Intel Core i7-1165G7 testing with a Dell 0GG9PT (1.0.3 BIOS) and Intel UHD 3GB on Ubuntu 20.10 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 2012203-PTS-ONEDNN2092
oneDNN 2.0 Intel Tiger Lake
Intel Core i7-1165G7 testing with a Dell 0GG9PT (1.0.3 BIOS) and Intel UHD 3GB on Ubuntu 20.10 via the Phoronix Test Suite.
Run 1:
Processor: Intel Core i7-1165G7 @ 4.70GHz (4 Cores / 8 Threads), Motherboard: Dell 0GG9PT (1.0.3 BIOS), Chipset: Intel Tiger Lake-LP, Memory: 16GB, Disk: Kioxia KBG40ZNS256G NVMe 256GB, Graphics: Intel UHD 3GB (1300MHz), Audio: Realtek ALC289, Network: Intel Wi-Fi 6 AX201
OS: Ubuntu 20.10, Kernel: 5.10.0-051000rc7daily20201213-generic (x86_64) 20201212, Desktop: GNOME Shell 3.38.1, Display Server: X Server 1.20.9, Display Driver: modesetting 1.20.9, OpenGL: 4.6 Mesa 20.2.1, Vulkan: 1.2.145, Compiler: GCC 10.2.0, File-System: ext4, Screen Resolution: 1920x1200
Run 2:
Processor: Intel Core i7-1165G7 @ 4.70GHz (4 Cores / 8 Threads), Motherboard: Dell 0GG9PT (1.0.3 BIOS), Chipset: Intel Tiger Lake-LP, Memory: 16GB, Disk: Kioxia KBG40ZNS256G NVMe 256GB, Graphics: Intel UHD 3GB (1300MHz), Audio: Realtek ALC289, Network: Intel Wi-Fi 6 AX201
OS: Ubuntu 20.10, Kernel: 5.10.0-051000rc7daily20201213-generic (x86_64) 20201212, Desktop: GNOME Shell 3.38.1, Display Server: X Server 1.20.9, Display Driver: modesetting 1.20.9, OpenGL: 4.6 Mesa 20.2.1, Vulkan: 1.2.145, Compiler: GCC 10.2.0, File-System: ext4, Screen Resolution: 1920x1200
Run 3:
Processor: Intel Core i7-1165G7 @ 4.70GHz (4 Cores / 8 Threads), Motherboard: Dell 0GG9PT (1.0.3 BIOS), Chipset: Intel Tiger Lake-LP, Memory: 16GB, Disk: Kioxia KBG40ZNS256G NVMe 256GB, Graphics: Intel UHD 3GB (1300MHz), Audio: Realtek ALC289, Network: Intel Wi-Fi 6 AX201
OS: Ubuntu 20.10, Kernel: 5.10.0-051000rc7daily20201213-generic (x86_64) 20201212, Desktop: GNOME Shell 3.38.1, Display Server: X Server 1.20.9, Display Driver: modesetting 1.20.9, OpenGL: 4.6 Mesa 20.2.1, Vulkan: 1.2.145, Compiler: GCC 10.2.0, File-System: ext4, Screen Resolution: 1920x1200
oneDNN 2.0
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 12.20 |================================================================
Run 2 . 12.23 |================================================================
Run 3 . 12.29 |================================================================
oneDNN 2.0
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 6.43606 |========================================================
Run 2 . 6.44613 |========================================================
Run 3 . 7.17560 |==============================================================
oneDNN 2.0
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 2.48937 |=========================================================
Run 2 . 2.49932 |=========================================================
Run 3 . 2.70714 |==============================================================
oneDNN 2.0
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 2.84008 |=========================================================
Run 2 . 2.85308 |=========================================================
Run 3 . 3.11496 |==============================================================
oneDNN 2.0
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
Run 1 . 25.56 |==============================================================
Run 2 . 25.76 |===============================================================
Run 3 . 26.35 |================================================================
oneDNN 2.0
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
Run 1 . 7.92055 |=========================================================
Run 2 . 8.29222 |============================================================
Run 3 . 8.63478 |==============================================================
oneDNN 2.0
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 13.50 |==========================================================
Run 2 . 14.96 |================================================================
Run 3 . 14.11 |============================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 14.10 |============================================================
Run 2 . 14.94 |================================================================
Run 3 . 14.92 |================================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 11.35 |============================================================
Run 2 . 12.12 |================================================================
Run 3 . 12.15 |================================================================
oneDNN 2.0
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 9.64780 |==========================================================
Run 2 . 10.09398 |=============================================================
Run 3 . 10.07588 |=============================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 3.00801 |===========================================================
Run 2 . 3.16932 |==============================================================
Run 3 . 3.17051 |==============================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 2.74299 |===========================================================
Run 2 . 2.88129 |==============================================================
Run 3 . 2.89264 |==============================================================
oneDNN 2.0
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 8928.89 |===========================================================
Run 2 . 9301.85 |==============================================================
Run 3 . 9350.26 |==============================================================
oneDNN 2.0
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 4570.32 |===========================================================
Run 2 . 4793.37 |==============================================================
Run 3 . 4799.77 |==============================================================
oneDNN 2.0
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 9072.26 |============================================================
Run 2 . 9309.37 |==============================================================
Run 3 . 9315.57 |==============================================================
oneDNN 2.0
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
Run 1 . 50.89 |==============================================================
Run 2 . 51.97 |================================================================
Run 3 . 52.12 |================================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
Run 1 . 59.94 |==============================================================
Run 2 . 61.73 |================================================================
Run 3 . 61.15 |===============================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
Run 1 . 37.75 |========================================================
Run 2 . 42.82 |================================================================
Run 3 . 42.79 |================================================================
oneDNN 2.0
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 4537.86 |==========================================================
Run 2 . 4799.98 |==============================================================
Run 3 . 4809.37 |==============================================================
oneDNN 2.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 4.96366 |==========================================================
Run 2 . 5.35102 |==============================================================
Run 3 . 5.35007 |==============================================================
oneDNN 2.0
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
Run 1 . 8891.89 |===========================================================
Run 2 . 9302.70 |==============================================================
Run 3 . 9299.60 |==============================================================
oneDNN 2.0
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
Run 1 . 4532.64 |==========================================================
Run 2 . 4807.71 |==============================================================
Run 3 . 4816.40 |==============================================================
oneDNN 2.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 2.74771 |=========================================================
Run 2 . 2.99320 |==============================================================
Run 3 . 3.00553 |==============================================================
oneDNN 2.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
Run 1 . 12.06 |=============================================================
Run 2 . 12.66 |================================================================
Run 3 . 12.71 |================================================================