TestRunNewCMake

Intel Pentium Gold G6400 testing with a ASRock H510M-HDV/M.2 SE (P1.60 BIOS) and Intel UHD 610 CML GT1 3GB on Ubuntu 20.04 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2311037-HERT-H510G6431

Jump To Table - Results

Intel UHD 610 CML GT1

Processor: Intel Pentium Gold G6400 @ 4.00GHz (2 Cores / 4 Threads), Motherboard: ASRock H510M-HDV/M.2 SE (P1.60 BIOS), Chipset: Intel Comet Lake PCH, Memory: 3584MB, Disk: 1000GB Western Digital WDS100T2B0A, Graphics: Intel UHD 610 CML GT1 3GB (1050MHz), Audio: Realtek ALC897, Monitor: G185BGEL01, Network: Realtek RTL8111/8168/8411

OS: Ubuntu 20.04, Kernel: 5.15.0-86-generic (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, OpenGL: 4.6 Mesa 21.2.6, Vulkan: 1.2.182, Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1368x768

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --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++,gm2 --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-9-9QDOt0/gcc-9-9.4.0/debian/tmp-nvptx/usr,hsa --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-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: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0xf8 - Thermald 1.9.1
Python Notes: Python 3.8.10
Security Notes: gather_data_sampling: Not affected + itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + 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 Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Mitigation of Microcode + tsx_async_abort: Not affected

Whisper.cpp

Whisper.cpp is a port of OpenAI's Whisper model in C/C++. Whisper.cpp is developed by Georgi Gerganov for transcribing WAV audio files to text / speech recognition. Whisper.cpp supports ARM NEON, x86 AVX, and other advanced CPU features. Learn more via the OpenBenchmarking.org test page.

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: SGDOneClassSVM

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status.

Benchmark: Isolation Forest

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status.

Benchmark: Plot Fast KMeans

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status.

Whisper.cpp

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.

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

PlaidML

This test profile uses PlaidML deep learning framework developed by Intel for offering up various benchmarks. Learn more via the OpenBenchmarking.org test page.

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Caffe

PlaidML

This test profile uses PlaidML deep learning framework developed by Intel for offering up various benchmarks. 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.

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

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.

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

NCNN

NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. 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.

Result

Inference Time Cost (ms)

Numenta Anomaly Benchmark

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

ONNX Runtime

Result

Inference Time Cost (ms)

Result

Inference Time Cost (ms)

Result

Inference Time Cost (ms)

Caffe

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

Benchmark: scikit_qda

Intel UHD 610 CML GT1: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

Benchmark: scikit_linearridgeregression

Intel UHD 610 CML GT1: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

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.

ONNX Runtime

Result

Inference Time Cost (ms)

Result

Inference Time Cost (ms)

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

ONNX Runtime

Result

Inference Time Cost (ms)

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: Isotonic / Pathological

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Benchmark: Isotonic / Logistic

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status.

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.

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: Isotonic / Perturbed Logarithm

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status.

Caffe

Numpy Benchmark

This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.

Caffe

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

oneDNN

Numenta Anomaly Benchmark

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Numenta Anomaly Benchmark

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

ONNX Runtime

Result

Inference Time Cost (ms)

Caffe

ONNX Runtime

Result

Inference Time Cost (ms)

Result

Inference Time Cost (ms)

Result

Inference Time Cost (ms)

Result

Inference Time Cost (ms)

Result

Inference Time Cost (ms)

OpenVINO

ONNX Runtime

Result

Inference Time Cost (ms)

Result

Inference Time Cost (ms)

Result

Inference Time Cost (ms)

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: Plot Non-Negative Matrix Factorization

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: KeyError:

OpenVINO

Numenta Anomaly Benchmark

OpenVINO

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.

OpenVINO

TensorFlow Lite

OpenVINO

TensorFlow Lite

OpenVINO

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

R Benchmark

This test is a quick-running survey of general R performance Learn more via the OpenBenchmarking.org test page.

Numenta Anomaly Benchmark

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

RNNoise

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.

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

oneDNN

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: RCV1 Logreg Convergencet

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: IndexError: list index out of range

oneDNN

OpenCV

This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.

Test: DNN - Deep Neural Network

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: [ERROR:[email protected]] global persistence.cpp:505 open Can't open file: '/opencv_extra-4.7.0/testdata/perf/dnn.xml' in read mode

oneDNN

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

oneDNN

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.

