somervillePyTorchFULL

AMD Ryzen 9 9950X 16-Core testing with a ASUS PRIME B650M-A II (3201 BIOS) and Gigabyte NVIDIA GeForce RTX 2070 SUPER 8GB on Ubuntu 24.04 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 2501271-NE-SOMERVILL26
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Result
Identifier
Performance Per
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Date
Run
  Test
  Duration
run1
January 28
  1 Hour, 41 Minutes
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run1

Processor: AMD Ryzen 9 9950X 16-Core @ 5.75GHz (16 Cores / 32 Threads), Motherboard: ASUS PRIME B650M-A II (3201 BIOS), Chipset: AMD Device 14d8, Memory: 4 x 48GB DDR5-3600MT/s G Skill F5-6800J3446F48G, Disk: 2000GB Samsung SSD 980 PRO 2TB, Graphics: Gigabyte NVIDIA GeForce RTX 2070 SUPER 8GB, Audio: NVIDIA TU104 HD Audio, Monitor: VS2447 100Hz, Network: 2 x Intel 10-Gigabit X540-AT2 + Realtek RTL8125 2.5GbE

OS: Ubuntu 24.04, Kernel: 6.8.0-51-generic (x86_64), Display Server: X Server 1.21.1.11, Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.4.131, Compiler: GCC 13.3.0 + CUDA 12.4, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Processor Notes: Scaling Governor: amd-pstate-epp powersave (EPP: balance_performance) - CPU Microcode: 0xb404023
Python Notes: Python 3.12.3
Security Notes: 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: Not affected + 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; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected

somervillePyTorchFULLOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 9 9950X 16-Core @ 5.75GHz (16 Cores / 32 Threads)ASUS PRIME B650M-A II (3201 BIOS)AMD Device 14d84 x 48GB DDR5-3600MT/s G Skill F5-6800J3446F48G2000GB Samsung SSD 980 PRO 2TBGigabyte NVIDIA GeForce RTX 2070 SUPER 8GBNVIDIA TU104 HD AudioVS2447 100Hz2 x Intel 10-Gigabit X540-AT2 + Realtek RTL8125 2.5GbEUbuntu 24.046.8.0-51-generic (x86_64)X Server 1.21.1.11NVIDIAOpenCL 3.0 CUDA 12.4.131GCC 13.3.0 + CUDA 12.4ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDisplay ServerDisplay DriverOpenCLCompilerFile-SystemScreen ResolutionSomervillePyTorchFULL BenchmarksSystem Logs- Transparent Huge Pages: madvise- Scaling Governor: amd-pstate-epp powersave (EPP: balance_performance) - CPU Microcode: 0xb404023 - Python 3.12.3- 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: Not affected + 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; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected

somervillePyTorchFULLpytorch: 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_lpytorch: NVIDIA CUDA GPU - 1 - ResNet-50pytorch: NVIDIA CUDA GPU - 1 - ResNet-152pytorch: NVIDIA CUDA GPU - 16 - ResNet-50pytorch: NVIDIA CUDA GPU - 32 - ResNet-50pytorch: NVIDIA CUDA GPU - 64 - ResNet-50pytorch: NVIDIA CUDA GPU - 16 - ResNet-152pytorch: NVIDIA CUDA GPU - 256 - ResNet-50pytorch: NVIDIA CUDA GPU - 32 - ResNet-152pytorch: NVIDIA CUDA GPU - 512 - ResNet-50pytorch: NVIDIA CUDA GPU - 64 - ResNet-152pytorch: NVIDIA CUDA GPU - 256 - ResNet-152pytorch: NVIDIA CUDA GPU - 512 - ResNet-152pytorch: NVIDIA CUDA GPU - 1 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 16 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 32 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 64 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 256 - Efficientnet_v2_lpytorch: NVIDIA CUDA GPU - 512 - Efficientnet_v2_lrun173.7729.4453.1352.0152.4621.0752.0121.2650.9121.2021.2821.2116.2312.6412.6912.4212.6512.66325.51131.72208.83208.57208.4485.97207.5285.82207.7985.8585.6885.67104.2058.3858.3058.2658.2758.24OpenBenchmarking.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-50run11632486480SE +/- 0.24, N = 373.77MIN: 67.93 / MAX: 74.81

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-152run1714212835SE +/- 0.20, N = 329.44MIN: 27.2 / MAX: 30.14

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50run11224364860SE +/- 0.17, N = 353.13MIN: 46.44 / MAX: 53.96

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50run11224364860SE +/- 0.27, N = 352.01MIN: 48.14 / MAX: 53.17

