AMD Ryzen 7 5700G testing with a ASUS TUF GAMING B550M-PLUS (WI-FI) (2423 BIOS) and ASUS AMD Cezanne 512MB 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 2204252-NE-ZZ773288056
zz
AMD Ryzen 7 5700G testing with a ASUS TUF GAMING B550M-PLUS (WI-FI) (2423 BIOS) and ASUS AMD Cezanne 512MB on Ubuntu 21.10 via the Phoronix Test Suite.
A:
Processor: AMD Ryzen 7 5700G @ 3.80GHz (8 Cores / 16 Threads), Motherboard: ASUS TUF GAMING B550M-PLUS (WI-FI) (2423 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: 1000GB Samsung SSD 980 PRO 1TB, Graphics: ASUS AMD Cezanne 512MB (2000/1800MHz), Audio: AMD Renoir Radeon HD Audio, Monitor: MX279, Network: Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX200
OS: Ubuntu 21.10, Kernel: 5.16.0-051600rc8daily20220108-generic (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server 1.20.11 + Wayland, OpenGL: 4.6 Mesa 22.0.0-devel (git-9cb9101 2022-01-08 impish-oibaf-ppa) (LLVM 13.0.0 DRM 3.44), Vulkan: 1.2.199, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080
B:
Processor: AMD Ryzen 7 5700G @ 3.80GHz (8 Cores / 16 Threads), Motherboard: ASUS TUF GAMING B550M-PLUS (WI-FI) (2423 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: 1000GB Samsung SSD 980 PRO 1TB, Graphics: ASUS AMD Cezanne 512MB (2000/1800MHz), Audio: AMD Renoir Radeon HD Audio, Monitor: MX279, Network: Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX200
OS: Ubuntu 21.10, Kernel: 5.16.0-051600rc8daily20220108-generic (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server 1.20.11 + Wayland, OpenGL: 4.6 Mesa 22.0.0-devel (git-9cb9101 2022-01-08 impish-oibaf-ppa) (LLVM 13.0.0 DRM 3.44), Vulkan: 1.2.199, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080
C:
Processor: AMD Ryzen 7 5700G @ 3.80GHz (8 Cores / 16 Threads), Motherboard: ASUS TUF GAMING B550M-PLUS (WI-FI) (2423 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: 1000GB Samsung SSD 980 PRO 1TB, Graphics: ASUS AMD Cezanne 512MB (2000/1800MHz), Audio: AMD Renoir Radeon HD Audio, Monitor: MX279, Network: Realtek RTL8125 2.5GbE + Intel Wi-Fi 6 AX200
OS: Ubuntu 21.10, Kernel: 5.16.0-051600rc8daily20220108-generic (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server 1.20.11 + Wayland, OpenGL: 4.6 Mesa 22.0.0-devel (git-9cb9101 2022-01-08 impish-oibaf-ppa) (LLVM 13.0.0 DRM 3.44), Vulkan: 1.2.199, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080
ONNX Runtime 1.11
Model: bertsquad-12 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 496 |============================================
B . 495 |============================================
C . 788 |======================================================================
oneDNN 2.6
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 2.84523 |==================================================================
B . 2.60928 |=============================================================
C . 2.60520 |============================================================
ONNX Runtime 1.11
Model: GPT-2 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 6063 |================================================================
B . 6062 |================================================================
C . 6545 |=====================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 3580.24 |=============================================================
B . 3577.41 |=============================================================
C . 3850.32 |==================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 2.91606 |================================================================
B . 3.01126 |==================================================================
C . 2.86797 |===============================================================
SVT-AV1 1.0
Encoder Mode: Preset 12 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
A . 89.67 |====================================================================
B . 86.97 |==================================================================
C . 89.69 |====================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 8.14632 |================================================================
B . 8.36734 |==================================================================
C . 8.25826 |=================================================================
libavif avifenc 0.10
Encoder Speed: 10, Lossless
Seconds < Lower Is Better
A . 5.733 |====================================================================
B . 5.590 |==================================================================
C . 5.635 |===================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 2210.71 |================================================================
B . 2214.51 |=================================================================
C . 2265.57 |==================================================================
oneDNN 2.6
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 3.98732 |=================================================================
B . 4.05032 |==================================================================
C . 4.07036 |==================================================================
ONNX Runtime 1.11
Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 1040 |====================================================================
B . 1036 |====================================================================
C . 1056 |=====================================================================
SVT-AV1 1.0
Encoder Mode: Preset 12 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
A . 355.48 |===================================================================
B . 350.78 |==================================================================
C . 349.10 |==================================================================
ONNX Runtime 1.11
Model: super-resolution-10 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 3273 |====================================================================
B . 3309 |=====================================================================
C . 3263 |====================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 7.03558 |=================================================================
B . 7.02718 |=================================================================
C . 7.12334 |==================================================================
SVT-AV1 1.0
Encoder Mode: Preset 8 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
A . 92.45 |===================================================================
B . 93.62 |====================================================================
C . 92.82 |===================================================================
oneDNN 2.6
