pytorch-test AMD Ryzen 7 5700X 8-Core testing with a MSI PRO B550M-P GEN3 (MS-7D95) v1.0 (1.60 BIOS) and NVIDIA GeForce RTX 4070 SUPER 12GB on Debian 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 2403306-NE-PYTORCHTE32 All options Processor: AMD Ryzen 7 5700X 8-Core @ 3.40GHz (8 Cores / 16 Threads), Motherboard: MSI PRO B550M-P GEN3 (MS-7D95) v1.0 (1.60 BIOS), Chipset: AMD Starship/Matisse, Memory: 128GB, Disk: 256GB ARDOR GAMING m.2 NVME 256Gb AL1282 + 4001GB Seagate ST4000VX016-3CV1 + 512GB Apacer AS350 512, Graphics: NVIDIA GeForce RTX 4070 SUPER 12GB, Audio: NVIDIA Device 22bc, Monitor: PHL 242V8, Network: Realtek RTL8111/8168/8211/8411
OS: Debian, Kernel: 6.6.15-amd64 (x86_64), Desktop: Xfce 4.18, Display Server: X Server 1.21.1.11, Display Driver: NVIDIA 550.54.15, OpenGL: 4.6.0, OpenCL: OpenCL 3.0 CUDA 12.4.89 + OpenCL 3.0 PoCL 5.0+debian Linux +Asserts RELOC SPIR LLVM 16.0.6 SLEEF DISTRO POCL_DEBUG, Compiler: GCC 13.2.0 + CUDA 12.0, File-System: ext4, Screen Resolution: 4480x1440
Kernel Notes: Transparent Huge Pages: alwaysProcessor Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa20120ePython Notes: Python 3.11.8Security Notes: 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: Vulnerable + spec_store_bypass: Vulnerable + spectre_v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers + spectre_v2: Vulnerable IBPB: disabled STIBP: disabled PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: ResNet-152 All options 4 8 12 16 20 SE +/- 0.12, N = 3 18.16 MIN: 14.49 / MAX: 18.57
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 All options 7 14 21 28 35 SE +/- 0.32, N = 5 28.47 MIN: 19.33 / MAX: 30.1
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 All options 7 14 21 28 35 SE +/- 0.29, N = 3 28.33 MIN: 19.28 / MAX: 29.17
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 All options 7 14 21 28 35 SE +/- 0.21, N = 10 27.77 MIN: 16.01 / MAX: 29.49
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: ResNet-152 All options 3 6 9 12 15 SE +/- 0.07, N = 3 11.47 MIN: 8.19 / MAX: 11.89
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 All options 7 14 21 28 35 SE +/- 0.22, N = 15 27.81 MIN: 12.38 / MAX: 30.18
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: ResNet-152 All options 3 6 9 12 15 SE +/- 0.02, N = 3 11.52 MIN: 9.36 / MAX: 11.86
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: ResNet-50 All options 7 14 21 28 35 SE +/- 0.32, N = 15 27.94 MIN: 16.81 / MAX: 29.62
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: ResNet-152 All options 3 6 9 12 15 SE +/- 0.10, N = 3 11.50 MIN: 8.81 / MAX: 11.87
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: ResNet-152 All options 3 6 9 12 15 SE +/- 0.02, N = 3 11.41 MIN: 8.11 / MAX: 11.89
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: ResNet-152 All options 3 6 9 12 15 SE +/- 0.10, N = 3 11.32 MIN: 7.82 / MAX: 12.05
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l All options 3 6 9 12 15 SE +/- 0.04, N = 3 10.98 MIN: 8.22 / MAX: 11.42
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l All options 2 4 6 8 10 SE +/- 0.01, N = 3 7.74 MIN: 5.95 / MAX: 7.85
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l All options 2 4 6 8 10 SE +/- 0.10, N = 3 7.61 MIN: 5.99 / MAX: 7.8
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l All options 2 4 6 8 10 SE +/- 0.05, N = 3 7.60 MIN: 5.08 / MAX: 7.86
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l All options 2 4 6 8 10 SE +/- 0.09, N = 3 7.50 MIN: 5.5 / MAX: 7.79
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l All options 2 4 6 8 10 SE +/- 0.07, N = 9 7.44 MIN: 4.46 / MAX: 7.85
