oneDNN MKL-DNN

This is a test of the Intel oneDNN (formerly DNNL / Deep Neural Network Library / MKL-DNN) 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.

Learn more about this test at the upstream project site: https://github.com/oneapi-src/oneDNN.

This utility test profile is maintained by Michael Larabel. This OpenBenchmarking.org test profile was created on 16 April 2019 and last updated on 17 June 2020. To run this test with the Phoronix Test Suite, the basic command is: phoronix-test-suite benchmark mkl-dnn.

This test has an average install time of 1 minute, 37 seconds and an average run-time of 8 minutes, 21 seconds.

20k+ Downloads

Installation StatsTest Completion StatsPublic Result UploadsOpenBenchmarking.orgEventsoneDNN MKL-DNN Popularitypts/mkl-dnn2019.042019.052019.062019.072019.082019.092019.102019.112019.122020.012020.022020.032020.042020.052020.062020.072020.082020.092020.102020.111100022000330004400055000

* Data based on those opting to upload their test results to OpenBenchmarking.org and users enabling the opt-in anonymous statistics reporting.

Deconvolution Batch deconv_3d19.7%Deconvolution Batch deconv_1d20.2%IP Batch All19.9%IP Batch 1D20.2%Recurrent Neural Network Training9.9%Recurrent Neural Network Inference10.1%Harness Option PopularityOpenBenchmarking.org
u8s8f3236.7%f3263.3%Data Type Option PopularityOpenBenchmarking.org

Revision History

pts/mkl-dnn-1.3.1   [View Source]   17 Jun 2020 16:31 EDT
NOTICE: The pts/mkl-dnn test profile has been succeeded by the pts/onednn test profile to match the naming convetion used by Intel following their renaming to oneDNN. Run pts/onednn test profile to use the latest version of Intel oneDNN benchmark

pts/mkl-dnn-1.3.0   [View Source]   09 Apr 2020 18:14 EDT
Fix as did not mean to expose f16 option.

pts/mkl-dnn-1.2.0   [View Source]   09 Apr 2020 18:10 EDT
Update against oneDNN 1.3 sources.

pts/mkl-dnn-1.1.1   [View Source]   03 Oct 2019 19:35 EDT
Update data types.

pts/mkl-dnn-1.1.0   [View Source]   03 Oct 2019 16:09 EDT
Update against MKL-DNN DNNL 1.1.

pts/mkl-dnn-1.0.2   [View Source]   18 Apr 2019 20:44 EDT
Fixes per reported test argument reporting and setting of OMP env vars.

pts/mkl-dnn-1.0.1   [View Source]   17 Apr 2019 09:20 EDT
MKLDNN_ARCH_OPT_FLAGS="-O3 $CXXFLAGS"

pts/mkl-dnn-1.0.0   [View Source]   16 Apr 2019 20:44 EDT
Initial commit of Intel MKL-DNN benchdnn benchmark.

Performance Metrics

Analyze Test Configuration:

oneDNN MKL-DNN 1.3

Harness: Deconvolution Batch deconv_1d - Data Type: f32

OpenBenchmarking.org metrics for this test profile configuration based on 150 public samples since 9 April 2020 with the latest data as of 15 November 2020.

Below is an overview of the generalized performance for components where there is sufficient statistically significant data based upon user-uploaded results. It is important to keep in mind particularly in the Linux/open-source space there can be vastly different OS configurations, with this overview intended to offer just general guidance as to the performance expectations.

OpenBenchmarking.orgDistribution Of Public Results - Harness: Deconvolution Batch deconv_1d - Data Type: f32150 Results Range From 1 To 303 ms11835526986103120137154171188205222239256273290307306090120150

Based on OpenBenchmarking.org data, the selected test / test configuration (oneDNN MKL-DNN 1.3 - Harness: Deconvolution Batch deconv_1d - Data Type: f32) has an average run-time of 2 minutes. By default this test profile is set to run at least 3 times but may increase if the standard deviation exceeds pre-defined defaults or other calculations deem additional runs necessary for greater statistical accuracy of the result.

OpenBenchmarking.orgMinutesTime Required To Complete BenchmarkHarness: Deconvolution Batch deconv_1d - Data Type: f32Run-Time246810Min: 1 / Avg: 1.44 / Max: 5

Based on public OpenBenchmarking.org results, the selected test / test configuration has an average standard deviation of 0.8%.

OpenBenchmarking.orgPercent, Fewer Is BetterAverage Deviation Between RunsHarness: Deconvolution Batch deconv_1d - Data Type: f32Deviation3691215Min: 0 / Avg: 0.77 / Max: 8

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