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1.70968 |==================================================================
B . 1.68982 |=================================================================
C . 1.70049 |==================================================================
SVT-AV1 1.0
Encoder Mode: Preset 4 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
A . 6.035 |===================================================================
B . 6.044 |===================================================================
C . 6.091 |====================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 2213.99 |=================================================================
B . 2219.82 |==================================================================
C . 2232.66 |==================================================================
oneDNN 2.6
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 11.71 |===================================================================
B . 11.73 |====================================================================
C . 11.80 |====================================================================
libavif avifenc 0.10
Encoder Speed: 6, Lossless
Seconds < Lower Is Better
A . 12.54 |====================================================================
B . 12.50 |====================================================================
C . 12.59 |====================================================================
libavif avifenc 0.10
Encoder Speed: 6
Seconds < Lower Is Better
A . 10.15 |====================================================================
B . 10.17 |====================================================================
C . 10.22 |====================================================================
SVT-AV1 1.0
Encoder Mode: Preset 10 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
A . 189.41 |===================================================================
B . 188.79 |===================================================================
C . 190.06 |===================================================================
oneDNN 2.6
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1.39092 |==================================================================
B . 1.39030 |==================================================================
C . 1.39934 |==================================================================
SVT-AV1 1.0
Encoder Mode: Preset 8 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
A . 26.39 |====================================================================
B . 26.32 |====================================================================
C . 26.25 |====================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 2213.88 |==================================================================
B . 2225.46 |==================================================================
C . 2214.16 |==================================================================
oneDNN 2.6
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 4.48476 |==================================================================
B . 4.48147 |==================================================================
C . 4.50371 |==================================================================
libavif avifenc 0.10
Encoder Speed: 0
Seconds < Lower Is Better
A . 158.84 |===================================================================
B . 158.88 |===================================================================
C . 158.15 |===================================================================
oneDNN 2.6
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 22.35 |====================================================================
B . 22.40 |====================================================================
C . 22.45 |====================================================================
oneDNN 2.6
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 23.47 |====================================================================
B . 23.56 |====================================================================
C . 23.46 |====================================================================
SVT-AV1 1.0
Encoder Mode: Preset 10 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
A . 65.14 |====================================================================
B . 64.92 |====================================================================
C . 65.19 |====================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 3584.64 |==================================================================
B . 3577.26 |==================================================================
C . 3591.14 |==================================================================
ONNX Runtime 1.11
Model: yolov4 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 299 |======================================================================
B . 298 |======================================================================
C . 299 |======================================================================
SVT-AV1 1.0
Encoder Mode: Preset 4 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
A . 1.981 |====================================================================
B . 1.984 |====================================================================
C . 1.978 |====================================================================
libavif avifenc 0.10
Encoder Speed: 2
Seconds < Lower Is Better
A . 74.46 |====================================================================
B . 74.24 |====================================================================
C . 74.28 |====================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 3583.10 |==================================================================
B . 3580.17 |==================================================================
C . 3585.22 |==================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1.95484 |==================================================================
B . 1.95319 |==================================================================
C . 1.95551 |==================================================================
ONNX Runtime 1.11
Model: fcn-resnet101-11 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 47 |=======================================================================
B . 47 |=======================================================================
C . 47 |=======================================================================
Java JMH
Ops/s > Higher 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: Convolution Batch Shapes Auto - 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