fs

Intel Core i7-1185G7 testing with a Dell 0DXP1F (3.7.0 BIOS) and Intel Xe TGL GT2 15GB on Ubuntu 22.04 via the Phoronix Test Suite.

HTML result view exported from: https://openbenchmarking.org/result/2311191-PTS-FS16700400&sor&grt.

fsProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLOpenCLVulkanCompilerFile-SystemScreen ResolutionsbcdIntel Core i7-1185G7 @ 4.80GHz (4 Cores / 8 Threads)Dell 0DXP1F (3.7.0 BIOS)Intel Tiger Lake-LP16GBMicron 2300 NVMe 512GBIntel Xe TGL GT2 15GB (1350MHz)Realtek ALC289Intel Wi-Fi 6 AX201Ubuntu 22.046.2.0-36-generic (x86_64)GNOME Shell 42.2X Server + Wayland4.6 Mesa 22.0.1OpenCL 3.01.3.204GCC 11.4.0ext41920x1200OpenBenchmarking.orgKernel Details- Transparent Huge Pages: madviseProcessor Details- Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0xac - Thermald 2.4.9 Python Details- Python 3.10.12Security Details- gather_data_sampling: Mitigation of Microcode + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: 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 IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected

fsblender: BMW27 - CPU-Onlyblender: Classroom - CPU-Onlyblender: Fishy Cat - CPU-Onlyblender: Pabellon Barcelona - CPU-Onlypytorch: 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_lsbcd417.281100.02528.211401.8831.0811.5114.2313.8613.825.4913.905.5713.835.545.555.567.374.084.114.104.134.10417.641103.33528.721405.3730.159.9313.7813.6513.755.6313.765.5813.745.615.605.597.324.114.124.114.084.12416.741101.16528.011401.1330.6210.9713.7813.7913.795.6213.815.6113.815.615.595.597.364.134.124.124.124.12416.311113.84528.781402.1424.759.7913.8113.5313.795.5813.685.6213.775.625.625.627.334.134.114.124.124.11OpenBenchmarking.org

Blender

Blend File: BMW27 - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: BMW27 - Compute: CPU-Onlydcsb90180270360450SE +/- 0.10, N = 3416.31416.74417.28417.64

Blender

Blend File: Classroom - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: Classroom - Compute: CPU-Onlyscbd2004006008001000SE +/- 12.91, N = 41100.021101.161103.331113.84

Blender

Blend File: Fishy Cat - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: Fishy Cat - Compute: CPU-Onlycsbd110220330440550SE +/- 0.85, N = 3528.01528.21528.72528.78

Blender

Blend File: Pabellon Barcelona - Compute: CPU-Only

OpenBenchmarking.orgSeconds, Fewer Is BetterBlender 4.0Blend File: Pabellon Barcelona - Compute: CPU-Onlycsdb30060090012001500SE +/- 1.37, N = 31401.131401.881402.141405.37

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50scbd714212835SE +/- 0.45, N = 1331.0830.6230.1524.75MIN: 24.28 / MAX: 32.05MIN: 23.7 / MAX: 31.5MIN: 23.77 / MAX: 31.47MIN: 13.75 / MAX: 31.86

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152scbd3691215SE +/- 0.02, N = 311.5110.979.939.79MIN: 9.43 / MAX: 12.68MIN: 9 / MAX: 12.55MIN: 5.85 / MAX: 12.52MIN: 7.71 / MAX: 9.95

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50sdcb48121620SE +/- 0.05, N = 314.2313.8113.7813.78MIN: 12.34 / MAX: 16.52MIN: 11.95 / MAX: 14.1MIN: 11.44 / MAX: 14MIN: 12.06 / MAX: 14.04

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50scbd48121620SE +/- 0.10, N = 313.8613.7913.6513.53MIN: 12.24 / MAX: 14.1MIN: 12.26 / MAX: 13.98MIN: 11.36 / MAX: 13.96MIN: 7.72 / MAX: 13.95

