Intel Core i9-12900K testing with a ASUS ROG STRIX Z690-E GAMING WIFI (1003 BIOS) and Gigabyte AMD Radeon RX 6800 XT 16GB on Ubuntu 21.10 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 2203319-NE-ONEDNNONN20
onednn onnx alderlake
Intel Core i9-12900K testing with a ASUS ROG STRIX Z690-E GAMING WIFI (1003 BIOS) and Gigabyte AMD Radeon RX 6800 XT 16GB on Ubuntu 21.10 via the Phoronix Test Suite.
A:
Processor: Intel Core i9-12900K @ 5.20GHz (16 Cores / 24 Threads), Motherboard: ASUS ROG STRIX Z690-E GAMING WIFI (1003 BIOS), Chipset: Intel Device 7aa7, Memory: 32GB, Disk: 1000GB Western Digital WDS100T1X0E-00AFY0 + 2000GB, Graphics: Gigabyte AMD Radeon RX 6800 XT 16GB (2575/1000MHz), Audio: Intel Device 7ad0, Monitor: ASUS VP28U, Network: Intel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411
OS: Ubuntu 21.10, Kernel: 5.17.0-phx (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server 1.20.13 + Wayland, OpenGL: 4.6 Mesa 22.1.0-devel (git-ae710f3 2022-03-26 impish-oibaf-ppa) (LLVM 13.0.1 DRM 3.46), Vulkan: 1.3.207, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 3840x2160
B:
Processor: Intel Core i9-12900K @ 5.20GHz (16 Cores / 24 Threads), Motherboard: ASUS ROG STRIX Z690-E GAMING WIFI (1003 BIOS), Chipset: Intel Device 7aa7, Memory: 32GB, Disk: 1000GB Western Digital WDS100T1X0E-00AFY0 + 2000GB, Graphics: Gigabyte AMD Radeon RX 6800 XT 16GB (2575/1000MHz), Audio: Intel Device 7ad0, Monitor: ASUS VP28U, Network: Intel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411
OS: Ubuntu 21.10, Kernel: 5.17.0-phx (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server 1.20.13 + Wayland, OpenGL: 4.6 Mesa 22.1.0-devel (git-ae710f3 2022-03-26 impish-oibaf-ppa) (LLVM 13.0.1 DRM 3.46), Vulkan: 1.3.207, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 3840x2160
C:
Processor: Intel Core i9-12900K @ 5.20GHz (16 Cores / 24 Threads), Motherboard: ASUS ROG STRIX Z690-E GAMING WIFI (1003 BIOS), Chipset: Intel Device 7aa7, Memory: 32GB, Disk: 1000GB Western Digital WDS100T1X0E-00AFY0 + 2000GB, Graphics: Gigabyte AMD Radeon RX 6800 XT 16GB (2575/1000MHz), Audio: Intel Device 7ad0, Monitor: ASUS VP28U, Network: Intel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411
OS: Ubuntu 21.10, Kernel: 5.17.0-phx (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server 1.20.13 + Wayland, OpenGL: 4.6 Mesa 22.1.0-devel (git-ae710f3 2022-03-26 impish-oibaf-ppa) (LLVM 13.0.1 DRM 3.46), Vulkan: 1.3.207, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 3840x2160
D:
Processor: Intel Core i9-12900K @ 5.20GHz (16 Cores / 24 Threads), Motherboard: ASUS ROG STRIX Z690-E GAMING WIFI (1003 BIOS), Chipset: Intel Device 7aa7, Memory: 32GB, Disk: 1000GB Western Digital WDS100T1X0E-00AFY0 + 2000GB, Graphics: Gigabyte AMD Radeon RX 6800 XT 16GB (2575/1000MHz), Audio: Intel Device 7ad0, Monitor: ASUS VP28U, Network: Intel I225-V + Intel Wi-Fi 6 AX210/AX211/AX411
OS: Ubuntu 21.10, Kernel: 5.17.0-phx (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server 1.20.13 + Wayland, OpenGL: 4.6 Mesa 22.1.0-devel (git-ae710f3 2022-03-26 impish-oibaf-ppa) (LLVM 13.0.1 DRM 3.46), Vulkan: 1.3.207, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 3840x2160
ONNX Runtime 1.11
Model: bertsquad-12 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 925 |===================================================================
B . 937 |====================================================================
C . 933 |====================================================================
D . 964 |======================================================================
ONNX Runtime 1.11
Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 111 |======================================================================
B . 111 |======================================================================
C . 111 |======================================================================
D . 111 |======================================================================
ONNX Runtime 1.11
Model: fcn-resnet101-11 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 96 |=======================================================================
B . 95 |======================================================================
C . 95 |======================================================================
D . 95 |======================================================================
ONNX Runtime 1.11
Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 363 |======================================================================
B . 364 |======================================================================
C . 362 |======================================================================
D . 364 |======================================================================
ONNX Runtime 1.11
Model: GPT-2 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 7989 |====================================================================
B . 8110 |=====================================================================
C . 8046 |====================================================================
D . 8095 |=====================================================================
ONNX Runtime 1.11
Model: GPT-2 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 11035 |===================================================================
B . 11077 |====================================================================
C . 11079 |====================================================================
D . 11123 |====================================================================
ONNX Runtime 1.11
Model: yolov4 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 628 |=====================================================================
B . 632 |======================================================================
C . 631 |======================================================================
D . 633 |======================================================================
ONNX Runtime 1.11
Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 1908 |=====================================================================
B . 1913 |=====================================================================
C . 1896 |====================================================================
D . 1893 |====================================================================
ONNX Runtime 1.11
Model: bertsquad-12 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 931 |======================================================================
B . 916 |=====================================================================
C . 918 |=====================================================================
D . 926 |======================================================================
ONNX Runtime 1.11
Model: yolov4 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 670 |======================================================================
B . 664 |=====================================================================
C . 666 |======================================================================
D . 669 |======================================================================
ONNX Runtime 1.11
Model: super-resolution-10 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 4518 |=====================================================================
