bits

bits

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2406302-NE-BITS3822089
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Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
bits
June 30
  4 Hours, 11 Minutes
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bitsOpenBenchmarking.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 1IE18UKJRN5SEN-HKA215GB QEMU HDDRed Hat QXL paravirtual graphic card 20GBRed Hat Virtio deviceUbuntu 22.045.15.0-113-generic (x86_64)NVIDIAOpenCL 3.0 CUDA 12.4.891.3.277GCC 11.4.0ext41280x800KVMProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelDisplay DriverOpenCLVulkanCompilerFile-SystemScreen ResolutionSystem LayerBits PerformanceSystem 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

bitstensorflow: GPU - 16 - ResNet-50tensorflow: GPU - 16 - GoogLeNettensorflow: CPU - 16 - ResNet-50tensorflow: CPU - 16 - GoogLeNettensorflow: GPU - 16 - AlexNettensorflow: CPU - 16 - AlexNettensorflow: GPU - 16 - VGG-16tensorflow: CPU - 16 - VGG-16pytorch: CPU - 16 - Efficientnet_v2_lpytorch: CPU - 16 - ResNet-152pytorch: CPU - 16 - ResNet-50bits3.4411.758.4225.5914.4143.771.143.244.115.8515.16OpenBenchmarking.org

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: GPU - Batch Size: 16 - Model: ResNet-50bits0.7741.5482.3223.0963.87SE +/- 0.02, N = 33.44

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: GoogLeNetbits3691215SE +/- 0.01, N = 311.75

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: ResNet-50bits246810SE +/- 0.04, N = 38.42

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: GoogLeNetbits612182430SE +/- 0.05, N = 325.59

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: AlexNetbits48121620SE +/- 0.00, N = 314.41

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: AlexNetbits1020304050SE +/- 0.14, N = 343.77

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: VGG-16bits0.25650.5130.76951.0261.2825SE +/- 0.00, N = 31.14

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: VGG-16bits0.7291.4582.1872.9163.645SE +/- 0.00, N = 33.24

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: Efficientnet_v2_lbits0.92481.84962.77443.69924.624SE +/- 0.05, N = 94.11MIN: 3.73 / MAX: 4.36

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152bits1.31632.63263.94895.26526.5815SE +/- 0.04, N = 35.85MIN: 5.18 / MAX: 5.94

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50bits48121620SE +/- 0.18, N = 315.16MIN: 13.27 / MAX: 15.71