tri

tri

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Performance Per
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  Duration
tri
July 01
  5 Hours, 48 Minutes
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tri

Processor: Intel Xeon E5-2667 v3 (4 Cores / 8 Threads), Motherboard: Blade Shadow ShadowM v2.0 (1.1.3 BIOS), Chipset: Intel 82G33/G31/P35/P31 + ICH9, Memory: 1 x 12GB RAM-2400MT/s Blade R5NVNF8QVGPTY1-26C, Disk: 215GB QEMU HDD, Graphics: Red Hat QXL paravirtual graphic card 8GB, Network: Red Hat Virtio device

OS: Ubuntu 22.04, Kernel: 5.15.0-113-generic (x86_64), Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.4.89, 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: 0x49
Python Notes: Python 3.10.12
Security Notes: 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

triOpenBenchmarking.orgPhoronix Test SuiteIntel 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 11.4.0ext41280x800KVMProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelDisplay DriverOpenCLVulkanCompilerFile-SystemScreen ResolutionSystem LayerTri BenchmarksSystem Logs- 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

triai-benchmark: Device AI Scoreai-benchmark: Device Training Scoreai-benchmark: Device Inference Scoretensorflow: 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-50tri11616115502.569.276.2618.339.0233.260.882.473.084.5911.16OpenBenchmarking.org

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 AI Scoretri20040060080010001161

OpenBenchmarking.orgScore, More Is BetterAI Benchmark Alpha 0.1.2Device Training Scoretri130260390520650611

OpenBenchmarking.orgScore, More Is BetterAI Benchmark Alpha 0.1.2Device Inference Scoretri120240360480600550

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-50tri0.5761.1521.7282.3042.88SE +/- 0.01, N = 32.56

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: GoogLeNettri3691215SE +/- 0.03, N = 39.27

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: ResNet-50tri246810SE +/- 0.03, N = 36.26

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: GoogLeNettri510152025SE +/- 0.02, N = 318.33

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: AlexNettri3691215SE +/- 0.02, N = 39.02

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: AlexNettri816243240SE +/- 0.02, N = 333.26

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: GPU - Batch Size: 16 - Model: VGG-16tri0.1980.3960.5940.7920.99SE +/- 0.00, N = 30.88

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.16.1Device: CPU - Batch Size: 16 - Model: VGG-16tri0.55581.11161.66742.22322.779SE +/- 0.00, N = 32.47

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_ltri0.6931.3862.0792.7723.465SE +/- 0.00, N = 33.08MIN: 2.92 / MAX: 3.1

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-152tri1.03282.06563.09844.13125.164SE +/- 0.02, N = 34.59MIN: 2.83 / MAX: 4.68

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.2.1Device: CPU - Batch Size: 16 - Model: ResNet-50tri3691215SE +/- 0.10, N = 311.16MIN: 7.91 / MAX: 11.48

tri

Processor: Intel Xeon E5-2667 v3 (4 Cores / 8 Threads), Motherboard: Blade Shadow ShadowM v2.0 (1.1.3 BIOS), Chipset: Intel 82G33/G31/P35/P31 + ICH9, Memory: 1 x 12GB RAM-2400MT/s Blade R5NVNF8QVGPTY1-26C, Disk: 215GB QEMU HDD, Graphics: Red Hat QXL paravirtual graphic card 8GB, Network: Red Hat Virtio device

OS: Ubuntu 22.04, Kernel: 5.15.0-113-generic (x86_64), Display Driver: NVIDIA, OpenCL: OpenCL 3.0 CUDA 12.4.89, 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: 0x49
Python Notes: Python 3.10.12
Security Notes: 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

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