B . 4504 |=====================================================================
C . 4444 |====================================================================
D . 4506 |=====================================================================
ONNX Runtime 1.11
Model: super-resolution-10 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 5332 |====================================================================
B . 5349 |=====================================================================
C . 5328 |====================================================================
D . 5384 |=====================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 8.27680 |=================================================================
B . 8.38411 |==================================================================
C . 8.21415 |=================================================================
D . 8.05084 |===============================================================
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 2881.96 |==================================================================
B . 2885.96 |==================================================================
C . 2882.62 |==================================================================
D . 2880.35 |==================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 2884.94 |==================================================================
B . 2881.54 |==================================================================
C . 2879.53 |==================================================================
D . 2882.22 |==================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 2883.12 |==================================================================
B . 2875.93 |==================================================================
C . 2881.76 |==================================================================
D . 2879.92 |==================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1617.55 |==================================================================
B . 1612.80 |==================================================================
C . 1611.27 |==================================================================
D . 1614.27 |==================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 1615.08 |=================================================================
B . 1630.53 |==================================================================
C . 1618.78 |==================================================================
D . 1616.11 |=================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 1614.54 |==================================================================
B . 1611.80 |==================================================================
C . 1612.97 |==================================================================
D . 1614.78 |==================================================================
oneDNN 2.6
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1.033654 |=================================================================
B . 0.826339 |====================================================
C . 0.842998 |=====================================================
D . 1.011141 |================================================================
oneDNN 2.6
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1.08279 |==================================================================
B . 1.05578 |================================================================
C . 1.05754 |================================================================
D . 1.05923 |=================================================================
oneDNN 2.6
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 1.24567 |===============================================================
B . 1.25510 |===============================================================
C . 1.28432 |=================================================================
D . 1.31081 |==================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1.35376 |==================================================================
B . 1.35332 |==================================================================
C . 1.34061 |=================================================================
D . 1.34296 |=================================================================
oneDNN 2.6
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 2.63724 |==================================================================
B . 2.65135 |==================================================================
C . 2.63180 |==================================================================
D . 2.63287 |==================================================================
oneDNN 2.6
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 3.91213 |==================================================================
B . 3.81887 |================================================================
C . 3.88487 |==================================================================
D . 3.88717 |==================================================================
oneDNN 2.6
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 0.882082 |=================================================================
B . 0.851627 |===============================================================
C . 0.881799 |=================================================================
D . 0.885057 |=================================================================
oneDNN 2.6
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 5.90865 |==================================================================
B . 5.90320 |==================================================================
C . 5.90578 |==================================================================
D . 5.90930 |==================================================================
oneDNN 2.6
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 6.05436 |==================================================================
B . 6.04615 |==================================================================
C . 6.05662 |==================================================================
D . 6.04798 |==================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 5.24536 |==================================================================
B . 5.24601 |==================================================================
C . 5.24648 |==================================================================
D . 5.24826 |==================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 2.21737 |==================================================================
B . 2.22008 |==================================================================
C . 2.22075 |==================================================================
D . 2.21930 |==================================================================
oneDNN 2.6
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
oneDNN 2.6
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
oneDNN 2.6
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
oneDNN 2.6
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
oneDNN 2.6
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
oneDNN 2.6
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better