AMD Ryzen Threadripper 3990X 64-Core testing with a Gigabyte TRX40 AORUS PRO WIFI (F4p BIOS) and AMD Radeon RX 5700 8GB on Pop 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 2203314-PTS-ONEDNNON39
onednn onnx threadripper
AMD Ryzen Threadripper 3990X 64-Core testing with a Gigabyte TRX40 AORUS PRO WIFI (F4p BIOS) and AMD Radeon RX 5700 8GB on Pop 21.10 via the Phoronix Test Suite.
A:
Processor: AMD Ryzen Threadripper 3990X 64-Core @ 2.90GHz (64 Cores / 128 Threads), Motherboard: Gigabyte TRX40 AORUS PRO WIFI (F4p BIOS), Chipset: AMD Starship/Matisse, Memory: 128GB, Disk: Samsung SSD 970 EVO Plus 500GB, Graphics: AMD Radeon RX 5700 8GB (1750/875MHz), Audio: AMD Navi 10 HDMI Audio, Monitor: DELL P2415Q, Network: Intel I211 + Intel Wi-Fi 6 AX200
OS: Pop 21.10, Kernel: 5.17.0-rc1-sched-core-phx (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server, OpenGL: 4.6 Mesa 21.2.2 (LLVM 12.0.1), Vulkan: 1.2.182, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 3840x2160
B:
Processor: AMD Ryzen Threadripper 3990X 64-Core @ 2.90GHz (64 Cores / 128 Threads), Motherboard: Gigabyte TRX40 AORUS PRO WIFI (F4p BIOS), Chipset: AMD Starship/Matisse, Memory: 128GB, Disk: Samsung SSD 970 EVO Plus 500GB, Graphics: AMD Radeon RX 5700 8GB (1750/875MHz), Audio: AMD Navi 10 HDMI Audio, Monitor: DELL P2415Q, Network: Intel I211 + Intel Wi-Fi 6 AX200
OS: Pop 21.10, Kernel: 5.17.0-rc1-sched-core-phx (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server, OpenGL: 4.6 Mesa 21.2.2 (LLVM 12.0.1), Vulkan: 1.2.182, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 3840x2160
C:
Processor: AMD Ryzen Threadripper 3990X 64-Core @ 2.90GHz (64 Cores / 128 Threads), Motherboard: Gigabyte TRX40 AORUS PRO WIFI (F4p BIOS), Chipset: AMD Starship/Matisse, Memory: 128GB, Disk: Samsung SSD 970 EVO Plus 500GB, Graphics: AMD Radeon RX 5700 8GB (1750/875MHz), Audio: AMD Navi 10 HDMI Audio, Monitor: DELL P2415Q, Network: Intel I211 + Intel Wi-Fi 6 AX200
OS: Pop 21.10, Kernel: 5.17.0-rc1-sched-core-phx (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server, OpenGL: 4.6 Mesa 21.2.2 (LLVM 12.0.1), Vulkan: 1.2.182, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 3840x2160
D:
Processor: AMD Ryzen Threadripper 3990X 64-Core @ 2.90GHz (64 Cores / 128 Threads), Motherboard: Gigabyte TRX40 AORUS PRO WIFI (F4p BIOS), Chipset: AMD Starship/Matisse, Memory: 128GB, Disk: Samsung SSD 970 EVO Plus 500GB, Graphics: AMD Radeon RX 5700 8GB (1750/875MHz), Audio: AMD Navi 10 HDMI Audio, Monitor: DELL P2415Q, Network: Intel I211 + Intel Wi-Fi 6 AX200
OS: Pop 21.10, Kernel: 5.17.0-rc1-sched-core-phx (x86_64), Desktop: GNOME Shell 40.5, Display Server: X Server, OpenGL: 4.6 Mesa 21.2.2 (LLVM 12.0.1), Vulkan: 1.2.182, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 3840x2160
ONNX Runtime 1.11
Model: GPT-2 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 3461 |====================================================================
B . 3512 |=====================================================================
C . 3529 |=====================================================================
D . 3495 |====================================================================
ONNX Runtime 1.11
Model: GPT-2 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 4219 |============================================================
B . 4710 |===================================================================
C . 4823 |=====================================================================
D . 4441 |================================================================
ONNX Runtime 1.11
Model: yolov4 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 361 |======================================================================
B . 362 |======================================================================
C . 362 |======================================================================
D . 361 |======================================================================
ONNX Runtime 1.11
Model: yolov4 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 293 |====================================================================
B . 293 |====================================================================
C . 295 |=====================================================================
D . 300 |======================================================================
ONNX Runtime 1.11
Model: bertsquad-12 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 424 |=====================================================================
B . 425 |=====================================================================
C . 432 |======================================================================
D . 421 |====================================================================
ONNX Runtime 1.11
Model: bertsquad-12 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 531 |=========================================================
B . 647 |======================================================================
C . 646 |======================================================================
D . 642 |=====================================================================
ONNX Runtime 1.11
Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 82 |=======================================================================
B . 80 |=====================================================================
C . 81 |======================================================================
D . 81 |======================================================================
ONNX Runtime 1.11
Model: fcn-resnet101-11 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 153 |====================================================================
B . 156 |======================================================================
C . 153 |====================================================================
D . 157 |======================================================================
ONNX Runtime 1.11
Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 1088 |=====================================================================
B . 1072 |====================================================================
C . 1079 |====================================================================
D . 1079 |====================================================================
ONNX Runtime 1.11
Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 995 |====================================================================
B . 1010 |=====================================================================
C . 1017 |=====================================================================
D . 991 |===================================================================
ONNX Runtime 1.11
Model: super-resolution-10 - Device: CPU - Executor: Parallel
Inferences Per Minute > Higher Is Better
A . 3815 |=====================================================================
B . 3780 |====================================================================
C . 3784 |====================================================================
D . 3731 |===================================================================
