pyt

AMD EPYC 9654 96-Core testing with a AMD Titanite_4G (RTI1007B BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite.

HTML result view exported from: https://openbenchmarking.org/result/2311183-NE-PYT49584300&gru&sor.

pytProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelDesktopDisplay ServerCompilerFile-SystemScreen Resolutionabcd2 x AMD EPYC 9654 96-Core @ 2.40GHz (192 Cores / 384 Threads)AMD Titanite_4G (RTI1007B BIOS)AMD Device 14a41520GB3201GB Micron_7450_MTFDKCC3T2TFSASPEEDBroadcom NetXtreme BCM5720 PCIeUbuntu 23.106.6.0-rc5-phx-patched (x86_64)GNOME Shell 45.0X Server 1.21.1.7GCC 13.2.0ext41920x1200AMD EPYC 9654 96-Core @ 2.40GHz (96 Cores / 192 Threads)768GBOpenBenchmarking.orgKernel Details- Transparent Huge Pages: madviseProcessor Details- Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa10113ePython Details- Python 3.11.6Security Details- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: 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

pytpytorch: 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_labcd21.9110.2820.2221.7720.469.2420.559.3421.378.948.978.716.751.291.2946.6118.9239.8739.8240.0616.3839.4216.1239.6916.4416.4516.4810.896.446.366.416.346.4046.7117.8339.8039.8339.9616.0839.7516.0539.9315.8416.1416.4611.116.376.3846.4518.6540.3839.5439.7716.0140.2315.8939.3516.5916.5715.8910.966.396.366.376.406.38OpenBenchmarking.org

PyTorch

Device: CPU - Batch Size: 1 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50cbda112233445546.7146.6146.4521.91MIN: 45.82 / MAX: 47.64MIN: 41.78 / MAX: 47.84MIN: 45.7 / MAX: 47.37MIN: 12.38 / MAX: 23.84

PyTorch

Device: CPU - Batch Size: 1 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152bdca51015202518.9218.6517.8310.28MIN: 10.58 / MAX: 19.1MIN: 18.45 / MAX: 18.9MIN: 17.67 / MAX: 17.98MIN: 5.05 / MAX: 10.64

PyTorch

Device: CPU - Batch Size: 16 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50dbca91827364540.3839.8739.8020.22MIN: 39.31 / MAX: 41.34MIN: 38.37 / MAX: 40.84MIN: 38.39 / MAX: 40.71MIN: 11.34 / MAX: 20.76

PyTorch

Device: CPU - Batch Size: 32 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50cbda91827364539.8339.8239.5421.77MIN: 39.1 / MAX: 40.71MIN: 38.85 / MAX: 40.77MIN: 38.66 / MAX: 40.21MIN: 18.59 / MAX: 22.39

PyTorch

Device: CPU - Batch Size: 64 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50bcda91827364540.0639.9639.7720.46MIN: 39.17 / MAX: 40.95MIN: 38.88 / MAX: 40.88MIN: 38.66 / MAX: 40.81MIN: 12.21 / MAX: 21.36

PyTorch

Device: CPU - Batch Size: 16 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152bcda4812162016.3816.0816.019.24MIN: 16.2 / MAX: 16.56MIN: 15.91 / MAX: 16.23MIN: 15.87 / MAX: 16.17MIN: 6.47 / MAX: 9.41

PyTorch

Device: CPU - Batch Size: 256 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50dcba91827364540.2339.7539.4220.55MIN: 39.26 / MAX: 41.13MIN: 38.53 / MAX: 40.65MIN: 38.56 / MAX: 40.13MIN: 12.2 / MAX: 21.39

PyTorch

Device: CPU - Batch Size: 32 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152bcda4812162016.1216.0515.899.34MIN: 15.97 / MAX: 16.29MIN: 15.91 / MAX: 16.29MIN: 15.7 / MAX: 16.11MIN: 5.1 / MAX: 9.48

PyTorch

Device: CPU - Batch Size: 512 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-50cbda91827364539.9339.6939.3521.37MIN: 38.89 / MAX: 40.82MIN: 38.5 / MAX: 40.43MIN: 38.36 / MAX: 40.29MIN: 20.51 / MAX: 21.86

PyTorch

Device: CPU - Batch Size: 64 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152dbca4812162016.5916.4415.848.94MIN: 16.4 / MAX: 16.74MIN: 16.27 / MAX: 16.62MIN: 15.67 / MAX: 16MIN: 4.61 / MAX: 9.21

PyTorch

Device: CPU - Batch Size: 256 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-152dbca4812162016.5716.4516.148.97MIN: 16.4 / MAX: 16.73MIN: 16.26 / MAX: 16.62MIN: 15.96 / MAX: 16.34MIN: 5.15 / MAX: 9.12

PyTorch

Device: CPU - Batch Size: 512 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-152bcda4812162016.4816.4615.898.71MIN: 16.26 / MAX: 16.64MIN: 16.27 / MAX: 16.61MIN: 15.72 / MAX: 16.05MIN: 8.58 / MAX: 8.83

PyTorch

Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lcdba369121511.1110.9610.896.75MIN: 11.02 / MAX: 11.2MIN: 10.88 / MAX: 11.02MIN: 10.79 / MAX: 10.98MIN: 4.17 / MAX: 6.89

PyTorch

Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lbdca2468106.446.396.371.29MIN: 5.99 / MAX: 6.57MIN: 4.97 / MAX: 6.52MIN: 5.95 / MAX: 6.52MIN: 0.62 / MAX: 2.39

PyTorch

Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lcdba2468106.386.366.361.29MIN: 5.17 / MAX: 6.51MIN: 5.94 / MAX: 6.48MIN: 5.91 / MAX: 6.47MIN: 0.48 / MAX: 2.37

PyTorch

Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lbd2468106.416.37MIN: 5.98 / MAX: 6.55MIN: 5.86 / MAX: 6.49

PyTorch

Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_ldb2468106.406.34MIN: 6 / MAX: 6.53MIN: 5.84 / MAX: 6.47

PyTorch

Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_lbd2468106.406.38MIN: 5.88 / MAX: 6.53MIN: 5.97 / MAX: 6.5

Phoronix Test Suite v10.8.5