gpu-server-eu-report AMD Ryzen Threadripper PRO 5955WX 16-Cores testing with a ASUS Pro WS WRX80E-SAGE SE WIFI II (1302 BIOS) and Gigabyte NVIDIA GeForce RTX 4090 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 2404028-NE-GPUSERVER82 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte Processor: AMD Ryzen Threadripper PRO 5955WX 16-Cores @ 4.00GHz (16 Cores / 32 Threads), Motherboard: ASUS Pro WS WRX80E-SAGE SE WIFI II (1302 BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 4001GB Western Digital WD_BLACK SN850X 4000GB, Graphics: Gigabyte NVIDIA GeForce RTX 4090 24GB, Audio: NVIDIA Device 22ba, Network: 2 x Intel 10G X550T + Intel Wi-Fi 6 AX210/AX211/AX411
OS: Ubuntu 22.04, Kernel: 6.5.0-26-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.4, Display Driver: NVIDIA, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 800x600
Kernel Notes: Transparent Huge Pages: madviseProcessor Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa008205Python Notes: Python 3.10.12Security 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: 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 + srbds: Not affected + tsx_async_abort: Not affected
PyTorch OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 0.46, N = 3 214.60 MIN: 182.08 / MAX: 216.79
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.18, N = 3 74.76 MIN: 68.57 / MAX: 75.86
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 0.25, N = 3 213.53 MIN: 197.82 / MAX: 215.82
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 0.36, N = 3 212.31 MIN: 199.16 / MAX: 214.97
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 1.41, N = 3 213.68 MIN: 126.84 / MAX: 218.74
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.44, N = 3 75.20 MIN: 68.59 / MAX: 76.29
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 0.90, N = 3 213.57 MIN: 202.47 / MAX: 217.01
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.59, N = 3 74.44 MIN: 67.59 / MAX: 76.29
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 50 100 150 200 250 SE +/- 0.09, N = 3 211.68 MIN: 201.79 / MAX: 214.18
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.43, N = 3 75.22 MIN: 68.96 / MAX: 76.24
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.36, N = 3 75.13 MIN: 68.59 / MAX: 76.23
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 20 40 60 80 100 SE +/- 0.54, N = 3 74.93 MIN: 68.82 / MAX: 76.5
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.36, N = 6 38.73 MIN: 34.49 / MAX: 39.57
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.17, N = 3 38.12 MIN: 34.52 / MAX: 38.69
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.46, N = 4 38.01 MIN: 34.07 / MAX: 38.96
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.28, N = 13 37.76 MIN: 33.85 / MAX: 39.14
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.10, N = 3 38.08 MIN: 34.82 / MAX: 38.49
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte 9 18 27 36 45 SE +/- 0.18, N = 3 38.27 MIN: 34.61 / MAX: 38.86
AMD Ryzen Threadripper PRO 5955WX 16-Cores - Gigabyte Processor: AMD Ryzen Threadripper PRO 5955WX 16-Cores @ 4.00GHz (16 Cores / 32 Threads), Motherboard: ASUS Pro WS WRX80E-SAGE SE WIFI II (1302 BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 4001GB Western Digital WD_BLACK SN850X 4000GB, Graphics: Gigabyte NVIDIA GeForce RTX 4090 24GB, Audio: NVIDIA Device 22ba, Network: 2 x Intel 10G X550T + Intel Wi-Fi 6 AX210/AX211/AX411
OS: Ubuntu 22.04, Kernel: 6.5.0-26-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.4, Display Driver: NVIDIA, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 800x600
Kernel Notes: Transparent Huge Pages: madviseProcessor Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa008205Python Notes: Python 3.10.12Security 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: 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 + srbds: Not affected + tsx_async_abort: Not affected
Testing initiated at 2 April 2024 12:40 by user gpu-server-eu.