ONNX Runtime 1.11
Model: super-resolution-10 - Device: CPU - Executor: Standard
Inferences Per Minute > Higher Is Better
A . 7323 |===================================================================
B . 6401 |==========================================================
C . 7560 |=====================================================================
D . 7375 |===================================================================
oneDNN 2.6
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 2.00953 |==================================================================
B . 1.96420 |=================================================================
C . 1.99383 |=================================================================
D . 1.91681 |===============================================================
oneDNN 2.6
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 5.54387 |=========================================================
B . 6.27072 |=================================================================
C . 6.28663 |=================================================================
D . 6.40806 |==================================================================
oneDNN 2.6
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 2.18433 |============================================================
B . 2.35161 |================================================================
C . 2.37403 |=================================================================
D . 2.42176 |==================================================================
oneDNN 2.6
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1.13774 |==================================================================
B . 1.11774 |=================================================================
C . 1.11927 |=================================================================
D . 1.10705 |================================================================
oneDNN 2.6
Harness: IP 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: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 0.941266 |=================================================================
B . 0.910056 |===============================================================
C . 0.904110 |==============================================================
D . 0.928404 |================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 6.68005 |================================================================
B . 6.82166 |=================================================================
C . 6.85039 |=================================================================
D . 6.90607 |==================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 2.10617 |==================================================================
B . 2.11025 |==================================================================
C . 2.11212 |==================================================================
D . 2.11146 |==================================================================
oneDNN 2.6
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 6.39430 |=================================================================
B . 6.43330 |==================================================================
C . 6.44689 |==================================================================
D . 6.44330 |==================================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1.52619 |=================================================================
B . 1.49871 |===============================================================
C . 1.56000 |==================================================================
D . 1.45511 |==============================================================
oneDNN 2.6
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 0.979135 |================================================================
B . 0.992713 |=================================================================
C . 0.987020 |=================================================================
D . 0.984819 |================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 4959.96 |=================================================================
B . 5003.99 |==================================================================
C . 4964.59 |=================================================================
D . 4882.28 |================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 1260.20 |==================================================================
B . 1251.24 |==================================================================
C . 1221.12 |================================================================
D . 1211.44 |===============================================================
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 4954.34 |=================================================================
B . 5034.83 |==================================================================
C . 5003.38 |==================================================================
D . 4950.97 |=================================================================
oneDNN 2.6
Harness: Convolution Batch Shapes Auto - 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: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 1236.75 |==================================================================
B . 1246.07 |==================================================================
C . 1238.60 |==================================================================
D . 1223.53 |=================================================================
oneDNN 2.6
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
ms < Lower Is Better
A . 7.59165 |==================================================================
B . 6.93013 |============================================================
C . 7.57941 |==================================================================
D . 7.04981 |=============================================================
oneDNN 2.6
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 5028.06 |==================================================================
B . 4997.82 |==================================================================
C . 5011.49 |==================================================================
D . 4884.90 |================================================================
oneDNN 2.6
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
A . 1208.52 |================================================================
B . 1242.44 |=================================================================
C . 1250.99 |==================================================================
D . 1254.39 |==================================================================
oneDNN 2.6
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
A . 11.60 |==================================================================
B . 11.34 |=================================================================
C . 11.90 |====================================================================
D . 11.75 |===================================================================
oneDNN 2.6
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better