bits

bits

HTML result view exported from: https://openbenchmarking.org/result/2406302-NE-BITS6722089.

bitsProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelDisplay DriverOpenCLVulkanCompilerFile-SystemScreen ResolutionSystem LayerbitsIntel Xeon E5-2667 v3 (4 Cores / 8 Threads)Blade Shadow ShadowM v2.0 (1.1.3 BIOS)Intel 82G33/G31/P35/P31 + ICH91 x 12GB RAM-2400MT/s Blade R5NVNF8QVGPTY1-26C215GB QEMU HDDRed Hat QXL paravirtual graphic card 8GBRed Hat Virtio deviceUbuntu 22.045.15.0-113-generic (x86_64)NVIDIAOpenCL 3.0 CUDA 12.4.891.3.277GCC 11.4.0ext41280x800KVMOpenBenchmarking.org- Transparent Huge Pages: madvise- CPU Microcode: 0x49- Python 3.10.12- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Mitigation of PTE Inversion + mds: Mitigation of Clear buffers; SMT Host state unknown + meltdown: Mitigation of PTI + mmio_stale_data: Mitigation of Clear buffers; SMT Host state unknown + retbleed: Not affected + spec_rstack_overflow: Not affected + 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: conditional; RSB filling; PBRSB-eIBRS: Not affected; BHI: Retpoline + srbds: Not affected + tsx_async_abort: Not affected

bitspytorch: 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-50bits11.324.653.072.460.8833.238.9118.206.289.312.56OpenBenchmarking.org

PyTorch

Device: CPU - Batch Size: 16 - Model: ResNet-50

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50bits3691215SE +/- 0.05, N = 311.32MIN: 10.07 / MAX: 11.57

PyTorch

Device: CPU - Batch Size: 16 - Model: ResNet-152

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152bits1.04632.09263.13894.18525.2315SE +/- 0.01, N = 34.65MIN: 4.25 / MAX: 4.7

PyTorch

Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_lbits0.69081.38162.07242.76323.454SE +/- 0.02, N = 33.07MIN: 2.69 / MAX: 3.13

TensorFlow

Device: CPU - Batch Size: 16 - Model: VGG-16

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: VGG-16bits0.55351.1071.66052.2142.7675SE +/- 0.00, N = 32.46

TensorFlow

Device: GPU - Batch Size: 16 - Model: VGG-16

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: VGG-16bits0.1980.3960.5940.7920.99SE +/- 0.00, N = 30.88

TensorFlow

Device: CPU - Batch Size: 16 - Model: AlexNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: AlexNetbits816243240SE +/- 0.04, N = 333.23

TensorFlow

Device: GPU - Batch Size: 16 - Model: AlexNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: AlexNetbits246810SE +/- 0.01, N = 38.91

TensorFlow

Device: CPU - Batch Size: 16 - Model: GoogLeNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: GoogLeNetbits48121620SE +/- 0.04, N = 318.20

TensorFlow

Device: CPU - Batch Size: 16 - Model: ResNet-50

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: ResNet-50bits246810SE +/- 0.04, N = 36.28

TensorFlow

Device: GPU - Batch Size: 16 - Model: GoogLeNet

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: GoogLeNetbits3691215SE +/- 0.03, N = 39.31

TensorFlow

Device: GPU - Batch Size: 16 - Model: ResNet-50

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: ResNet-50bits0.5761.1521.7282.3042.88SE +/- 0.01, N = 32.56

Phoronix Test Suite v10.8.5