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.
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 12 October 2023
Test Type Processor
Average Install Time 5 Minutes, 8 Seconds
Average Run Time 2 Minutes, 28 Seconds
Test Dependencies C/C++ Compiler Toolchain + CMake
Accolades 60k+ Downloads Public Result Uploads * Reported Installs ** Reported Test Completions ** Test Profile Page Views *** OpenBenchmarking.org Events oneDNN Popularity Statistics pts/onednn 2020.06 2020.07 2020.08 2020.09 2020.10 2020.11 2020.12 2021.01 2021.02 2021.03 2021.04 2021.05 2021.06 2021.07 2021.08 2021.09 2021.10 2021.11 2021.12 2022.01 2022.02 2022.03 2022.04 2022.05 2022.06 2022.07 2022.08 2022.09 2022.10 2022.11 2022.12 2023.01 2023.02 2023.03 2023.04 2023.05 2023.06 2023.07 2023.08 2023.09 2023.10 2023.11 20K 40K 60K 80K 100K
* Uploading of benchmark result data to OpenBenchmarking.org is always optional (opt-in) via the Phoronix Test Suite for users wishing to share their results publicly. ** 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. *** Test profile page view reporting began March 2021. Data updated weekly as of 20 November 2023.
IP Shapes 1D 14.2% Deconvolution Batch shapes_1d 10.7% Convolution Batch Shapes Auto 10.8% Recurrent Neural Network Inference 19.0% Deconvolution Batch shapes_3d 10.7% IP Shapes 3D 15.4% Recurrent Neural Network Training 19.1% Harness Option Popularity OpenBenchmarking.org
bf16bf16bf16 35.9% f32 37.8% u8s8f32 26.3% Data Type Option Popularity OpenBenchmarking.org
Revision Historypts/onednn-3.3.0 [View Source ] Thu, 12 Oct 2023 11:14:07 GMT Update against oneDNN 3.3 upstream.
pts/onednn-3.1.0 [View Source ] Fri, 31 Mar 2023 18:14:37 GMT Update against oneDNN 3.1 upstream.
pts/onednn-3.0.0 [View Source ] Mon, 19 Dec 2022 21:07:39 GMT Update against oneDNN 3.0 upstream.
pts/onednn-2.7.0 [View Source ] Wed, 28 Sep 2022 13:00:44 GMT Update against oneDNN 2.7 upstream.
pts/onednn-1.8.0 [View Source ] Tue, 29 Mar 2022 19:55:25 GMT Update against oneDNN 2.6 upstream.
pts/onednn-1.7.0 [View Source ] Sat, 13 Mar 2021 07:49:33 GMT Update against oneDNN 2.1.2 upstream.
pts/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-3.3.x - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.x - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.x - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-3.3.x - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-3.3.x - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-3.3.x - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-3.3.x - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.x - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.x - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.x - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.x - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.x - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.x - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.x - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.x - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.x - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-3.3.x - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-3.3.x - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-3.3.x - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.x - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.x - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.x - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-3.1.x - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-3.1.x - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-3.1.x - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-3.1.x - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-3.1.x - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-3.1.x - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.x - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-3.1.x - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.x - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.x - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.x - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.x - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.x - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.x - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.x - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.x - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.x - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.x - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.x - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.x - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.x - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.x - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.x - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-3.0.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-3.0.x - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-3.0.x - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-3.0.x - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-3.0.x - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-3.0.x - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-3.0.x - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.x - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.x - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-3.0.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.x - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.x - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.x - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.x - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.x - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.x - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.x - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.x - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.x - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.x - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.x - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-2.7.x - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.x - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-2.7.x - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-2.7.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-2.7.x - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-2.7.x - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-2.7.x - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-2.7.x - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-2.7.x - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.x - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.x - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.x - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.x - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.x - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.x - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.x - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-2.7.x - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.x - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.x - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.x - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.x - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.8.x - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.8.x - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.8.x - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.8.x - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.8.x - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.8.x - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.8.x - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-1.8.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.8.x - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.8.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.8.x - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.8.x - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-1.8.x - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-1.8.x - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.8.x - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-1.8.x - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.8.x - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.8.x - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.8.x - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.8.x - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.8.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.8.x - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.8.x - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.8.x - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.x - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.7.x - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.x - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.7.x - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-1.7.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.x - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.7.x - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-1.7.x - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-1.7.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.7.x - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.x - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.x - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.x - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-1.7.x - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.x - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.x - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.x - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.x - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.7.x - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.x - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.x - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.x - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.x - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.7.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-1.6.x - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.x - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - Harness: IP Batch All - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - Harness: Deconvolution Batch deconv_3d - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - Harness: Deconvolution Batch deconv_1d - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - Harness: IP Batch 1D - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - Harness: IP Batch All - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.5.x - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - Harness: IP Batch 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.5.x - Harness: IP Batch All - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - Harness: IP Batch 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - Harness: Deconvolution Batch deconv_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - Harness: Deconvolution Batch deconv_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.5.x - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU OpenBenchmarking.org metrics for this test profile configuration based on 260 public results since 12 October 2023 with the latest data as of 28 November 2023 .
