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 1 March 2024
Test Type Processor
Average Install Time 5 Minutes, 19 Seconds
Average Run Time 2 Minutes, 16 Seconds
Test Dependencies C/C++ Compiler Toolchain + CMake
Accolades 70k+ Downloads Public Result Uploads * Reported Installs ** Reported Test Completions ** Test Profile Page Views *** OpenBenchmarking.org Events oneDNN Popularity Statistics pts/onednn 2020.06 2020.08 2020.10 2020.12 2021.02 2021.04 2021.06 2021.08 2021.10 2021.12 2022.02 2022.04 2022.06 2022.08 2022.10 2022.12 2023.02 2023.04 2023.06 2023.08 2023.10 2023.12 2024.02 2024.04 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 21 April 2024.
Revision Historypts/onednn-3.4.0 [View Source ] Fri, 01 Mar 2024 13:02:43 GMT Update against oneDNN 3.4 upstream.
pts/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-3.4.x - Harness: Recurrent Neural Network Training - Engine: CPU pts/onednn-3.4.x - Harness: Deconvolution Batch shapes_1d - Engine: CPU pts/onednn-3.4.x - Harness: Deconvolution Batch shapes_3d - Engine: CPU pts/onednn-3.4.x - Harness: Recurrent Neural Network Inference - Engine: CPU pts/onednn-3.4.x - Harness: IP Shapes 3D - Engine: CPU pts/onednn-3.4.x - Harness: IP Shapes 1D - Engine: CPU pts/onednn-3.4.x - Harness: Convolution Batch Shapes Auto - Engine: CPU pts/onednn-3.3.x - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU 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: f32 - 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 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: 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: IP Shapes 3D - Data Type: bf16bf16bf16 - 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: Recurrent Neural Network Inference - Data Type: u8s8f32 - 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_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: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.x - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - 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: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.x - Harness: IP Shapes 1D - 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: Recurrent Neural Network Inference - Data Type: f32 - 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: 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 3.4 Harness: Recurrent Neural Network Training - Engine: CPU OpenBenchmarking.org metrics for this test profile configuration based on 123 public results since 1 March 2024 with the latest data as of 24 April 2024 .
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 - Engine: CPU 123 Results Range From 553 To 12554 ms 553 794 1035 1276 1517 1758 1999 2240 2481 2722 2963 3204 3445 3686 3927 4168 4409 4650 4891 5132 5373 5614 5855 6096 6337 6578 6819 7060 7301 7542 7783 8024 8265 8506 8747 8988 9229 9470 9711 9952 10193 10434 10675 10916 11157 11398 11639 11880 12121 12362 12603 6 12 18 24 30
Based on OpenBenchmarking.org data, the selected test / test configuration (oneDNN 3.4 - Harness: Recurrent Neural Network Training - 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: Recurrent Neural Network Training - Engine: CPU Run-Time 2 4 6 8 10 Min: 4 / Avg: 4.1 / Max: 5
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 Sandy Bridge (2011). Found on AMD processors since Bulldozer (2011).
VZEROUPPER VBROADCASTSS VINSERTF128 VPERMILPS VBROADCASTSD VEXTRACTF128 VPERMILPD VPERM2F128 VMASKMOVPS
Used by default on supported hardware. Found on Intel processors since Haswell (2013). Found on AMD processors since Excavator (2016).
VPBROADCASTQ VINSERTI128 VPBROADCASTD VPBLENDD VPSLLVD VEXTRACTI128 VPSRAVD VPERM2I128 VPGATHERQQ VGATHERQPS VPERMQ VPBROADCASTW VPSRLVQ VPBROADCASTB VPGATHERDQ VPGATHERQD VPSLLVQ VPMASKMOVQ VPERMD
Used by default on supported hardware. Found on Intel processors since Haswell (2013). Found on AMD processors since Bulldozer (2011).
