resultgangutlx

AMD Ryzen 5 5600G testing with a Gigabyte B450M DS3H-CF (F63c BIOS) and NVIDIA RTX A4000 16GB on Ubuntu 24.04 via the Phoronix Test Suite.

HTML result view exported from: https://openbenchmarking.org/result/2408150-NE-RESULTGAN97&grw.

resultgangutlxProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLCompilerFile-SystemScreen ResolutionAMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - GigabyteAMD Ryzen 5 5600G @ 4.46GHz (6 Cores / 12 Threads)Gigabyte B450M DS3H-CF (F63c BIOS)AMD Renoir/Cezanne24GBPatriot M.2 P300 512GB + 1024GB ADATA SX6000LNP + 2048GB XPG GAMMIX S11 ProNVIDIA RTX A4000 16GBNVIDIA GA104 HD AudioDP1 + LG ULTRAWIDERealtek RTL8111/8168/8211/8411Ubuntu 24.046.8.0-39-generic (x86_64)Xfce 4.18X Server 1.21.1.11NVIDIA 560.28.034.6.0GCC 13.2.0 + CUDA 12.6ext46000x1440OpenBenchmarking.org- Transparent Huge Pages: madvise- Scaling Governor: amd-pstate-epp powersave (EPP: performance) - CPU Microcode: 0xa50000d- Python 3.11.9- 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: Vulnerable: 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 Retpolines; IBPB: conditional; IBRS_FW; STIBP: always-on; RSB filling; PBRSB-eIBRS: Not affected; BHI: Not affected + srbds: Not affected + tsx_async_abort: Not affected

resultgangutlxpytorch: CPU - 1 - ResNet-50pytorch: CPU - 16 - ResNet-50pytorch: CPU - 32 - ResNet-50pytorch: CPU - 64 - ResNet-50pytorch: CPU - 256 - ResNet-50pytorch: CPU - 512 - ResNet-50pytorch: NVIDIA CUDA GPU - 1 - ResNet-50pytorch: NVIDIA CUDA GPU - 16 - ResNet-50pytorch: NVIDIA CUDA GPU - 32 - ResNet-50pytorch: NVIDIA CUDA GPU - 64 - ResNet-50pytorch: NVIDIA CUDA GPU - 256 - ResNet-50pytorch: NVIDIA CUDA GPU - 512 - ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte24.7313.7413.9414.7413.6515.06183.79183.04179.99184.99179.98168.86OpenBenchmarking.org

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 1 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte612182430SE +/- 0.30, N = 1524.73MIN: 12.22 / MAX: 27.64

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte48121620SE +/- 0.10, N = 313.74MIN: 8.28 / MAX: 15.45

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 32 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte48121620SE +/- 0.19, N = 313.94MIN: 8.75 / MAX: 15.84

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 64 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte48121620SE +/- 0.05, N = 314.74MIN: 10.23 / MAX: 15.94

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 256 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte48121620SE +/- 0.38, N = 913.65MIN: 6.72 / MAX: 16.12

PyTorch

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

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 512 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte48121620SE +/- 0.15, N = 615.06MIN: 10.56 / MAX: 16.28

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte4080120160200SE +/- 1.39, N = 15183.79MIN: 102.44 / MAX: 201.24

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte4080120160200SE +/- 0.62, N = 3183.04MIN: 112.5 / MAX: 194.99

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte4080120160200SE +/- 1.45, N = 15179.99MIN: 111.38 / MAX: 197.56

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte4080120160200SE +/- 1.02, N = 3184.99MIN: 128.27 / MAX: 196.82

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte4080120160200SE +/- 1.62, N = 7179.98MIN: 114.76 / MAX: 195.5

PyTorch

Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50AMD Ryzen 5 5600G - NVIDIA RTX A4000 16GB - Gigabyte4080120160200SE +/- 2.55, N = 12168.86MIN: 83.82 / MAX: 196.57

Phoronix Test Suite v10.8.5