f33-machine-learning

Intel Core i7-6700 testing with a ASUS Z170I PRO GAMING (0806 BIOS) and Intel HD 530 3GB on Fedora 33 via the Phoronix Test Suite.

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Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
dtdesk.z170.16GB.nvme
December 01 2020
  13 Hours, 24 Minutes
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f33-machine-learningOpenBenchmarking.orgPhoronix Test Suite 10.8.4Intel Core i7-6700 @ 4.00GHz (4 Cores / 8 Threads)ASUS Z170I PRO GAMING (0806 BIOS)Intel Xeon E3-1200 v5/E3-150016384MBSamsung SSD 950 PRO 512GB + 3001GB Western Digital WD30EFRX-68E + 5001GB Seagate ST5000DM000-1FK1Intel HD 530 3GB (1150MHz)Realtek ALC1150DELL ST2220TIntel I219-V + Qualcomm Atheros QCA6174 802.11acFedora 335.8.16-300.fc33.x86_64 (x86_64)Cinnamon 4.6.7X Server 1.20.8modesetting 1.20.84.6 Mesa 20.2.11.2.145Clang 11.0.0 + LLVM 11.0.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLVulkanCompilerFile-SystemScreen ResolutionF33-machine-learning BenchmarksSystem Logs- --build=x86_64-redhat-linux --disable-libunwind-exceptions --enable-__cxa_atexit --enable-bootstrap --enable-cet --enable-checking=release --enable-gnu-indirect-function --enable-gnu-unique-object --enable-initfini-array --enable-languages=c,c++,fortran,objc,obj-c++,ada,go,d,lto --enable-multilib --enable-offload-targets=nvptx-none --enable-plugin --enable-shared --enable-threads=posix --mandir=/usr/share/man --with-arch_32=i686 --with-gcc-major-version-only --with-isl --with-linker-hash-style=gnu --with-tune=generic --without-cuda-driver - Scaling Governor: intel_pstate powersave- Python 3.9.0- SELinux + itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Mitigation of PTE Inversion; VMX: conditional cache flushes SMT vulnerable + mds: Mitigation of Clear buffers; SMT vulnerable + meltdown: Mitigation of PTI + 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 Full generic retpoline IBPB: conditional IBRS_FW STIBP: conditional RSB filling + srbds: Vulnerable: No microcode + tsx_async_abort: Mitigation of Clear buffers; SMT vulnerable

f33-machine-learningonednn: IP Batch 1D - f32 - CPUonednn: IP Batch All - f32 - CPUonednn: IP Batch 1D - u8s8f32 - CPUonednn: IP Batch All - u8s8f32 - CPUonednn: Convolution Batch Shapes Auto - f32 - CPUonednn: Deconvolution Batch deconv_1d - f32 - CPUonednn: Deconvolution Batch deconv_3d - f32 - CPUonednn: Convolution Batch Shapes Auto - u8s8f32 - CPUonednn: Deconvolution Batch deconv_1d - u8s8f32 - CPUonednn: Deconvolution Batch deconv_3d - u8s8f32 - CPUonednn: Recurrent Neural Network Training - f32 - CPUonednn: Recurrent Neural Network Inference - f32 - CPUonednn: Matrix Multiply Batch Shapes Transformer - f32 - CPUonednn: Matrix Multiply Batch Shapes Transformer - u8s8f32 - CPUnumpy: deepspeech: CPUrbenchmark: rnnoise: tensorflow-lite: SqueezeNettensorflow-lite: Inception V4tensorflow-lite: NASNet Mobiletensorflow-lite: Mobilenet Floattensorflow-lite: Mobilenet Quanttensorflow-lite: Inception ResNet V2mnn: SqueezeNetV1.0mnn: resnet-v2-50mnn: MobileNetV2_224mnn: mobilenet-v1-1.0mnn: inception-v3plaidml: No - Inference - VGG16 - CPUplaidml: No - Inference - ResNet 50 - CPUnumenta-nab: EXPoSEnumenta-nab: Relative Entropynumenta-nab: Windowed Gaussiannumenta-nab: Earthgecko Skylinenumenta-nab: Bayesian Changepointmlpack: scikit_icamlpack: scikit_qdamlpack: scikit_svmmlpack: scikit_linearridgeregressionscikit-learn: dtdesk.z170.16GB.nvme10.33140.284.0158.4624.039.7314.9822.1910.997.97686.12416.016.9111.09280.0490.200.220630.064958657154893394954343014352165648252713.5072.627.6810.7382.566.462.942763.4536.1225.19277.5775.2848.9095.6628.544.0410.79OpenBenchmarking.org

