Intel Core i7-13700K testing with a ASUS PRIME Z790-P WIFI (1010 BIOS) and NVIDIA GeForce RTX 3090 24GB 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 2402267-NE-240225PYT25 240225pytorch - Phoronix Test Suite 240225pytorch Intel Core i7-13700K testing with a ASUS PRIME Z790-P WIFI (1010 BIOS) and NVIDIA GeForce RTX 3090 24GB on Ubuntu 22.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2402267-NE-240225PYT25&export=txt .
240225pytorch Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server Display Driver OpenGL Compiler File-System Screen Resolution 240225pytorch Intel Core i7-13700K @ 5.30GHz (16 Cores / 24 Threads) ASUS PRIME Z790-P WIFI (1010 BIOS) Intel Device 7a27 64GB 2000GB Samsung SSD 990 PRO with Heatsink 2TB NVIDIA GeForce RTX 3090 24GB Realtek ALC897 Sceptre K32 + HP VH240a Realtek RTL8125 2.5GbE + Intel Device 7a70 Ubuntu 22.04 6.5.0-21-generic (x86_64) GNOME Shell 42.9 X Server 1.21.1.4 NVIDIA 535.154.05 4.6.0 GCC 12.3.0 + Clang 15.0.7 + CUDA 12.3 ext4 1920x2160 OpenBenchmarking.org - Transparent Huge Pages: madvise - Scaling Governor: intel_pstate powersave (EPP: performance) - CPU Microcode: 0x11d - Thermald 2.4.9 - Python 3.10.12 - 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: 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 RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected
240225pytorch pytorch: CPU - 16 - ResNet-50 pytorch: CPU - 32 - ResNet-50 pytorch: CPU - 64 - ResNet-50 pytorch: CPU - 256 - ResNet-50 pytorch: NVIDIA CUDA GPU - 16 - ResNet-50 pytorch: NVIDIA CUDA GPU - 32 - ResNet-50 pytorch: NVIDIA CUDA GPU - 64 - ResNet-50 pytorch: NVIDIA CUDA GPU - 256 - ResNet-50 240225pytorch 34.48 33.62 33.28 34.08 436.14 436.80 431.20 436.12 OpenBenchmarking.org
PyTorch Device: CPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 240225pytorch 8 16 24 32 40 SE +/- 0.35, N = 14 34.48 MIN: 30.05 / MAX: 35.87
PyTorch Device: CPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 240225pytorch 8 16 24 32 40 SE +/- 0.47, N = 12 33.62 MIN: 28.51 / MAX: 35.44
PyTorch Device: CPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 240225pytorch 8 16 24 32 40 SE +/- 0.48, N = 15 33.28 MIN: 30.23 / MAX: 35.55
PyTorch Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 240225pytorch 8 16 24 32 40 SE +/- 0.47, N = 15 34.08 MIN: 30.43 / MAX: 35.71
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 240225pytorch 90 180 270 360 450 SE +/- 0.46, N = 3 436.14 MIN: 353.17 / MAX: 439.52
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 240225pytorch 90 180 270 360 450 SE +/- 0.37, N = 3 436.80 MIN: 364.53 / MAX: 439.61
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 240225pytorch 90 180 270 360 450 SE +/- 4.35, N = 5 431.20 MIN: 321.3 / MAX: 440.12
PyTorch Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 240225pytorch 90 180 270 360 450 SE +/- 0.17, N = 3 436.12 MIN: 353.29 / MAX: 439.01
Phoronix Test Suite v10.8.4