VFMADD231SS VFMADD213SS VFMADD132SS VFMADD132SD VFMADD132PS VFMADD231PS VFMADD213PS VFNMADD132PS VFNMSUB231PS VFNMSUB132SS VFNMADD132SS VFNMSUB231SS VFNMADD231PS VFNMADD231SS VFNMADD213SS VFMADD231SD VFMSUB132SS VFMADD132PD VFMADD231PD VFMADD213PD VFMSUB231SS VFMSUB213PS VFMSUB132PS VFMSUB213SS VFMSUB231SD
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: 2 March 2024
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)
ARMv8 64-bit
aarch64
ARMv8 Neoverse-N1 128-Core, ARMv8 Neoverse-V2 72-Core
Recent Test Results
2 Systems - 219 Benchmark Results
AMD Ryzen 7 7840HS - Framework Laptop 16 - AMD Device 14e8
Ubuntu 23.10 - 6.7.0-060700-generic - GNOME Shell 45.2
3 Systems - 131 Benchmark Results
AMD Ryzen 7 4700U - LENOVO IdeaPad 5 14ARE05 LNVNB161216 - AMD Renoir
Fedora Linux 39 - 6.5.7-300.fc39.x86_64 - GNOME Shell 45.0
1 System - 7 Benchmark Results
AMD Ryzen 5 8600G - ASRock A620M-HDV/M.2 - AMD Device 14e8
Ubuntu 24.04 - 6.8.0-11-generic - Xfce 4.18
76 Systems - 921 Benchmark Results
Intel Core i9-14900K - ASUS PRIME Z790-P - Intel Device 7a27
SystemRescue 10.01 - 6.1.30-1-lts - X Server 1.21.1.8
1 System - 23 Benchmark Results
Intel Core i9-14900K - ASUS PRIME Z790-P - Intel Device 7a27
SystemRescue 10.01 - 6.1.30-1-lts - X Server 1.21.1.8
2 Systems - 108 Benchmark Results
Intel Core i7-1065G7 - Dell 06CDVY - Intel Ice Lake-LP DRAM
Ubuntu 23.10 - 6.7.0-060700rc5-generic - GNOME Shell 45.1
4 Systems - 158 Benchmark Results
Intel Xeon E E-2488 - Supermicro Super Server X13SCL-F v0123456789 - Intel Device 7a27
Ubuntu 22.04 - 6.2.0-26-generic - GNOME Shell 42.9
4 Systems - 158 Benchmark Results
AMD Ryzen Threadripper PRO 5965WX 24-Cores - ASUS Pro WS WRX80E-SAGE SE WIFI - AMD Starship
Ubuntu 23.10 - 6.5.0-15-generic - GNOME Shell 45.0
4 Systems - 120 Benchmark Results
ARMv8 Neoverse-N1 - GIGABYTE G242-P36-00 MP32-AR2-00 v01000100 - Ampere Computing LLC Altra PCI Root Complex A
Ubuntu 23.10 - 6.5.0-15-generic - GCC 13.2.0
3 Systems - 119 Benchmark Results
AMD EPYC 8534P 64-Core - AMD Cinnabar - AMD Device 14a4
Ubuntu 23.10 - 6.5.0-15-generic - GNOME Shell
3 Systems - 114 Benchmark Results
AMD Ryzen 5 5500U - NB01 TUXEDO Aura 15 Gen2 NL5xNU - AMD Renoir
Tuxedo 22.04 - 6.0.0-1010-oem - KDE Plasma 5.26.5
1 System - 341 Benchmark Results
AMD Ryzen 9 7950X 16-Core - ASUS ProArt X670E-CREATOR WIFI - AMD Device 14d8
Pop 22.04 - 6.6.10-76060610-generic - GNOME Shell 42.5
2 Systems - 8 Benchmark Results
AMD EPYC 7R13 48-Core - Supermicro H12SSL-I v1.02 - AMD Starship
EndeavourOS rolling - 6.7.9-zen1-1-zen - X Server 1.21.1.11
3 Systems - 64 Benchmark Results
