test-pytorch Intel Xeon E5-2680 v4 testing with a MACHINIST X99-MR9A PRO MAX (5.11 BIOS) and NVIDIA GeForce RTX 3060 12GB 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 2404251-NE-TESTPYTOR08 teste-total Processor: Intel Xeon E5-2680 v4 @ 3.30GHz (14 Cores / 28 Threads), Motherboard: MACHINIST X99-MR9A PRO MAX (5.11 BIOS), Chipset: Intel Xeon E7 v4/Xeon, Memory: 32GB, Disk: 500GB KINGSTON SNV2S500G + 160GB MAXTOR STM316021 + 512GB P3-512, Graphics: NVIDIA GeForce RTX 3060 12GB, Audio: Realtek ALC897, Monitor: TV-PHILCO, Network: Realtek RTL8111/8168/8411
OS: Ubuntu 22.04, Kernel: 6.5.0-28-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.4, Display Driver: NVIDIA 535.171.04, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1920x1080
Kernel Notes: Transparent Huge Pages: madviseProcessor Notes: Scaling Governor: intel_cpufreq schedutil - CPU Microcode: 0xb000040Python Notes: Python 3.10.12Security Notes: gather_data_sampling: Not affected + itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Mitigation of PTE Inversion; VMX: conditional cache flushes SMT vulnerable + mds: Mitigation of Clear buffers; SMT vulnerable + meltdown: Mitigation of PTI + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + 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 Retpolines IBPB: conditional IBRS_FW STIBP: conditional RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Mitigation of Clear buffers; SMT vulnerable
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: ResNet-152 teste-total 3 6 9 12 15 SE +/- 0.04, N = 3 11.29 MIN: 8.37 / MAX: 12.03
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: ResNet-50 teste-total 5 10 15 20 25 SE +/- 0.23, N = 3 20.75 MIN: 15.96 / MAX: 21.78
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: ResNet-50 teste-total 5 10 15 20 25 SE +/- 0.19, N = 3 20.96 MIN: 17.26 / MAX: 22.23
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: ResNet-50 teste-total 5 10 15 20 25 SE +/- 0.19, N = 3 21.07 MIN: 16.2 / MAX: 22.19
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: ResNet-152 teste-total 2 4 6 8 10 SE +/- 0.03, N = 3 8.80 MIN: 6.41 / MAX: 9.14
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: ResNet-50 teste-total 5 10 15 20 25 SE +/- 0.15, N = 3 20.86 MIN: 15.67 / MAX: 22.29
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: ResNet-152 teste-total 2 4 6 8 10 SE +/- 0.03, N = 3 8.81 MIN: 8.42 / MAX: 9.02
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: ResNet-50 teste-total 5 10 15 20 25 SE +/- 0.16, N = 10 21.04 MIN: 14.62 / MAX: 23.22
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: ResNet-152 teste-total 2 4 6 8 10 SE +/- 0.01, N = 3 8.85 MIN: 7.34 / MAX: 9.06
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: ResNet-152 teste-total 2 4 6 8 10 SE +/- 0.04, N = 3 8.86 MIN: 7.22 / MAX: 9.09
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: ResNet-152 teste-total 2 4 6 8 10 SE +/- 0.03, N = 3 8.70 MIN: 6.36 / MAX: 8.91
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l teste-total 2 4 6 8 10 SE +/- 0.03, N = 3 6.66 MIN: 5.42 / MAX: 7.35
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l teste-total 1.1205 2.241 3.3615 4.482 5.6025 SE +/- 0.00, N = 3 4.98 MIN: 4.4 / MAX: 5.16
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l teste-total 1.116 2.232 3.348 4.464 5.58 SE +/- 0.03, N = 3 4.96 MIN: 4.58 / MAX: 5.17
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l teste-total 1.1363 2.2726 3.4089 4.5452 5.6815 SE +/- 0.02, N = 3 5.05 MIN: 4.2 / MAX: 5.22
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l teste-total 1.125 2.25 3.375 4.5 5.625 SE +/- 0.01, N = 3 5.00 MIN: 4.53 / MAX: 5.17
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l teste-total 1.107 2.214 3.321 4.428 5.535 SE +/- 0.05, N = 3 4.92 MIN: 4.17 / MAX: 5.11
