU - 512 - ResNet-50pytorch: CPU - 64 - ResNet-152pytorch: CPU - 256 - ResNet-152pytorch: CPU - 512 - ResNet-152pytorch: CPU - 1 - Efficientnet_v2_lpytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lpytorch: CPU - 256 - Efficientnet_v2_lpytorch: CPU - 512 - Efficientnet_v2_labc41.1115.1533.3032.9433.8613.1933.6713.0733.4212.9612.9713.248.645.655.645.675.655.6840.6714.8833.3133.6533.5113.0633.3413.0333.5112.9913.1013.098.785.655.625.635.675.6540.7515.0333.5433.3633.6513.1033.5412.9933.6213.2213.3913.108.645.625.695.665.685.65OpenBenchmarking.org

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-50abc91827364541.1140.6740.75MIN: 39.95 / MAX: 41.96MIN: 39.57 / MAX: 41.58MIN: 39.84 / MAX: 41.56

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-152abc4812162015.1514.8815.03MIN: 14.65 / MAX: 15.33MIN: 14.71 / MAX: 15MIN: 9.88 / MAX: 15.14

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50abc81624324033.3033.3133.54MIN: 16.55 / MAX: 33.78MIN: 32.5 / MAX: 33.78MIN: 18.28 / MAX: 34

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50abc81624324032.9433.6533.36MIN: 14.77 / MAX: 33.36MIN: 32.2 / MAX: 34.12MIN: 31.61 / MAX: 33.8

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50abc81624324033.8633.5133.65MIN: 32.86 / MAX: 34.37MIN: 15.58 / MAX: 33.94MIN: 32.6 / MAX: 34.13

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152abc369121513.1913.0613.10MIN: 13.07 / MAX: 13.3MIN: 8.77 / MAX: 13.16MIN: 12.7 / MAX: 13.21

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50abc81624324033.6733.3433.54MIN: 32.41 / MAX: 34.14MIN: 32.81 / MAX: 33.77MIN: 16.1 / MAX: 34.1

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-152abc369121513.0713.0312.99MIN: 8.78 / MAX: 13.2MIN: 12.7 / MAX: 13.14MIN: 8.89 / MAX: 13.09

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-50abc81624324033.4233.5133.62MIN: 32.29 / MAX: 33.81MIN: 32.71 / MAX: 33.92MIN: 32.63 / MAX: 34.14

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-152abc369121512.9612.9913.22MIN: 12.23 / MAX: 13.06MIN: 12.86 / MAX: 13.09MIN: 13.02 / MAX: 13.32

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-152abc369121512.9713.1013.39MIN: 8.8 / MAX: 13.09MIN: 13 / MAX: 13.2MIN: 8.95 / MAX: 13.48

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-152abc369121513.2413.0913.10MIN: 12.34 / MAX: 13.35MIN: 8.81 / MAX: 13.18MIN: 12.96 / MAX: 13.2

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_labc2468108.648.788.64MIN: 8.55 / MAX: 8.71MIN: 6.74 / MAX: 8.86MIN: 8.5 / MAX: 8.72

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_labc1.27132.54263.81395.08526.35655.655.655.62MIN: 4.48 / MAX: 5.76MIN: 4.71 / MAX: 5.76MIN: 4.63 / MAX: 5.72

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_labc1.28032.56063.84095.12126.40155.645.625.69MIN: 4.7 / MAX: 5.75MIN: 5.06 / MAX: 5.71MIN: 4.72 / MAX: 5.78

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_labc1.27582.55163.82745.10326.3795.675.635.66MIN: 5.32 / MAX: 5.76MIN: 4.88 / MAX: 5.72MIN: 4.52 / MAX: 5.76

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_labc1.2782.5563.8345.1126.395.655.675.68MIN: 4.62 / MAX: 5.73MIN: 4.55 / MAX: 5.76MIN: 5.4 / MAX: 5.78

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_labc1.2782.5563.8345.1126.395.685.655.65MIN: 4.6 / MAX: 5.77MIN: 5.38 / MAX: 5.73MIN: 4.41 / MAX: 5.74