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
# Compatible Public Results
ms (Average)
OpenBenchmarking.org Distribution Of Public Results - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU 260 Results Range From 654 To 42197 ms 654 1485 2316 3147 3978 4809 5640 6471 7302 8133 8964 9795 10626 11457 12288 13119 13950 14781 15612 16443 17274 18105 18936 19767 20598 21429 22260 23091 23922 24753 25584 26415 27246 28077 28908 29739 30570 31401 32232 33063 33894 34725 35556 36387 37218 38049 38880 39711 40542 41373 42204 20 40 60 80 100
Based on OpenBenchmarking.org data, the selected test / test configuration (oneDNN 3.3 - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ) has an average run-time of 5 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: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU Run-Time 4 8 12 16 20 Min: 4 / Avg: 4.34 / Max: 15
Based on public OpenBenchmarking.org results, the selected test / test configuration has an average standard deviation of 0.1% .
OpenBenchmarking.org Percent, Fewer Is Better Average Deviation Between Runs Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU Deviation 2 4 6 8 10 Min: 0 / Avg: 0.09 / Max: 2
Does It Scale Well With Increasing Cores? Yes , based on the automated analysis of the collected public benchmark data, this test / test settings does generally scale well with increasing CPU core counts. Data based on publicly available results for this test / test settings, separated by vendor, result divided by the reference CPU clock speed, grouped by matching physical CPU core count, and normalized against the smallest core count tested from each vendor for each CPU having a sufficient number of test samples and statistically significant data.
OpenBenchmarking.org Relative Core Scaling To Base oneDNN CPU Core Scaling Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU 8 12 16 32 64 96 1.67 3.34 5.01 6.68 8.35
Notable Instruction Set Usage Notable instruction set extensions supported by this test, based on an automatic analysis by the Phoronix Test Suite / OpenBenchmarking.org analytics engine.
Instruction Set
Support
Instructions Detected
Used by default on supported hardware. Found on Intel processors since at least 2010. Found on AMD processors since Bulldozer (2011).
POPCNT
Used by default on supported hardware. Found on Intel processors since Sandy Bridge (2011). Found on AMD processors since Bulldozer (2011).
VZEROUPPER VEXTRACTF128 VBROADCASTSS VINSERTF128 VPERMILPS VBROADCASTSD VPERMILPD VPERM2F128 VMASKMOVPS
Used by default on supported hardware. Found on Intel processors since Haswell (2013). Found on AMD processors since Excavator (2016).
VPBROADCASTQ VPGATHERQQ VGATHERQPS VPGATHERQD VPERM2I128 VINSERTI128 VPBROADCASTD VPBLENDD VPSLLVD VEXTRACTI128 VPSRAVD VPBROADCASTW VPERMQ VPSRLVQ VPBROADCASTB VGATHERDPS VPGATHERDQ VPERMD VPSLLVQ VPMASKMOVQ
Used by default on supported hardware. Found on Intel processors since Haswell (2013). Found on AMD processors since Bulldozer (2011).
