pyt

AMD Ryzen 7 7840U testing with a Framework FRANMDCP07 (03.03 BIOS) and AMD Phoenix1 512MB on Ubuntu 23.10 via the Phoronix Test Suite.

HTML result view exported from: https://openbenchmarking.org/result/2311177-NE-PYT09912200&rdt&grw.

pytProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLCompilerFile-SystemScreen ResolutionabcAMD Ryzen 7 7840U @ 5.13GHz (8 Cores / 16 Threads)Framework FRANMDCP07 (03.03 BIOS)AMD Device 14e816GB512GB Western Digital WD PC SN740 SDDPNQD-512GAMD Phoenix1 512MB (2700/2800MHz)AMD Rembrandt Radeon HD AudioMEDIATEK MT7922 802.11ax PCIUbuntu 23.106.5.0-5-generic (x86_64)GNOME Shell 45.0X Server 1.21.1.7 + Wayland4.6 Mesa 23.2.1-1ubuntu3 (LLVM 15.0.7 DRM 3.54)GCC 13.2.0ext42256x1504OpenBenchmarking.orgKernel Details- Transparent Huge Pages: madviseProcessor Details- Scaling Governor: amd-pstate-epp powersave (EPP: performance) - Platform Profile: balanced - CPU Microcode: 0xa704103 - ACPI Profile: balanced Python 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 no microcode + 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_labc48.3819.7428.4128.2928.4912.0428.6412.0628.2711.9411.8911.8111.537.957.958.077.958.0047.2618.9527.9628.0528.0111.8628.1811.8628.2911.7811.7611.9211.698.088.158.158.168.1348.3619.6927.8127.8328.0011.8128.1912.0128.5012.0411.9311.8811.568.328.128.268.138.23OpenBenchmarking.org

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50abc1122334455SE +/- 0.44, N = 648.3847.2648.36MIN: 41.22 / MAX: 50.02MIN: 41.53 / MAX: 51.31MIN: 41.06 / MAX: 49.91

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152abc510152025SE +/- 0.11, N = 319.7418.9519.69MIN: 18.61 / MAX: 20.18MIN: 14.51 / MAX: 19.68MIN: 18.04 / MAX: 20.35

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50abc714212835SE +/- 0.14, N = 328.4127.9627.81MIN: 25.51 / MAX: 29.19MIN: 21.85 / MAX: 28.92MIN: 26.48 / MAX: 28.8

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50abc714212835SE +/- 0.09, N = 328.2928.0527.83MIN: 27.3 / MAX: 29.15MIN: 26.42 / MAX: 29.48MIN: 26.49 / MAX: 28.98

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50abc714212835SE +/- 0.07, N = 328.4928.0128.00MIN: 26.35 / MAX: 29.3MIN: 20.32 / MAX: 29.01MIN: 26.54 / MAX: 28.94

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152abc3691215SE +/- 0.09, N = 312.0411.8611.81MIN: 11.44 / MAX: 13MIN: 11.09 / MAX: 13MIN: 11.35 / MAX: 12.69

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50abc714212835SE +/- 0.13, N = 328.6428.1828.19MIN: 26.98 / MAX: 29.4MIN: 26.5 / MAX: 29.46MIN: 26.46 / MAX: 28.95

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152abc3691215SE +/- 0.08, N = 312.0611.8612.01MIN: 9.79 / MAX: 13.14MIN: 9.37 / MAX: 13.04MIN: 11.45 / MAX: 13.16

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-50abc714212835SE +/- 0.14, N = 328.2728.2928.50MIN: 25.95 / MAX: 29.23MIN: 21.31 / MAX: 29.54MIN: 27.07 / MAX: 29.33

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152abc3691215SE +/- 0.08, N = 311.9411.7812.04MIN: 11.54 / MAX: 13.06MIN: 11.13 / MAX: 13.04MIN: 11.62 / MAX: 13.07

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-152abc3691215SE +/- 0.04, N = 311.8911.7611.93MIN: 11.44 / MAX: 12.85MIN: 10.23 / MAX: 12.89MIN: 11.42 / MAX: 12.99

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-152abc3691215SE +/- 0.02, N = 311.8111.9211.88MIN: 11.54 / MAX: 12.94MIN: 9.85 / MAX: 13.06MIN: 11.33 / MAX: 12.95

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_labc3691215SE +/- 0.09, N = 311.5311.6911.56MIN: 10.83 / MAX: 11.91MIN: 11.06 / MAX: 12.27MIN: 11.12 / MAX: 12.03

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_labc246810SE +/- 0.03, N = 37.958.088.32MIN: 6.64 / MAX: 8.58MIN: 6.63 / MAX: 8.62MIN: 7.08 / MAX: 8.83

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_labc246810SE +/- 0.04, N = 37.958.158.12MIN: 6.86 / MAX: 8.65MIN: 6.91 / MAX: 8.83MIN: 7.02 / MAX: 8.57

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_labc246810SE +/- 0.04, N = 38.078.158.26MIN: 6.39 / MAX: 8.61MIN: 6.84 / MAX: 8.85MIN: 6.74 / MAX: 8.84

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_labc246810SE +/- 0.04, N = 37.958.168.13MIN: 6.29 / MAX: 8.43MIN: 6.51 / MAX: 8.8MIN: 7.13 / MAX: 8.64

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_labc246810SE +/- 0.06, N = 38.008.138.23MIN: 6.26 / MAX: 8.59MIN: 6.49 / MAX: 8.69MIN: 6.6 / MAX: 8.75

Phoronix Test Suite v10.8.5