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50run11224364860SE +/- 0.19, N = 352.46MIN: 45.47 / MAX: 53.68

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152run1510152025SE +/- 0.11, N = 321.07MIN: 19.52 / MAX: 21.47

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50run11224364860SE +/- 0.62, N = 452.01MIN: 47.86 / MAX: 53.15

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-152run1510152025SE +/- 0.24, N = 321.26MIN: 18.19 / MAX: 22

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-50run11122334455SE +/- 0.39, N = 350.91MIN: 48.68 / MAX: 51.95

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-152run1510152025SE +/- 0.23, N = 421.20MIN: 18.7 / MAX: 21.86

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-152run1510152025SE +/- 0.13, N = 321.28MIN: 18.47 / MAX: 21.73

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-152run1510152025SE +/- 0.23, N = 321.21MIN: 19.83 / MAX: 21.7

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lrun148121620SE +/- 0.06, N = 316.23MIN: 15.83 / MAX: 16.5

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lrun13691215SE +/- 0.06, N = 312.64MIN: 11.04 / MAX: 13.16

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lrun13691215SE +/- 0.02, N = 312.69MIN: 11.27 / MAX: 13.14

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lrun13691215SE +/- 0.08, N = 312.42MIN: 10.74 / MAX: 13.24

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_lrun13691215SE +/- 0.05, N = 312.65MIN: 11.39 / MAX: 13.17

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_lrun13691215SE +/- 0.08, N = 312.66MIN: 11.08 / MAX: 13.25

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50run170140210280350SE +/- 0.37, N = 3325.51MIN: 314.65 / MAX: 329.04

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152run1306090120150SE +/- 0.25, N = 3131.72MIN: 100.93 / MAX: 132.78

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50run150100150200250SE +/- 0.19, N = 3208.83MIN: 193.54 / MAX: 210.94

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50run150100150200250SE +/- 0.18, N = 3208.57MIN: 193.37 / MAX: 210.52

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50run150100150200250SE +/- 0.22, N = 3208.44MIN: 151.51 / MAX: 210.37

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152run120406080100SE +/- 0.14, N = 385.97MIN: 83.42 / MAX: 87.55

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50run150100150200250SE +/- 0.19, N = 3207.52MIN: 192.76 / MAX: 209.28

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152run120406080100SE +/- 0.06, N = 385.82MIN: 83.62 / MAX: 87.16

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50run150100150200250SE +/- 0.17, N = 3207.79MIN: 192.71 / MAX: 209.46

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152run120406080100SE +/- 0.07, N = 385.85MIN: 83.46 / MAX: 87.19

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152run120406080100SE +/- 0.07, N = 385.68MIN: 83.44 / MAX: 87.17

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152run120406080100SE +/- 0.08, N = 385.67MIN: 82.97 / MAX: 87.07

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_lrun120406080100SE +/- 0.04, N = 3104.20MIN: 89.05 / MAX: 105.94

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_lrun11326395265SE +/- 0.05, N = 358.38MIN: 50.27 / MAX: 59.3

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_lrun11326395265SE +/- 0.06, N = 358.30MIN: 52.73 / MAX: 59.25

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_lrun11326395265SE +/- 0.07, N = 358.26MIN: 51.34 / MAX: 59.25

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_lrun11326395265SE +/- 0.05, N = 358.27MIN: 54.21 / MAX: 59.2

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_lrun11326395265SE +/- 0.04, N = 358.24MIN: 51.17 / MAX: 59.03

run1

Processor: AMD Ryzen 9 9950X 16-Core @ 5.75GHz (16 Cores / 32 Threads), Motherboard: ASUS PRIME B650M-A II (3201 BIOS), Chipset: AMD Device 14d8, Memory: 4 x 48GB DDR5-3600MT/s G Skill F5-6800J3446F48G, Disk: 2000GB Samsung SSD 980 PRO 2TB, Graphics: Gigabyte NVIDIA GeForce RTX 2070 SUPER 8GB, Audio: NVIDIA TU104 HD Audio, Monitor: VS2447 100Hz, Network: 2 x Intel 10-Gigabit X540-AT2 + Realtek RTL8125 2.5GbE

OS: Ubuntu 24.04, Kernel: 6.8.0-51-generic (x86_64), Display Server: X Server 1.21.1.11, Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.4.131, Compiler: GCC 13.3.0 + CUDA 12.4, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Processor Notes: Scaling Governor: amd-pstate-epp powersave (EPP: balance_performance) - CPU Microcode: 0xb404023
Python Notes: Python 3.12.3
Security Notes: 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: Not affected + 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; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 28 January 2025 02:40 by user parallel.