VFMADD231SS VFMADD213SS VFMADD132SS VFMADD132SD VFNMADD132SD VFMADD132PS VFMADD231PS VFMADD213PS VFNMADD132PS VFNMSUB231PS VFNMSUB132SS VFNMADD132SS VFNMSUB231SS VFNMADD231PS VFNMADD231SS VFNMADD213SS VFMSUB132SS VFMADD132PD VFMADD231PD VFMADD231SD VFMADD213PD VFMSUB231SS VFMSUB231SD VFMSUB213PS VFMSUB132PS VFMSUB213SS
Advanced Vector Extensions 512 (AVX512)
Requires passing a supported compiler/build flag (verified with targets: cascadelake, sapphirerapids).
(ZMM REGISTER USE)
The test / benchmark does honor compiler flag changes.
Last automated analysis: 31 March 2023
This test profile binary relies on the shared libraries libdnnl.so.3, libm.so.6, libgomp.so.1, libc.so.6 .
Tested CPU Architectures This benchmark has been successfully tested on the below mentioned architectures. The CPU architectures listed is where successful OpenBenchmarking.org result uploads occurred, namely for helping to determine if a given test is compatible with various alternative CPU architectures.
CPU Architecture
Kernel Identifier
Verified On
Intel / AMD x86 64-bit
x86_64
(Many Processors)
Recent Test Results
1 System - 39 Benchmark Results
AMD Ryzen 9 3900X 12-Core - ASUS TUF GAMING X570-PLUS - AMD Starship
Debian 12 - 6.1.0-13-amd64 - X Server 1.21.1.7
2 Systems - 180 Benchmark Results
1 System - 21 Benchmark Results
Intel Xeon E5-1620 v4 - HP 212B v1.01 - Intel Xeon E7 v4
Ubuntu 22.04 - 6.2.0-37-generic - GNOME Shell 42.9
4 Systems - 177 Benchmark Results
AMD Ryzen 9 7950X3D 16-Core - ASRockRack B650D4U-2L2T/BCM - AMD Device 14d8
Ubuntu 22.04 - 6.6.0-rc4-phx-amd-pref-core - GNOME Shell 42.9
4 Systems - 350 Benchmark Results
AMD EPYC 8534P 64-Core - AMD Cinnabar - AMD Device 14a4
Ubuntu 23.10 - 6.5.0-5-generic - GNOME Shell 45.0
4 Systems - 415 Benchmark Results
AMD Ryzen 9 7950X 16-Core - ASUS ROG STRIX X670E-E GAMING WIFI - AMD Device 14d8
Ubuntu 23.10 - 6.5.0-9-generic - GNOME Shell 45.0
3 Systems - 513 Benchmark Results
AMD EPYC 8534P 64-Core - AMD Cinnabar - AMD Device 14a4
Ubuntu 23.10 - 6.5.0-5-generic - GNOME Shell 45.0
1 System - 21 Benchmark Results
2 x AMD EPYC 7642 48-Core - Dell 0GK70M - AMD Starship
CentOS Linux 7 - 3.10.0-1160.24.1.el7.x86_64 - GCC 4.8.5 20150623
2 Systems - 339 Benchmark Results
AMD Ryzen 7 7800X3D 8-Core - ASUS ProArt B650-CREATOR - AMD Device 14d8
Debian 12 - 6.5.0-0.deb12.1-amd64 - Xfce 4.18
Featured Kernel Comparison
5 Systems - 108 Benchmark Results
AMD Ryzen 7 5800H - LENOVO LNVNB161216 - AMD Renoir
Xilka 2023.03.07 - 6.6.1 - KDE Plasma 5.27.9
1 System - 264 Benchmark Results