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 All options 50 100 150 200 250 SE +/- 2.38, N = 3 212.31 MIN: 110.61 / MAX: 225.39
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 All options 20 40 60 80 100 SE +/- 0.73, N = 3 76.01 MIN: 44.66 / MAX: 78.91
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 All options 50 100 150 200 250 SE +/- 2.06, N = 3 214.58 MIN: 172.29 / MAX: 221
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 All options 50 100 150 200 250 SE +/- 0.92, N = 3 217.50 MIN: 172.82 / MAX: 221.5
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 All options 50 100 150 200 250 SE +/- 0.25, N = 3 217.22 MIN: 199.94 / MAX: 220.82
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 All options 16 32 48 64 80 SE +/- 0.08, N = 3 73.74 MIN: 43.03 / MAX: 79.24
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 All options 50 100 150 200 250 SE +/- 2.38, N = 4 212.89 MIN: 127.22 / MAX: 220.8
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 All options 20 40 60 80 100 SE +/- 0.91, N = 3 76.28 MIN: 39.08 / MAX: 78.37
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 All options 50 100 150 200 250 SE +/- 2.52, N = 3 215.10 MIN: 122.92 / MAX: 222.21
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 All options 20 40 60 80 100 SE +/- 0.79, N = 5 75.03 MIN: 50.37 / MAX: 78.56
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 All options 20 40 60 80 100 SE +/- 0.39, N = 3 76.76 MIN: 58.4 / MAX: 78.75
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 All options 20 40 60 80 100 SE +/- 0.08, N = 3 77.12 MIN: 69.63 / MAX: 78.12
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l All options 9 18 27 36 45 SE +/- 0.08, N = 3 40.73 MIN: 37.27 / MAX: 41.27
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l All options 9 18 27 36 45 SE +/- 0.12, N = 3 38.81 MIN: 33.4 / MAX: 40.1
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l All options 9 18 27 36 45 SE +/- 0.56, N = 3 39.03 MIN: 35.91 / MAX: 40.16
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l All options 9 18 27 36 45 SE +/- 0.29, N = 3 39.00 MIN: 36.15 / MAX: 40
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l All options 9 18 27 36 45 SE +/- 0.05, N = 3 39.65 MIN: 36.18 / MAX: 40.35
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l All options 9 18 27 36 45 SE +/- 0.41, N = 5 37.97 MIN: 24.02 / MAX: 39.94
All options Processor: AMD Ryzen 7 5700X 8-Core @ 3.40GHz (8 Cores / 16 Threads), Motherboard: MSI PRO B550M-P GEN3 (MS-7D95) v1.0 (1.60 BIOS), Chipset: AMD Starship/Matisse, Memory: 128GB, Disk: 256GB ARDOR GAMING m.2 NVME 256Gb AL1282 + 4001GB Seagate ST4000VX016-3CV1 + 512GB Apacer AS350 512, Graphics: NVIDIA GeForce RTX 4070 SUPER 12GB, Audio: NVIDIA Device 22bc, Monitor: PHL 242V8, Network: Realtek RTL8111/8168/8211/8411
OS: Debian, Kernel: 6.6.15-amd64 (x86_64), Desktop: Xfce 4.18, Display Server: X Server 1.21.1.11, Display Driver: NVIDIA 550.54.15, OpenGL: 4.6.0, OpenCL: OpenCL 3.0 CUDA 12.4.89 + OpenCL 3.0 PoCL 5.0+debian Linux +Asserts RELOC SPIR LLVM 16.0.6 SLEEF DISTRO POCL_DEBUG, Compiler: GCC 13.2.0 + CUDA 12.0, File-System: ext4, Screen Resolution: 4480x1440
Kernel Notes: Transparent Huge Pages: alwaysProcessor Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa20120ePython Notes: Python 3.11.8Security Notes: 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: Vulnerable + spec_store_bypass: Vulnerable + spectre_v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers + spectre_v2: Vulnerable IBPB: disabled STIBP: disabled PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected
Testing initiated at 30 March 2024 10:45 by user serge.