xeon r gold onednn 3.0

2 x Intel Xeon Gold 5220R testing with a TYAN S7106 (V2.01.B40 BIOS) and ASPEED 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 2212200-NE-XEONRGOLD29

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December 20 2022

38 Minutes

December 20 2022

12 Minutes

December 20 2022

12 Minutes

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xeon r gold onednn 3.0 2 x Intel Xeon Gold 5220R testing with a TYAN S7106 (V2.01.B40 BIOS) and ASPEED on Ubuntu 20.04 via the Phoronix Test Suite. a: Processor: 2 x Intel Xeon Gold 5220R @ 3.90GHz (36 Cores / 72 Threads), Motherboard: TYAN S7106 (V2.01.B40 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 94GB, Disk: 500GB Samsung SSD 860, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel I210 + 2 x QLogic cLOM8214 1/10GbE OS: Ubuntu 20.04, Kernel: 6.1.0-phx (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1080 b: Processor: 2 x Intel Xeon Gold 5220R @ 3.90GHz (36 Cores / 72 Threads), Motherboard: TYAN S7106 (V2.01.B40 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 94GB, Disk: 500GB Samsung SSD 860, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel I210 + 2 x QLogic cLOM8214 1/10GbE OS: Ubuntu 20.04, Kernel: 6.1.0-phx (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1080 c: Processor: 2 x Intel Xeon Gold 5220R @ 3.90GHz (36 Cores / 72 Threads), Motherboard: TYAN S7106 (V2.01.B40 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 94GB, Disk: 500GB Samsung SSD 860, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel I210 + 2 x QLogic cLOM8214 1/10GbE OS: Ubuntu 20.04, Kernel: 6.1.0-phx (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1080 oneDNN 3.0 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 1.75250 |================================================================== b . 1.74919 |================================================================= c . 1.76469 |================================================================== oneDNN 3.0 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 3.81228 |================================================================ b . 3.77586 |=============================================================== c . 3.94714 |================================================================== oneDNN 3.0 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 1.73090 |=============================================================== b . 1.67674 |============================================================= c . 1.80903 |================================================================== oneDNN 3.0 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 1.29656 |================================================================== b . 1.27572 |================================================================= c . 1.26769 |================================================================= oneDNN 3.0 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 5.78540 |================================================================ b . 5.80604 |================================================================ c . 5.97953 |================================================================== oneDNN 3.0 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 3.11322 |================================================================== b . 3.04360 |================================================================= c . 3.06163 |================================================================= oneDNN 3.0 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 7.59329 |================================================================== b . 7.58018 |================================================================== c . 7.51633 |================================================================= oneDNN 3.0 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 8.14539 |================================================================== b . 7.93565 |================================================================ c . 8.11243 |================================================================== oneDNN 3.0 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 2.76971 |================================================================== b . 2.76176 |================================================================== c . 2.75773 |================================================================== oneDNN 3.0 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 7.07070 |================================================================== b . 7.10229 |================================================================== c . 7.04447 |================================================================= oneDNN 3.0 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 0.583986 |================================================================= b . 0.581283 |================================================================= c . 0.576626 |================================================================ oneDNN 3.0 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 0.696550 |================================================================= b . 0.693376 |================================================================= c . 0.695969 |================================================================= oneDNN 3.0 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 1400.64 |================================================================== b . 1397.35 |================================================================== c . 1397.94 |================================================================== oneDNN 3.0 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 800.12 |=================================================================== b . 799.43 |=================================================================== c . 797.84 |=================================================================== oneDNN 3.0 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 1409.74 |================================================================== b . 1392.36 |================================================================= c . 1401.03 |================================================================== oneDNN 3.0 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 6.41676 |================================================================== b . 6.42421 |================================================================== c . 6.43035 |================================================================== oneDNN 3.0 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 9.21597 |================================================================== b . 9.06402 |================================================================= c . 9.14855 |================================================================== oneDNN 3.0 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 9.59277 |================================================================ b . 9.61333 |================================================================ c . 9.86147 |================================================================== oneDNN 3.0 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 796.63 |================================================================== b . 805.57 |=================================================================== c . 798.11 |================================================================== oneDNN 3.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better a . 0.517990 |================================================================= b . 0.510599 |================================================================ c . 0.507931 |================================================================ oneDNN 3.0 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 1400.41 |================================================================== b . 1399.00 |================================================================== c . 1404.71 |================================================================== oneDNN 3.0 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 802.64 |================================================================== b . 802.95 |================================================================== c . 811.32 |=================================================================== oneDNN 3.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 0.215615 |=============================================================== b . 0.221767 |================================================================= c . 0.219184 |================================================================ oneDNN 3.0 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 2.34752 |================================================================ b . 2.41696 |================================================================== c . 2.35576 |================================================================