Intel Core i7-5960X - Gigabyte X99-UD4-CF - Intel Xeon E7 v3
Debian 12 - 6.1.0-11-amd64 - GCC 12.2.0
2 Systems - 69 Benchmark Results
AMD Ryzen 5 8500G - ASRock B650 Pro RS - AMD Device 14e8
Ubuntu 23.10 - 6.7.3-060703-generic - GNOME Shell 45.2
Most Popular Test Results
3 Systems - 16 Benchmark Results
Intel Core i7-1185G7 - Dell XPS 13 9310 0DXP1F - Intel Tiger Lake-LP
Ubuntu 23.10 - 6.7.0-060700rc5-generic - GNOME Shell 45.1
3 Systems - 17 Benchmark Results
Intel Xeon Silver 4216 - TYAN S7100AG2NR - Intel Sky Lake-E DMI3 Registers
Debian 12 - 6.1.0-11-amd64 - X Server
3 Systems - 8 Benchmark Results
AMD EPYC 7551 32-Core - GIGABYTE MZ31-AR0-00 v01010101 - AMD 17h
Debian 12 - 6.1.0-10-amd64 - GCC 12.2.0
4 Systems - 7 Benchmark Results
ARMv8 Neoverse-V2 - Quanta Cloud QuantaGrid S74G-2U 1S7GZ9Z0000 S7G MB - 1 x 480GB DRAM-6400MT
Ubuntu 22.04 - 6.5.0-1007-NVIDIA-64k - NVIDIA
2 Systems - 16 Benchmark Results
2 x INTEL XEON PLATINUM 8592+ - Quanta Cloud QuantaGrid D54Q-2U S6Q-MB-MPS - Intel Device 1bce
Fedora Linux 39 - 6.5.6-300.fc39.x86_64 - GCC 13.2.1 20231205
3 Systems - 17 Benchmark Results
Intel Core i9-10980XE - ASRock X299 Steel Legend - Intel Sky Lake-E DMI3 Registers
Ubuntu 22.04 - 6.5.0-18-generic - GNOME Shell 42.2
3 Systems - 17 Benchmark Results
AMD EPYC 7F32 8-Core - ASRockRack EPYCD8 - AMD Starship
Debian 12 - 6.1.0-11-amd64 - X Server
4 Systems - 7 Benchmark Results
AMD Ryzen Threadripper 7980X 64-Cores - System76 Thelio Major - AMD Device 14a4
Ubuntu 23.10 - 6.5.0-21-generic - GNOME Shell 45.2
2 Systems - 56 Benchmark Results
Intel Core Ultra 7 155H - MTL Swift SFG14-72T Coral_MTH - Intel Device 7e7f
Ubuntu 23.10 - 6.8.0-060800rc1daily20240126-generic - GNOME Shell 45.2
Featured Graphics Comparison
AMD Ryzen 9 5900HX - ASUS ROG Strix G513QY_G513QY G513QY v1.0 - AMD Renoir
Ubuntu 22.10 - 5.19.0-46-generic - GNOME Shell 43.0
2 Systems - 69 Benchmark Results
AMD Ryzen 5 8500G - ASRock B650 Pro RS - AMD Device 14e8
Ubuntu 23.10 - 6.7.3-060703-generic - GNOME Shell 45.2
2 Systems - 100 Benchmark Results
AMD Ryzen 7 PRO 5850U - LENOVO ThinkPad T14s Gen 2a 20XF004WUS - AMD Renoir
Fedora Linux 39 - 6.5.8-300.fc39.x86_64 - GNOME Shell 45.0
3 Systems - 119 Benchmark Results
AMD EPYC 8534P 64-Core - AMD Cinnabar - AMD Device 14a4
Ubuntu 23.10 - 6.5.0-15-generic - GNOME Shell
2 Systems - 108 Benchmark Results
Intel Core i7-1065G7 - Dell 06CDVY - Intel Ice Lake-LP DRAM
Ubuntu 23.10 - 6.7.0-060700rc5-generic - GNOME Shell 45.1
2 Systems - 85 Benchmark Results
2 x AMD EPYC 9684X 96-Core - AMD Titanite_4G - AMD Device 14a4
Ubuntu 24.04 - 6.8.0-11-generic - GNOME Shell 45.3