test-pytorch

Intel Xeon E5-2680 v4 testing with a MACHINIST X99-MR9A PRO MAX (5.11 BIOS) and NVIDIA GeForce RTX 3060 12GB on Ubuntu 22.04 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 2404251-NE-TESTPYTOR08
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teste-total
April 25
  4 Hours, 38 Minutes
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teste-total

Processor: Intel Xeon E5-2680 v4 @ 3.30GHz (14 Cores / 28 Threads), Motherboard: MACHINIST X99-MR9A PRO MAX (5.11 BIOS), Chipset: Intel Xeon E7 v4/Xeon, Memory: 32GB, Disk: 500GB KINGSTON SNV2S500G + 160GB MAXTOR STM316021 + 512GB P3-512, Graphics: NVIDIA GeForce RTX 3060 12GB, Audio: Realtek ALC897, Monitor: TV-PHILCO, Network: Realtek RTL8111/8168/8411

OS: Ubuntu 22.04, Kernel: 6.5.0-28-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.4, Display Driver: NVIDIA 535.171.04, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Processor Notes: Scaling Governor: intel_cpufreq schedutil - CPU Microcode: 0xb000040
Python Notes: Python 3.10.12
Security Notes: gather_data_sampling: Not affected + itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Mitigation of PTE Inversion; VMX: conditional cache flushes SMT vulnerable + mds: Mitigation of Clear buffers; SMT vulnerable + meltdown: Mitigation of PTI + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + 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 Retpolines IBPB: conditional IBRS_FW STIBP: conditional RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Mitigation of Clear buffers; SMT vulnerable

test-pytorchOpenBenchmarking.orgPhoronix Test SuiteIntel Xeon E5-2680 v4 @ 3.30GHz (14 Cores / 28 Threads)MACHINIST X99-MR9A PRO MAX (5.11 BIOS)Intel Xeon E7 v4/Xeon32GB500GB KINGSTON SNV2S500G + 160GB MAXTOR STM316021 + 512GB P3-512NVIDIA GeForce RTX 3060 12GBRealtek ALC897TV-PHILCORealtek RTL8111/8168/8411Ubuntu 22.046.5.0-28-generic (x86_64)GNOME Shell 42.9X Server 1.21.1.4NVIDIA 535.171.04GCC 11.4.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverCompilerFile-SystemScreen ResolutionTest-pytorch BenchmarksSystem Logs- Transparent Huge Pages: madvise- Scaling Governor: intel_cpufreq schedutil - CPU Microcode: 0xb000040- Python 3.10.12- gather_data_sampling: Not affected + itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Mitigation of PTE Inversion; VMX: conditional cache flushes SMT vulnerable + mds: Mitigation of Clear buffers; SMT vulnerable + meltdown: Mitigation of PTI + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + 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 Retpolines IBPB: conditional IBRS_FW STIBP: conditional RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Mitigation of Clear buffers; SMT vulnerable

test-pytorchpytorch: 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_lteste-total29.0911.2920.7520.9621.078.8020.868.8121.048.858.868.706.664.984.965.055.004.92102.3036.3299.6499.9199.6235.53100.1835.53100.2135.4035.1635.5018.2217.7917.8717.9617.8718.05OpenBenchmarking.org

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-50teste-total714212835SE +/- 0.21, N = 1229.09MIN: 22.21 / MAX: 31.7

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-152teste-total3691215SE +/- 0.04, N = 311.29MIN: 8.37 / MAX: 12.03

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50teste-total510152025SE +/- 0.23, N = 320.75MIN: 15.96 / MAX: 21.78

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50teste-total510152025SE +/- 0.19, N = 320.96MIN: 17.26 / MAX: 22.23

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50teste-total510152025SE +/- 0.19, N = 321.07MIN: 16.2 / MAX: 22.19

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152teste-total246810SE +/- 0.03, N = 38.80MIN: 6.41 / MAX: 9.14

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50teste-total510152025SE +/- 0.15, N = 320.86MIN: 15.67 / MAX: 22.29

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-152teste-total246810SE +/- 0.03, N = 38.81MIN: 8.42 / MAX: 9.02

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-50teste-total510152025SE +/- 0.16, N = 1021.04MIN: 14.62 / MAX: 23.22

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-152teste-total246810SE +/- 0.01, N = 38.85MIN: 7.34 / MAX: 9.06

