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 initiative.
To run this test with the Phoronix Test Suite , the basic command is: phoronix-test-suite benchmark onednn .
Test Created 17 June 2020
Last Updated 20 December 2020
Test Maintainer Michael Larabel
Test Type Processor
Average Install Time 8 Minutes, 59 Seconds
Average Run Time 2 Minutes, 2 Seconds
Accolades 5k+ Downloads Public Result Uploads Reported Installs* Test Completions* OpenBenchmarking.org Events oneDNN Popularity Statistics pts/onednn 2020.06 2020.07 2020.08 2020.09 2020.10 2020.11 2020.12 2021.01 4K 8K 12K 16K 20K
* Data based on those opting to upload their test results to OpenBenchmarking.org and users enabling the opt-in anonymous statistics reporting while running benchmarks from an Internet-connected platform. Data current as of Fri, 22 Jan 2021 18:34:28 GMT.
Deconvolution Batch shapes_3d 11.4% Recurrent Neural Network Training 15.9% IP Shapes 1D 11.5% Convolution Batch Shapes Auto 11.5% Matrix Multiply Batch Shapes Transformer 11.3% Recurrent Neural Network Inference 15.7% IP Shapes 3D 11.5% Deconvolution Batch shapes_1d 11.4% Harness Option Popularity OpenBenchmarking.org
bf16bf16bf16 14.0% u8s8f32 40.1% f32 45.8% Data Type Option Popularity OpenBenchmarking.org
Revision Historypts/onednn-1.6.1 [View Source ] Sun, 20 Dec 2020 09:58:16 GMT This test profile builds and works fine on macOS so enable it (MacOSX).
pts/onednn-1.6.0 [View Source ] Wed, 09 Dec 2020 13:47:31 GMT Update against oneDNN 2.0 upstream.
pts/onednn-1.5.0 [View Source ] Wed, 17 Jun 2020 16:26:39 GMT Initial commit of oneDNN test profile based on Intel oneDNN 1.5, forked from existing mkl-dnn test profile that is named from MKL-DNN before it was renamed to DNNL and then oneDNN. So create new test profile to match Intel naming convention.
Performance MetricsAnalyze Test Configuration: pts/onednn-1.6.x - oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Deconvolution Batch deconv_3d - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: IP Batch All - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Deconvolution Batch deconv_1d - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: IP Batch 1D - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: IP Batch 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: IP Batch All - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: IP Batch 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Deconvolution Batch deconv_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Deconvolution Batch deconv_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - oneDNN 1.5 - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU oneDNN 1.5 Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU OpenBenchmarking.org metrics for this test profile configuration based on 505 public results since 17 June 2020 with the latest data as of 8 December 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.
Component
Percentile Rank
# Matching Public Results
ms (Average)
OpenBenchmarking.org Distribution Of Public Results - Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU 505 Results Range From 4 To 3201 ms 4 68 132 196 260 324 388 452 516 580 644 708 772 836 900 964 1028 1092 1156 1220 1284 1348 1412 1476 1540 1604 1668 1732 1796 1860 1924 1988 2052 2116 2180 2244 2308 2372 2436 2500 2564 2628 2692 2756 2820 2884 2948 3012 3076 3140 3204 100 200 300 400 500
Based on OpenBenchmarking.org data, the selected test / test configuration (oneDNN 1.5 - Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU ) has an average run-time of 4 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.org Minutes Time Required To Complete Benchmark Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU Run-Time 3 6 9 12 15 Min: 1 / Avg: 3.1 / Max: 10
Based on public OpenBenchmarking.org results, the selected test / test configuration has an average standard deviation of 0.5% .
OpenBenchmarking.org Percent, Fewer Is Better Average Deviation Between Runs Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU Deviation 3 6 9 12 15 Min: 0 / Avg: 0.54 / Max: 12
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