Intel Core i5-1145G7 testing with a LENOVO 20XW004AUS (N32ET71W 1.47 BIOS) and Intel Xe TGL GT2 3GB on Ubuntu 20.04 via the Phoronix Test Suite.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2203301-NE-ONEDNNTGL27
onednn tgl
Intel Core i5-1145G7 testing with a LENOVO 20XW004AUS (N32ET71W 1.47 BIOS) and Intel Xe TGL GT2 3GB on Ubuntu 20.04 via the Phoronix Test Suite.
A:
Processor: Intel Core i5-1145G7 @ 4.40GHz (4 Cores / 8 Threads), Motherboard: LENOVO 20XW004AUS (N32ET71W 1.47 BIOS), Chipset: Intel Device a0ef, Memory: 16GB, Disk: 1024GB SAMSUNG MZVLB1T0HBLR-000H1, Graphics: Intel Xe TGL GT2 3GB (1300MHz), Audio: Realtek ALC287, Network: Intel Device a0f0
OS: Ubuntu 20.04, Kernel: 5.14.0-1027-oem (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, OpenGL: 4.6 Mesa 21.2.6, Vulkan: 1.2.182, Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1200
B:
Processor: Intel Core i5-1145G7 @ 4.40GHz (4 Cores / 8 Threads), Motherboard: LENOVO 20XW004AUS (N32ET71W 1.47 BIOS), Chipset: Intel Device a0ef, Memory: 16GB, Disk: 1024GB SAMSUNG MZVLB1T0HBLR-000H1, Graphics: Intel Xe TGL GT2 3GB (1300MHz), Audio: Realtek ALC287, Network: Intel Device a0f0
OS: Ubuntu 20.04, Kernel: 5.14.0-1027-oem (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, OpenGL: 4.6 Mesa 21.2.6, Vulkan: 1.2.182, Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1200
C:
Processor: Intel Core i5-1145G7 @ 4.40GHz (4 Cores / 8 Threads), Motherboard: LENOVO 20XW004AUS (N32ET71W 1.47 BIOS), Chipset: Intel Device a0ef, Memory: 16GB, Disk: 1024GB SAMSUNG MZVLB1T0HBLR-000H1, Graphics: Intel Xe TGL GT2 3GB (1300MHz), Audio: Realtek ALC287, Network: Intel Device a0f0
OS: Ubuntu 20.04, Kernel: 5.14.0-1027-oem (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, OpenGL: 4.6 Mesa 21.2.6, Vulkan: 1.2.182, Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1200
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 9342.56 |==================================================================
B . 9340.03 |==================================================================
C . 9341.19 |==================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 9344.99 |==================================================================
B . 9347.91 |==================================================================
C . 9346.82 |==================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 9343.97 |==================================================================
B . 9342.43 |==================================================================
C . 9341.05 |==================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 17.70 |==================================================================
B . 18.30 |====================================================================
C . 18.21 |====================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 4767.33 |==================================================================
B . 4768.46 |==================================================================
C . 4759.63 |==================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 4760.20 |==================================================================
B . 4762.01 |==================================================================
C . 4764.79 |==================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 4762.61 |==================================================================
B . 4762.94 |==================================================================
C . 4763.90 |==================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 63.56 |===================================================================
B . 63.93 |===================================================================
C . 64.46 |====================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 3.00906 |=================================================================
B . 3.03781 |=================================================================
C . 3.07591 |==================================================================
oneDNN 2.6
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1.95301 |===========================================================
B . 2.08277 |===============================================================
C . 2.16909 |==================================================================
oneDNN 2.6
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 8.46208 |===========================================================
B . 8.95669 |==============================================================
C . 9.51734 |==================================================================
oneDNN 2.6
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 3.97598 |==================================================================
B . 3.97577 |==================================================================
C . 3.98095 |==================================================================
oneDNN 2.6
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1.70174 |==================================================================
B . 1.70897 |==================================================================
C . 1.71077 |==================================================================
oneDNN 2.6
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 12.27 |====================================================================
B . 12.20 |====================================================================
C . 12.20 |====================================================================
oneDNN 2.6
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 6.11673 |================================================================
B . 6.31136 |==================================================================
C . 6.35160 |==================================================================
oneDNN 2.6
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 19.84 |====================================================================
B . 19.84 |====================================================================
C . 19.80 |====================================================================
oneDNN 2.6
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 2.11018 |=============================================================
B . 2.28842 |==================================================================
C . 2.29786 |==================================================================
oneDNN 2.6
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 9.41308 |==========================================================
B . 9.99736 |==============================================================
C . 10.51531 |=================================================================
oneDNN 2.6
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 7.00896 |=============================================================
B . 7.61626 |==================================================================
C . 7.63750 |==================================================================
oneDNN 2.6
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 5.40966 |=================================================================
B . 5.40629 |=================================================================
C . 5.45216 |==================================================================
oneDNN 2.6
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 38.04 |====================================================================
B . 37.97 |====================================================================
C . 37.96 |====================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 38.27 |====================================================================
B . 38.31 |====================================================================
C . 38.40 |====================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 10.17120 |=================================================================
B . 9.93060 |===============================================================
C . 9.82487 |===============================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 2.41032 |==================================================================
B . 2.35766 |=================================================================
C . 2.34011 |================================================================