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

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Device: CPU - Batch Size: 256 - Model: ResNet-50

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Device: CPU - Batch Size: 256 - Model: AlexNet

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Device: CPU - Batch Size: 512 - Model: ResNet-50

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Device: CPU - Batch Size: 32 - Model: ResNet-50

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

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Device: CPU - Batch Size: 32 - Model: AlexNet

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Device: CPU - Batch Size: 256 - Model: VGG-16

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Device: CPU - Batch Size: 64 - Model: VGG-16

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

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Device: CPU - Batch Size: 32 - Model: VGG-16

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Device: CPU - Batch Size: 512 - Model: VGG-16

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Device: CPU - Batch Size: 512 - Model: AlexNet

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Device: CPU - Batch Size: 256 - Model: GoogLeNet

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Device: CPU - Batch Size: 64 - Model: ResNet-50

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

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Device: CPU - Batch Size: 512 - Model: GoogLeNet

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Device: CPU - Batch Size: 32 - Model: GoogLeNet

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Device: CPU - Batch Size: 64 - Model: AlexNet

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Device: CPU - Batch Size: 64 - Model: GoogLeNet

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Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: OSError: Neural Magic: Encountered exception while trying to read arch.bin: Unable to determine instruction set.

Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream

Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream

Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream

Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream

Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream

Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream

Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: Plot Parallel Pairwise

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: numpy.core._exceptions.MemoryError: Unable to allocate 74.5 GiB for an array with shape (100000, 100000) and data type float64

Neural Magic DeepSparse

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream

Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream

Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream

Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream

Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream

Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream

Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream

Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream

Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream

Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream

Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream

Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream

Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream

Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream

Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: Glmnet

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'glmnet.elastic_net'

OpenVINO

Model: Person Vehicle Bike Detection FP16 - Device: CPU

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status.

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.

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: TypeError: issubclass() arg 1 must be a class

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

Intel UHD 610 CML GT1: The test quit with a non-zero exit status.

DeepSpeech

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.

Acceleration: CPU

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ECP-CANDLE

Benchmark: P3B1

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Benchmark: P3B2

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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.

Intel UHD 610 CML GT1: The test quit with a non-zero exit status.

ONNX Runtime

Model: yolov4 - Device: CPU - Executor: Standard

Intel UHD 610 CML GT1: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: onnxruntime/onnxruntime/test/onnx/onnx_model_info.cc:45 void OnnxModelInfo::InitOnnxModelInfo(const PATH_CHAR_TYPE*) open file "yolov4/yolov4.onnx" failed: No such file or directory

Model: yolov4 - Device: CPU - Executor: Parallel

oneDNN

Harness: Deconvolution Batch shapes_1d - 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_3d - Data Type: bf16bf16bf16 - Engine: CPU

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Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU

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Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU

Intel UHD 610 CML GT1: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

187 Results Shown

Intel UHD 610 CML GT1

Testing initiated at 24 October 2023 10:41 by user hertz.

TestRunNewCMake

Statistics

Graph Settings

Multi-Way Comparison

Table

Run Management

Intel UHD 610 CML GT1

Whisper.cpp

Scikit-Learn

Whisper.cpp

Caffe

Scikit-Learn

PlaidML

Scikit-Learn

Caffe

PlaidML

Mobile Neural Network

Scikit-Learn

Numenta Anomaly Benchmark

Scikit-Learn

NCNN

ONNX Runtime

Numenta Anomaly Benchmark

Scikit-Learn

ONNX Runtime

Caffe

Scikit-Learn

Mlpack Benchmark

TNN

Mlpack Benchmark

Scikit-Learn

LeelaChessZero

ONNX Runtime

Scikit-Learn

ONNX Runtime

Scikit-Learn

oneDNN

Scikit-Learn

Caffe

Numpy Benchmark

Caffe

Scikit-Learn

oneDNN

Numenta Anomaly Benchmark

Scikit-Learn

Numenta Anomaly Benchmark

Scikit-Learn

Mlpack Benchmark

Scikit-Learn

ONNX Runtime

Caffe

ONNX Runtime

OpenVINO

ONNX Runtime

Scikit-Learn

OpenVINO

Numenta Anomaly Benchmark

OpenVINO

TensorFlow Lite

OpenVINO

TensorFlow Lite

OpenVINO

TensorFlow Lite

OpenVINO

Scikit-Learn

R Benchmark

Numenta Anomaly Benchmark

Mlpack Benchmark

RNNoise

TNN

oneDNN

Scikit-Learn

oneDNN

OpenCV

oneDNN

TNN

oneDNN

TensorFlow

Neural Magic DeepSparse

Scikit-Learn