tri

tri

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2407017-NE-TRI91491891
Jump To Table - Results

Statistics

Remove Outliers Before Calculating Averages

Graph Settings

Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
tri
July 01
  5 Hours, 25 Minutes
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):

tri

Processor: AMD EPYC 7543P 32-Core (4 Cores / 8 Threads), Motherboard: Blade Shadow ShadowM v2.0 (1.1.3 BIOS), Chipset: Intel 82G33/G31/P35/P31 + ICH9, Memory: 1 x 16GB RAM-2400MT/s Blade 2MEVPUH6DW9W57-PYL, Disk: 215GB QEMU HDD, Graphics: Red Hat QXL paravirtual graphic card 20GB, Network: Red Hat Virtio device

OS: Ubuntu 22.04, Kernel: 5.15.0-113-generic (x86_64), Display Server: X Server, Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.4.131, Vulkan: 1.3.277, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1280x800, System Layer: KVM

Kernel Notes: Transparent Huge Pages: madvise
Processor Notes: CPU Microcode: 0xa0011d1
Python Notes: Python 3.10.12
Security 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: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + 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

triOpenBenchmarking.orgPhoronix Test SuiteAMD EPYC 7543P 32-Core (4 Cores / 8 Threads)Blade Shadow ShadowM v2.0 (1.1.3 BIOS)Intel 82G33/G31/P35/P31 + ICH91 x 16GB RAM-2400MT/s Blade 2MEVPUH6DW9W57-PYL215GB QEMU HDDRed Hat QXL paravirtual graphic card 20GBRed Hat Virtio deviceUbuntu 22.045.15.0-113-generic (x86_64)X ServerNVIDIAOpenCL 3.0 CUDA 12.4.1311.3.277GCC 11.4.0ext41280x800KVMProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelDisplay ServerDisplay DriverOpenCLVulkanCompilerFile-SystemScreen ResolutionSystem LayerTri BenchmarksSystem Logs- Transparent Huge Pages: madvise- CPU Microcode: 0xa0011d1- 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: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + 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

tripytorch: CPU - 16 - ResNet-50pytorch: CPU - 16 - ResNet-152pytorch: CPU - 16 - Efficientnet_v2_ltensorflow: CPU - 16 - VGG-16tensorflow: GPU - 16 - VGG-16tensorflow: CPU - 16 - AlexNettensorflow: GPU - 16 - AlexNettensorflow: CPU - 16 - GoogLeNettensorflow: CPU - 16 - ResNet-50tensorflow: GPU - 16 - GoogLeNettensorflow: GPU - 16 - ResNet-50ai-benchmark: Device Inference Scoreai-benchmark: Device Training Scoreai-benchmark: Device AI Scoretri14.815.763.883.251.1444.2214.4425.548.5311.623.407168141530OpenBenchmarking.org

PyTorch

This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50tri48121620SE +/- 0.03, N = 314.81MIN: 14.22 / MAX: 15.13

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152tri1.2962.5923.8885.1846.48SE +/- 0.04, N = 35.76MIN: 5.11 / MAX: 5.88

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_ltri0.8731.7462.6193.4924.365SE +/- 0.05, N = 93.88MIN: 3.53 / MAX: 4.09

TensorFlow

This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: VGG-16tri0.73131.46262.19392.92523.6565SE +/- 0.00, N = 33.25

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: VGG-16tri0.25650.5130.76951.0261.2825SE +/- 0.00, N = 31.14

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: AlexNettri1020304050SE +/- 0.04, N = 344.22

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: AlexNettri48121620SE +/- 0.01, N = 314.44

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: GoogLeNettri612182430SE +/- 0.06, N = 325.54

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: ResNet-50tri246810SE +/- 0.03, N = 38.53

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: GoogLeNettri3691215SE +/- 0.06, N = 311.62

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: ResNet-50tri0.7651.532.2953.063.825SE +/- 0.01, N = 33.40

AI Benchmark Alpha

AI Benchmark Alpha is a Python library for evaluating artificial intelligence (AI) performance on diverse hardware platforms and relies upon the TensorFlow machine learning library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterAI Benchmark Alpha 0.1.2Device Inference Scoretri150300450600750716

OpenBenchmarking.orgScore, More Is BetterAI Benchmark Alpha 0.1.2Device Training Scoretri2004006008001000814

OpenBenchmarking.orgScore, More Is BetterAI Benchmark Alpha 0.1.2Device AI Scoretri300600900120015001530

tri

Processor: AMD EPYC 7543P 32-Core (4 Cores / 8 Threads), Motherboard: Blade Shadow ShadowM v2.0 (1.1.3 BIOS), Chipset: Intel 82G33/G31/P35/P31 + ICH9, Memory: 1 x 16GB RAM-2400MT/s Blade 2MEVPUH6DW9W57-PYL, Disk: 215GB QEMU HDD, Graphics: Red Hat QXL paravirtual graphic card 20GB, Network: Red Hat Virtio device

OS: Ubuntu 22.04, Kernel: 5.15.0-113-generic (x86_64), Display Server: X Server, Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.4.131, Vulkan: 1.3.277, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1280x800, System Layer: KVM

Kernel Notes: Transparent Huge Pages: madvise
Processor Notes: CPU Microcode: 0xa0011d1
Python Notes: Python 3.10.12
Security 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: Mitigation of safe RET + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + 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

Testing initiated at 1 July 2024 02:54 by user ubuntu.