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-152teste-total246810SE +/- 0.04, N = 38.86MIN: 7.22 / MAX: 9.09

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-152teste-total246810SE +/- 0.03, N = 38.70MIN: 6.36 / MAX: 8.91

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_lteste-total246810SE +/- 0.03, N = 36.66MIN: 5.42 / MAX: 7.35

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lteste-total1.12052.2413.36154.4825.6025SE +/- 0.00, N = 34.98MIN: 4.4 / MAX: 5.16

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_lteste-total1.1162.2323.3484.4645.58SE +/- 0.03, N = 34.96MIN: 4.58 / MAX: 5.17

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_lteste-total1.13632.27263.40894.54525.6815SE +/- 0.02, N = 35.05MIN: 4.2 / MAX: 5.22

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_lteste-total1.1252.253.3754.55.625SE +/- 0.01, N = 35.00MIN: 4.53 / MAX: 5.17

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_lteste-total1.1072.2143.3214.4285.535SE +/- 0.05, N = 34.92MIN: 4.17 / MAX: 5.11

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50teste-total20406080100SE +/- 0.46, N = 3102.30MIN: 92.86 / MAX: 105.67

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152teste-total816243240SE +/- 0.38, N = 336.32MIN: 34.01 / MAX: 37.76

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50teste-total20406080100SE +/- 1.02, N = 599.64MIN: 82.77 / MAX: 104.87

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50teste-total20406080100SE +/- 0.89, N = 799.91MIN: 76.59 / MAX: 106.68

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50teste-total20406080100SE +/- 0.73, N = 399.62MIN: 89.42 / MAX: 102.62

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152teste-total816243240SE +/- 0.16, N = 335.53MIN: 33.45 / MAX: 36.51

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50teste-total20406080100SE +/- 0.32, N = 3100.18MIN: 91.43 / MAX: 102.65

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152teste-total816243240SE +/- 0.43, N = 335.53MIN: 32.04 / MAX: 37.12

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50teste-total20406080100SE +/- 1.18, N = 3100.21MIN: 89.37 / MAX: 104.69

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152teste-total816243240SE +/- 0.35, N = 335.40MIN: 32.64 / MAX: 36.8

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152teste-total816243240SE +/- 0.33, N = 335.16MIN: 32.29 / MAX: 36.53

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152teste-total816243240SE +/- 0.28, N = 335.50MIN: 32.84 / MAX: 36.69

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_lteste-total48121620SE +/- 0.21, N = 318.22MIN: 17.02 / MAX: 18.98

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_lteste-total48121620SE +/- 0.06, N = 317.79MIN: 16.76 / MAX: 18.24

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_lteste-total48121620SE +/- 0.22, N = 317.87MIN: 16.93 / MAX: 18.63

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_lteste-total48121620SE +/- 0.06, N = 317.96MIN: 17.08 / MAX: 18.37

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_lteste-total48121620SE +/- 0.03, N = 317.87MIN: 16.71 / MAX: 18.31

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_lteste-total48121620SE +/- 0.05, N = 318.05MIN: 16.72 / MAX: 18.46

teste-total

Processor: Intel Xeon E5-2680 v4 @ 3.30GHz (14 Cores / 28 Threads), Motherboard: MACHINIST X99-MR9A PRO MAX (5.11 BIOS), Chipset: Intel Xeon E7 v4/Xeon, Memory: 32GB, Disk: 500GB KINGSTON SNV2S500G + 160GB MAXTOR STM316021 + 512GB P3-512, Graphics: NVIDIA GeForce RTX 3060 12GB, Audio: Realtek ALC897, Monitor: TV-PHILCO, Network: Realtek RTL8111/8168/8411

OS: Ubuntu 22.04, Kernel: 6.5.0-28-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.4, Display Driver: NVIDIA 535.171.04, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: madvise
Processor Notes: Scaling Governor: intel_cpufreq schedutil - CPU Microcode: 0xb000040
Python Notes: Python 3.10.12
Security Notes: gather_data_sampling: Not affected + itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Mitigation of PTE Inversion; VMX: conditional cache flushes SMT vulnerable + mds: Mitigation of Clear buffers; SMT vulnerable + meltdown: Mitigation of PTI + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + 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 Retpolines IBPB: conditional IBRS_FW STIBP: conditional RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Mitigation of Clear buffers; SMT vulnerable

Testing initiated at 25 April 2024 15:32 by user pablue.