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50 teste-total 20 40 60 80 100 SE +/- 0.46, N = 3 102.30 MIN: 92.86 / MAX: 105.67
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152 teste-total 8 16 24 32 40 SE +/- 0.38, N = 3 36.32 MIN: 34.01 / MAX: 37.76
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50 teste-total 20 40 60 80 100 SE +/- 1.02, N = 5 99.64 MIN: 82.77 / MAX: 104.87
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50 teste-total 20 40 60 80 100 SE +/- 0.89, N = 7 99.91 MIN: 76.59 / MAX: 106.68
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50 teste-total 20 40 60 80 100 SE +/- 0.73, N = 3 99.62 MIN: 89.42 / MAX: 102.62
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152 teste-total 8 16 24 32 40 SE +/- 0.16, N = 3 35.53 MIN: 33.45 / MAX: 36.51
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50 teste-total 20 40 60 80 100 SE +/- 0.32, N = 3 100.18 MIN: 91.43 / MAX: 102.65
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152 teste-total 8 16 24 32 40 SE +/- 0.43, N = 3 35.53 MIN: 32.04 / MAX: 37.12
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50 teste-total 20 40 60 80 100 SE +/- 1.18, N = 3 100.21 MIN: 89.37 / MAX: 104.69
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152 teste-total 8 16 24 32 40 SE +/- 0.35, N = 3 35.40 MIN: 32.64 / MAX: 36.8
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152 teste-total 8 16 24 32 40 SE +/- 0.33, N = 3 35.16 MIN: 32.29 / MAX: 36.53
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152 teste-total 8 16 24 32 40 SE +/- 0.28, N = 3 35.50 MIN: 32.84 / MAX: 36.69
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l teste-total 4 8 12 16 20 SE +/- 0.21, N = 3 18.22 MIN: 17.02 / MAX: 18.98
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l teste-total 4 8 12 16 20 SE +/- 0.06, N = 3 17.79 MIN: 16.76 / MAX: 18.24
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l teste-total 4 8 12 16 20 SE +/- 0.22, N = 3 17.87 MIN: 16.93 / MAX: 18.63
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l teste-total 4 8 12 16 20 SE +/- 0.06, N = 3 17.96 MIN: 17.08 / MAX: 18.37
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l teste-total 4 8 12 16 20 SE +/- 0.03, N = 3 17.87 MIN: 16.71 / MAX: 18.31
OpenBenchmarking.org batches/sec, More Is Better PyTorch 2.2.1 Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l teste-total 4 8 12 16 20 SE +/- 0.05, N = 3 18.05 MIN: 16.72 / MAX: 18.46
teste-total Processor: Intel Xeon E5-2680 v4 @ 3.30GHz (14 Cores / 28 Threads), Motherboard: MACHINIST X99-MR9A PRO MAX (5.11 BIOS), Chipset: Intel Xeon E7 v4/Xeon, Memory: 32GB, Disk: 500GB KINGSTON SNV2S500G + 160GB MAXTOR STM316021 + 512GB P3-512, Graphics: NVIDIA GeForce RTX 3060 12GB, Audio: Realtek ALC897, Monitor: TV-PHILCO, Network: Realtek RTL8111/8168/8411
OS: Ubuntu 22.04, Kernel: 6.5.0-28-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.4, Display Driver: NVIDIA 535.171.04, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1920x1080
Kernel Notes: Transparent Huge Pages: madviseProcessor Notes: Scaling Governor: intel_cpufreq schedutil - CPU Microcode: 0xb000040Python Notes: Python 3.10.12Security Notes: gather_data_sampling: Not affected + itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Mitigation of PTE Inversion; VMX: conditional cache flushes SMT vulnerable + mds: Mitigation of Clear buffers; SMT vulnerable + meltdown: Mitigation of PTI + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + 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 Retpolines IBPB: conditional IBRS_FW STIBP: conditional RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Mitigation of Clear buffers; SMT vulnerable
Testing initiated at 25 April 2024 15:32 by user pablue.