adlp omnednn
Intel Core i7-1280P testing with a MSI MS-14C6 (E14C6IMS.115 BIOS) and MSI Intel ADL GT2 15GB on Ubuntu 22.10 via the Phoronix Test Suite.


a: 

	Processor: Intel Core i7-1280P @ 4.70GHz (14 Cores / 20 Threads), Motherboard: MSI MS-14C6 (E14C6IMS.115 BIOS), Chipset: Intel Alder Lake PCH, Memory: 16GB, Disk: 1024GB Micron_3400_MTFDKBA1T0TFH, Graphics: MSI Intel ADL GT2 15GB (1450MHz), Audio: Realtek ALC274, Network: Intel Alder Lake-P PCH CNVi WiFi

	OS: Ubuntu 22.10, Kernel: 5.19.0-38-generic (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.4 + Wayland, OpenGL: 4.6 Mesa 22.2.5, OpenCL: OpenCL 3.0, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080

b: 

	Processor: Intel Core i7-1280P @ 4.70GHz (14 Cores / 20 Threads), Motherboard: MSI MS-14C6 (E14C6IMS.115 BIOS), Chipset: Intel Alder Lake PCH, Memory: 16GB, Disk: 1024GB Micron_3400_MTFDKBA1T0TFH, Graphics: MSI Intel ADL GT2 15GB (1450MHz), Audio: Realtek ALC274, Network: Intel Alder Lake-P PCH CNVi WiFi

	OS: Ubuntu 22.10, Kernel: 5.19.0-38-generic (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.4 + Wayland, OpenGL: 4.6 Mesa 22.2.5, OpenCL: OpenCL 3.0, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080

c: 

	Processor: Intel Core i7-1280P @ 4.70GHz (14 Cores / 20 Threads), Motherboard: MSI MS-14C6 (E14C6IMS.115 BIOS), Chipset: Intel Alder Lake PCH, Memory: 16GB, Disk: 1024GB Micron_3400_MTFDKBA1T0TFH, Graphics: MSI Intel ADL GT2 15GB (1450MHz), Audio: Realtek ALC274, Network: Intel Alder Lake-P PCH CNVi WiFi

	OS: Ubuntu 22.10, Kernel: 5.19.0-38-generic (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.4 + Wayland, OpenGL: 4.6 Mesa 22.2.5, OpenCL: OpenCL 3.0, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080


oneDNN 3.1
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 31.06610 |=================================================================
b . 8.70600  |==================
c . 8.57054  |==================


oneDNN 3.1
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 7591.86  |============================================
b . 11256.90 |=================================================================
c . 7545.31  |============================================


oneDNN 3.1
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 2.97909 |==================================================================
b . 2.43264 |======================================================
c . 2.43351 |======================================================


oneDNN 3.1
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 3897.02 |===============================================================
b . 4105.07 |==================================================================
c . 3899.98 |===============================================================


oneDNN 3.1
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 3.86162 |=================================================================
b . 3.89311 |=================================================================
c . 3.93962 |==================================================================


oneDNN 3.1
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 10.82 |===================================================================
b . 11.04 |====================================================================
c . 10.85 |===================================================================


oneDNN 3.1
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 4.79174 |=================================================================
b . 4.78758 |=================================================================
c . 4.87212 |==================================================================


oneDNN 3.1
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 8.20737 |==================================================================
b . 8.21725 |==================================================================
c . 8.10845 |=================================================================


oneDNN 3.1
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 1.76244 |==================================================================
b . 1.76766 |==================================================================
c . 1.75303 |=================================================================


oneDNN 3.1
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 9.28976 |==================================================================
b . 9.24974 |==================================================================
c . 9.21746 |=================================================================


oneDNN 3.1
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 5.11614 |==================================================================
b . 5.11044 |==================================================================
c . 5.07796 |==================================================================


oneDNN 3.1
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 1.48158 |==================================================================
b . 1.47297 |==================================================================
c . 1.47108 |==================================================================


oneDNN 3.1
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 7573.19 |==================================================================
b . 7554.00 |==================================================================
c . 7535.24 |==================================================================


oneDNN 3.1
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 7579.56 |==================================================================
b . 7580.16 |==================================================================
c . 7559.92 |==================================================================


oneDNN 3.1
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 3910.73 |==================================================================
b . 3906.51 |==================================================================
c . 3901.80 |==================================================================


oneDNN 3.1
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 3908.96 |==================================================================
b . 3910.92 |==================================================================
c . 3910.85 |==================================================================