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 oneAPI initiative. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: IP Batch 1D - Data Type: f32 - Engine: CPUdtdesk.z170.16GB.nvme3691215SE +/- 0.59, N = 1510.33MIN: 7.631. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: IP Batch All - Data Type: f32 - Engine: CPUdtdesk.z170.16GB.nvme306090120150SE +/- 5.15, N = 12140.28MIN: 111.171. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: IP Batch 1D - Data Type: u8s8f32 - Engine: CPUdtdesk.z170.16GB.nvme0.90231.80462.70693.60924.5115SE +/- 0.06, N = 44.01MIN: 3.561. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPUdtdesk.z170.16GB.nvme1326395265SE +/- 1.87, N = 1558.46MIN: 48.591. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPUdtdesk.z170.16GB.nvme612182430SE +/- 0.16, N = 324.03MIN: 21.771. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Deconvolution Batch deconv_1d - Data Type: f32 - Engine: CPUdtdesk.z170.16GB.nvme3691215SE +/- 0.03, N = 39.73MIN: 8.941. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Deconvolution Batch deconv_3d - Data Type: f32 - Engine: CPUdtdesk.z170.16GB.nvme48121620SE +/- 0.20, N = 714.98MIN: 13.741. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPUdtdesk.z170.16GB.nvme510152025SE +/- 0.01, N = 322.19MIN: 20.221. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32 - Engine: CPUdtdesk.z170.16GB.nvme3691215SE +/- 0.11, N = 310.99MIN: 9.91. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32 - Engine: CPUdtdesk.z170.16GB.nvme246810SE +/- 0.01, N = 37.97MIN: 7.221. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPUdtdesk.z170.16GB.nvme150300450600750SE +/- 11.12, N = 15686.12MIN: 580.191. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPUdtdesk.z170.16GB.nvme90180270360450SE +/- 27.67, N = 15416.01MIN: 301.891. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPUdtdesk.z170.16GB.nvme246810SE +/- 0.29, N = 156.91MIN: 5.361. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 1.5Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPUdtdesk.z170.16GB.nvme3691215SE +/- 1.00, N = 1211.09MIN: 7.481. (CXX) g++ options: -O3 -march=native -std=c++11 -fopenmp -msse4.1 -fPIC -O2 -pie -lpthread -ldl

Numpy Benchmark

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

OpenBenchmarking.orgScore, More Is BetterNumpy Benchmarkdtdesk.z170.16GB.nvme60120180240300SE +/- 4.71, N = 3280.04

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.

OpenBenchmarking.orgSeconds, Fewer Is BetterDeepSpeech 0.6Acceleration: CPUdtdesk.z170.16GB.nvme20406080100SE +/- 0.83, N = 1490.20

R Benchmark

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

OpenBenchmarking.orgSeconds, Fewer Is BetterR Benchmarkdtdesk.z170.16GB.nvme0.04960.09920.14880.19840.248SE +/- 0.0024, N = 50.22061. R scripting front-end version 4.0.3 (2020-10-10)

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.

OpenBenchmarking.orgSeconds, Fewer Is BetterRNNoise 2020-06-28dtdesk.z170.16GB.nvme714212835SE +/- 0.53, N = 330.061. (CC) gcc options: -O2 -pedantic -fvisibility=hidden -lm

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation. 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.

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: SqueezeNetdtdesk.z170.16GB.nvme110K220K330K440K550KSE +/- 407.81, N = 3495865

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception V4dtdesk.z170.16GB.nvme1.5M3M4.5M6M7.5MSE +/- 15634.58, N = 37154893

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: NASNet Mobiledtdesk.z170.16GB.nvme80K160K240K320K400KSE +/- 6491.79, N = 3394954

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet Floatdtdesk.z170.16GB.nvme70K140K210K280K350KSE +/- 3468.50, N = 3343014

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet Quantdtdesk.z170.16GB.nvme80K160K240K320K400KSE +/- 1848.22, N = 3352165

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception ResNet V2dtdesk.z170.16GB.nvme1.4M2.8M4.2M5.6M7MSE +/- 7598.18, N = 36482527

Mobile Neural Network

MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by ALibaba. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2020-09-17Model: SqueezeNetV1.0dtdesk.z170.16GB.nvme3691215SE +/- 0.71, N = 913.50MIN: 9.54 / MAX: 110.181. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2020-09-17Model: resnet-v2-50dtdesk.z170.16GB.nvme1632486480SE +/- 1.66, N = 972.62MIN: 52.94 / MAX: 340.571. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2020-09-17Model: MobileNetV2_224dtdesk.z170.16GB.nvme246810SE +/- 0.44, N = 97.68MIN: 5.51 / MAX: 106.771. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2020-09-17Model: mobilenet-v1-1.0dtdesk.z170.16GB.nvme3691215SE +/- 0.53, N = 910.73MIN: 7.21 / MAX: 149.951. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2020-09-17Model: inception-v3dtdesk.z170.16GB.nvme20406080100SE +/- 2.83, N = 982.56MIN: 59.28 / MAX: 381.551. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

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.

OpenBenchmarking.orgFPS, More Is BetterPlaidMLFP16: No - Mode: Inference - Network: VGG16 - Device: CPUdtdesk.z170.16GB.nvme246810SE +/- 0.12, N = 36.46

OpenBenchmarking.orgFPS, More Is BetterPlaidMLFP16: No - Mode: Inference - Network: ResNet 50 - Device: CPUdtdesk.z170.16GB.nvme0.66151.3231.98452.6463.3075SE +/- 0.01, N = 32.94

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 timeseries 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.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: EXPoSEdtdesk.z170.16GB.nvme6001200180024003000SE +/- 57.30, N = 92763.45

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative Entropydtdesk.z170.16GB.nvme816243240SE +/- 0.30, N = 336.12

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed Gaussiandtdesk.z170.16GB.nvme612182430SE +/- 0.34, N = 325.19

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko Skylinedtdesk.z170.16GB.nvme60120180240300SE +/- 1.40, N = 3277.57

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian Changepointdtdesk.z170.16GB.nvme20406080100SE +/- 0.49, N = 375.28

Mlpack Benchmark

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

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_icadtdesk.z170.16GB.nvme1122334455SE +/- 0.13, N = 348.90

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_qdadtdesk.z170.16GB.nvme20406080100SE +/- 0.50, N = 395.66

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_svmdtdesk.z170.16GB.nvme714212835SE +/- 0.01, N = 328.54

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_linearridgeregressiondtdesk.z170.16GB.nvme0.9091.8182.7273.6364.545SE +/- 0.00, N = 34.04

Scikit-Learn

Scikit-learn is a Python module for machine learning Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 0.22.1dtdesk.z170.16GB.nvme3691215SE +/- 0.01, N = 310.79