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50sdcb48121620SE +/- 0.03, N = 313.8213.7913.7913.75MIN: 12.08 / MAX: 14.09MIN: 12.11 / MAX: 14.04MIN: 12.1 / MAX: 14.08MIN: 12.17 / MAX: 13.99

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152bcds1.26682.53363.80045.06726.334SE +/- 0.04, N = 35.635.625.585.49MIN: 5.35 / MAX: 5.71MIN: 5.35 / MAX: 5.69MIN: 4.32 / MAX: 5.7MIN: 4.9 / MAX: 5.6

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50scbd48121620SE +/- 0.12, N = 313.9013.8113.7613.68MIN: 10.84 / MAX: 14.08MIN: 11.69 / MAX: 14.05MIN: 9.38 / MAX: 13.97MIN: 11.92 / MAX: 14.02

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152dcbs1.26452.5293.79355.0586.3225SE +/- 0.01, N = 35.625.615.585.57MIN: 4.5 / MAX: 5.69MIN: 5.32 / MAX: 5.68MIN: 4.83 / MAX: 5.65MIN: 4.59 / MAX: 5.66

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-50scdb48121620SE +/- 0.02, N = 313.8313.8113.7713.74MIN: 12.12 / MAX: 14.03MIN: 12.27 / MAX: 14.04MIN: 7.55 / MAX: 14.1MIN: 12.06 / MAX: 13.95

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152dcbs1.26452.5293.79355.0586.3225SE +/- 0.00, N = 35.625.615.615.54MIN: 5.31 / MAX: 5.71MIN: 5.32 / MAX: 5.68MIN: 5.34 / MAX: 5.67MIN: 5.28 / MAX: 5.59

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-152dbcs1.26452.5293.79355.0586.3225SE +/- 0.01, N = 35.625.605.595.55MIN: 5.19 / MAX: 5.7MIN: 4.17 / MAX: 5.67MIN: 5.31 / MAX: 5.66MIN: 4.7 / MAX: 5.67

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-152dcbs1.26452.5293.79355.0586.3225SE +/- 0.01, N = 35.625.595.595.56MIN: 3.95 / MAX: 5.71MIN: 4.43 / MAX: 5.65MIN: 5.31 / MAX: 5.66MIN: 4.51 / MAX: 5.64

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_lscdb246810SE +/- 0.03, N = 37.377.367.337.32MIN: 5.71 / MAX: 7.46MIN: 6.88 / MAX: 7.43MIN: 5.29 / MAX: 7.45MIN: 5.07 / MAX: 7.42

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_ldcbs0.92931.85862.78793.71724.6465SE +/- 0.00, N = 34.134.134.114.08MIN: 3.27 / MAX: 4.18MIN: 3.56 / MAX: 4.17MIN: 3.78 / MAX: 4.16MIN: 3.25 / MAX: 4.13

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_lcbds0.9271.8542.7813.7084.635SE +/- 0.00, N = 34.124.124.114.11MIN: 3.19 / MAX: 4.17MIN: 3.84 / MAX: 4.17MIN: 2.84 / MAX: 4.16MIN: 3.08 / MAX: 4.17

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_ldcbs0.9271.8542.7813.7084.635SE +/- 0.00, N = 34.124.124.114.10MIN: 3.1 / MAX: 4.17MIN: 3.51 / MAX: 4.17MIN: 3.42 / MAX: 4.17MIN: 3.43 / MAX: 4.15

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_lsdcb0.92931.85862.78793.71724.6465SE +/- 0.00, N = 34.134.124.124.08MIN: 3.25 / MAX: 4.19MIN: 2.81 / MAX: 4.19MIN: 2.98 / MAX: 4.17MIN: 3.42 / MAX: 4.13

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_lcbds0.9271.8542.7813.7084.635SE +/- 0.00, N = 34.124.124.114.10MIN: 2.97 / MAX: 4.17MIN: 3.39 / MAX: 4.17MIN: 2.87 / MAX: 4.18MIN: 2.87 / MAX: 4.15

Phoronix Test Suite v10.8.4