Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2303168-NE-MLRESULTS39
Kernel Notes: Transparent Huge Pages: madvise Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-xKiWfi/gcc-11-11.3.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v Processor Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - Platform Profile: balanced - CPU Microcode: 0xa404102 - ACPI Profile: balanced Graphics Notes: BAR1 / Visible vRAM Size: 512 MB - vBIOS Version: SWBRT95123.001 Python Notes: Python 3.10.6 Security Notes: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: 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: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected
SHOC Scalable HeterOgeneous Computing
The CUDA and OpenCL version of Vetter's Scalable HeterOgeneous Computing benchmark suite. SHOC provides a number of different benchmark programs for evaluating the performance and stability of compute devices. Learn more via the OpenBenchmarking.org test page.
Target: OpenCL - Benchmark: S3D
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Target: OpenCL - Benchmark: Triad
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Target: OpenCL - Benchmark: FFT SP
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Target: OpenCL - Benchmark: MD5 Hash
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Target: OpenCL - Benchmark: Reduction
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Target: OpenCL - Benchmark: GEMM SGEMM_N
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Target: OpenCL - Benchmark: Max SP Flops
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Target: OpenCL - Benchmark: Bus Speed Download
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Target: OpenCL - Benchmark: Bus Speed Readback
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LeelaChessZero
LeelaChessZero (lc0 / lczero) is a chess engine automated vian neural networks. This test profile can be used for OpenCL, CUDA + cuDNN, and BLAS (CPU-based) benchmarking. Learn more via the OpenBenchmarking.org test page.
oneDNN
This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
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Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
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Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
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Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
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Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
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Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
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Mozilla DeepSpeech is a speech-to-text engine powered by TensorFlow for machine learning and derived from Baidu's Deep Speech research paper. This test profile times the speech-to-text process for a roughly three minute audio recording. Learn more via the OpenBenchmarking.org test page.
RNNoise is a recurrent neural network for audio noise reduction developed by Mozilla and Xiph.Org. This test profile is a single-threaded test measuring the time to denoise a sample 26 minute long 16-bit RAW audio file using this recurrent neural network noise suppression library. Learn more via the OpenBenchmarking.org test page.
TensorFlow Lite
This is a benchmark of the TensorFlow Lite implementation focused on TensorFlow machine learning for mobile, IoT, edge, and other cases. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.
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 too. Learn more via the OpenBenchmarking.org test page.
Device: CPU - Batch Size: 256 - Model: VGG-16
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Device: CPU - Batch Size: 512 - Model: VGG-16
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Device: CPU - Batch Size: 256 - Model: ResNet-50
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Device: CPU - Batch Size: 512 - Model: ResNet-50
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Neural Magic DeepSparse
This is a benchmark of Neural Magic's DeepSparse using its built-in deepsparse.benchmark utility and various models from their SparseZoo (https://sparsezoo.neuralmagic.com/). Learn more via the OpenBenchmarking.org test page.
spaCy
The spaCy library is an open-source solution for advanced neural language processing (NLP). The spaCy library leverages Python and is a leading neural language processing solution. This test profile times the spaCy CPU performance with various models. Learn more via the OpenBenchmarking.org test page.
Caffe
This is a benchmark of the Caffe deep learning framework and currently supports the AlexNet and Googlenet model and execution on both CPUs and NVIDIA GPUs. Learn more via the OpenBenchmarking.org test page.
Mobile Neural Network
MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. This MNN test profile is building the OpenMP / CPU threaded version for processor benchmarking and not any GPU-accelerated test. MNN does allow making use of AVX-512 extensions. Learn more via the OpenBenchmarking.org test page.
FP16: No - Mode: Inference - Network: VGG16 - Device: CPU
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FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU
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OpenVINO
This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.
ECP-CANDLE
The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales. Learn more via the OpenBenchmarking.org test page.
Benchmark: P1B2
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Benchmark: P3B1
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Benchmark: P3B2
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Numenta Anomaly Benchmark
Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.
ONNX Runtime
ONNX Runtime is developed by Microsoft and partners as a open-source, cross-platform, high performance machine learning inferencing and training accelerator. This test profile runs the ONNX Runtime with various models available from the ONNX Model Zoo. Learn more via the OpenBenchmarking.org test page.
Model: GPT-2 - Device: CPU - Executor: Parallel
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Model: GPT-2 - Device: CPU - Executor: Standard
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Model: yolov4 - Device: CPU - Executor: Parallel
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Model: yolov4 - Device: CPU - Executor: Standard
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Model: bertsquad-12 - Device: CPU - Executor: Parallel
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Model: bertsquad-12 - Device: CPU - Executor: Standard
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Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel
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Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard
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Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel
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Model: fcn-resnet101-11 - Device: CPU - Executor: Standard
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Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel
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Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard
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Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel
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Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard
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Model: super-resolution-10 - Device: CPU - Executor: Parallel
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Model: super-resolution-10 - Device: CPU - Executor: Standard
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Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel
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Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard
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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.