Xeon E3 oneDNN 2.0 Intel Xeon E3-1280 v5 testing with a MSI Z170A SLI PLUS (MS-7998) v1.0 (2.A0 BIOS) and ASUS AMD Radeon HD 7850 / R7 265 R9 270 1024SP on Ubuntu 20.04 via the Phoronix Test Suite. 1: Processor: Intel Xeon E3-1280 v5 @ 4.00GHz (4 Cores / 8 Threads), Motherboard: MSI Z170A SLI PLUS (MS-7998) v1.0 (2.A0 BIOS), Chipset: Intel Xeon E3-1200 v5/E3-1500, Memory: 32GB, Disk: 256GB TOSHIBA RD400, Graphics: ASUS AMD Radeon HD 7850 / R7 265 R9 270 1024SP, Audio: Realtek ALC1150, Monitor: VA2431, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc2daily20200826-generic (x86_64) 20200825, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.5 Mesa 20.0.8 (LLVM 10.0.0), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 2: Processor: Intel Xeon E3-1280 v5 @ 4.00GHz (4 Cores / 8 Threads), Motherboard: MSI Z170A SLI PLUS (MS-7998) v1.0 (2.A0 BIOS), Chipset: Intel Xeon E3-1200 v5/E3-1500, Memory: 32GB, Disk: 256GB TOSHIBA RD400, Graphics: ASUS AMD Radeon HD 7850 / R7 265 R9 270 1024SP, Audio: Realtek ALC1150, Monitor: VA2431, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc2daily20200826-generic (x86_64) 20200825, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.5 Mesa 20.0.8 (LLVM 10.0.0), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 3: Processor: Intel Xeon E3-1280 v5 @ 4.00GHz (4 Cores / 8 Threads), Motherboard: MSI Z170A SLI PLUS (MS-7998) v1.0 (2.A0 BIOS), Chipset: Intel Xeon E3-1200 v5/E3-1500, Memory: 32GB, Disk: 256GB TOSHIBA RD400, Graphics: ASUS AMD Radeon HD 7850 / R7 265 R9 270 1024SP, Audio: Realtek ALC1150, Monitor: VA2431, Network: Intel I219-V OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc2daily20200826-generic (x86_64) 20200825, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 4.5 Mesa 20.0.8 (LLVM 10.0.0), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080 oneDNN 2.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 8.11023 |================================================================== 2 . 8.13349 |================================================================== 3 . 8.10394 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 12.27 |==================================================================== 2 . 12.25 |==================================================================== 3 . 12.26 |==================================================================== oneDNN 2.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.66378 |================================================================== 2 . 3.66363 |================================================================== 3 . 3.66143 |================================================================== oneDNN 2.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3.23290 |================================================================== 2 . 3.23315 |================================================================== 3 . 3.22697 |================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 20.91 |==================================================================== 2 . 20.90 |==================================================================== 3 . 20.92 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 10.60 |==================================================================== 2 . 10.59 |==================================================================== 3 . 10.59 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 14.40 |==================================================================== 2 . 14.40 |==================================================================== 3 . 14.43 |==================================================================== oneDNN 2.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 20.63 |==================================================================== 2 . 20.52 |==================================================================== 3 . 20.49 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 11.33 |==================================================================== 2 . 11.08 |================================================================== 3 . 11.31 |==================================================================== oneDNN 2.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 7.43783 |================================================================== 2 . 7.44242 |================================================================== 3 . 7.43292 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 7402.18 |================================================================== 2 . 7401.84 |================================================================== 3 . 7402.39 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3950.40 |================================================================== 2 . 3954.92 |================================================================== 3 . 3949.85 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 7403.96 |================================================================== 2 . 7400.59 |================================================================== 3 . 7406.71 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3952.60 |================================================================== 2 . 3953.55 |================================================================== 3 . 3949.87 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 5.38679 |================================================================== 2 . 5.38858 |================================================================== 3 . 5.37225 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 7397.77 |================================================================== 2 . 7401.56 |================================================================== 3 . 7397.07 |================================================================== oneDNN 2.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3951.82 |================================================================== 2 . 3954.41 |================================================================== 3 . 3949.09 |================================================================== oneDNN 2.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 6.90131 |================================================================== 2 . 6.90507 |================================================================== 3 . 6.90071 |==================================================================