Intel Pentium Gold G6405 - ASRock H510M-HDV/M.2 SE - Intel Comet Lake PCH
Ubuntu 20.04 - 5.15.0-88-generic - GNOME Shell 3.36.9
2 Systems - 428 Benchmark Results
2 Systems - 202 Benchmark Results
2 Systems - 142 Benchmark Results
Intel Core i9-10980XE - ASRock X299 Steel Legend - Intel Sky Lake-E DMI3 Registers
Ubuntu 22.04 - 6.2.0-33-generic - GNOME Shell 42.2
Most Popular Test Results
Featured Processor Comparison
AMD Ryzen 9 7900X 12-Core - ASUS ROG STRIX X670E-E GAMING WIFI - AMD Device 14d8
Ubuntu 23.10 - 6.5.0-9-generic - GNOME Shell 45.0
Featured Processor Comparison
Intel Core i9-14900K - ASUS PRIME Z790-P WIFI - Intel Device 7a27
Ubuntu 23.10 - 6.5.0-9-generic - GNOME Shell 45.0
3 Systems - 68 Benchmark Results
2 x Intel Xeon Max 9480 - Supermicro X13DEM v1.10 - Intel Device 1bce
CentOS Stream 9 - 5.14.0-373.el9.x86_64 - GNOME Shell 40.10
2 Systems - 202 Benchmark Results
2 Systems - 35 Benchmark Results
2 x Intel Xeon Platinum 8490H - Quanta Cloud S6Q-MB-MPS - Intel Device 1bce
Ubuntu 23.10 - 6.6.0-rc5-phx-patched - GNOME Shell 45.0
2 Systems - 35 Benchmark Results
2 x AMD EPYC 9684X 96-Core - AMD Titanite_4G - AMD Device 14a4
Ubuntu 23.10 - 6.6.0-060600rc1-generic - GNOME Shell
4 Systems - 27 Benchmark Results
AMD Ryzen Threadripper 3990X 64-Core - Gigabyte TRX40 AORUS PRO WIFI - AMD Starship
Ubuntu 23.04 - 6.2.0-32-generic - GNOME Shell 44.0
3 Systems - 19 Benchmark Results
Intel Core i9-13900K - ASUS PRIME Z790-P WIFI - Intel Device 7a27
Ubuntu 23.10 - 6.5.0-7-generic - GNOME Shell 45.0
Featured OpenGL Comparison
AMD Ryzen 7 PRO 6850U - LENOVO 21CM0001US - AMD 17h-19h PCIe Root Complex
Ubuntu 23.10 - 6.3.0-7-generic - GNOME Shell
2 Systems - 63 Benchmark Results
Intel Xeon Silver 4216 - TYAN S7100AG2NR - Intel Sky Lake-E DMI3 Registers
Debian 12 - 6.1.0-11-amd64 - X Server
2 Systems - 134 Benchmark Results
AMD Ryzen 7 PRO 7840U - LENOVO 21K5001JUS - AMD Device 14e8
Fedora Linux 39 - 6.5.7-300.fc39.x86_64 - GNOME Shell 45.0
2 Systems - 339 Benchmark Results
2 x Intel Xeon Max 9480 - Supermicro X13DEM v1.10 - Intel Device 1bce
Ubuntu 22.04 - 6.2.0-34-generic - GNOME Shell 42.9
2 Systems - 24 Benchmark Results
AMD Ryzen 9 7950X 16-Core - ASUS ROG STRIX X670E-E GAMING WIFI - AMD Device 14d8
Ubuntu 23.10 - 6.5.0-9-generic - GNOME Shell 45.0
Featured Compiler Comparison
AMD Ryzen 7 4700U - LENOVO LNVNB161216 - AMD Renoir
Ubuntu 23.04 - 6.2.0-24-generic - GNOME Shell 44.2
2 Systems - 141 Benchmark Results
AMD Ryzen 7 PRO 7840U - LENOVO 21K5001JUS - AMD Device 14e8
Fedora Linux 39 - 6.5.7-300.fc39.x86_64 - GNOME Shell 45.0