Xeon Silver March

Intel Xeon Silver 4216 testing with a TYAN S7100AG2NR (V4.02 BIOS) and ASPEED on Debian 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 2103218-HA-XEONSILVE43
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March 21 2021
  2 Hours, 50 Minutes
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March 21 2021
  2 Hours, 52 Minutes
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March 21 2021
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Xeon Silver March Intel Xeon Silver 4216 testing with a TYAN S7100AG2NR (V4.02 BIOS) and ASPEED on Debian 10 via the Phoronix Test Suite. 1: Processor: Intel Xeon Silver 4216 @ 3.20GHz (16 Cores / 32 Threads), Motherboard: TYAN S7100AG2NR (V4.02 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 24GB, Disk: 240GB Corsair Force MP500, Graphics: ASPEED, Audio: Realtek ALC892, Network: 2 x Intel I350 OS: Debian 10, Kernel: 4.19.0-9-amd64 (x86_64), Desktop: GNOME Shell 3.30.2, Display Server: X Server, Compiler: GCC 8.3.0, File-System: ext4, Screen Resolution: 1024x768 2: Processor: Intel Xeon Silver 4216 @ 3.20GHz (16 Cores / 32 Threads), Motherboard: TYAN S7100AG2NR (V4.02 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 24GB, Disk: 240GB Corsair Force MP500, Graphics: ASPEED, Audio: Realtek ALC892, Network: 2 x Intel I350 OS: Debian 10, Kernel: 4.19.0-9-amd64 (x86_64), Desktop: GNOME Shell 3.30.2, Display Server: X Server, Compiler: GCC 8.3.0, File-System: ext4, Screen Resolution: 1024x768 3: Processor: Intel Xeon Silver 4216 @ 3.20GHz (16 Cores / 32 Threads), Motherboard: TYAN S7100AG2NR (V4.02 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 24GB, Disk: 240GB Corsair Force MP500, Graphics: ASPEED, Audio: Realtek ALC892, Network: 2 x Intel I350 OS: Debian 10, Kernel: 4.19.0-9-amd64 (x86_64), Desktop: GNOME Shell 3.30.2, Display Server: X Server, Compiler: GCC 8.3.0, File-System: ext4, Screen Resolution: 1024x768 AOM AV1 2.1-rc Encoder Mode: Speed 0 Two-Pass Frames Per Second > Higher Is Better 1 . 0.21 |===================================================================== 2 . 0.20 |================================================================== 3 . 0.21 |===================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 6.39957 |================================================================== 2 . 6.21612 |================================================================ 3 . 6.16607 |================================================================ oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.45952 |================================================================== 2 . 1.42021 |================================================================ 3 . 1.44027 |================================================================= oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3.93294 |================================================================== 2 . 3.85096 |================================================================= 3 . 3.91852 |================================================================== Mobile Neural Network 1.1.3 Model: mobilenet-v1-1.0 ms < Lower Is Better 1 . 3.265 |=================================================================== 2 . 3.227 |=================================================================== 3 . 3.292 |==================================================================== Mobile Neural Network 1.1.3 Model: inception-v3 ms < Lower Is Better 1 . 55.10 |==================================================================== 2 . 54.55 |=================================================================== 3 . 54.09 |=================================================================== Stockfish 13 Total Time Nodes Per Second > Higher Is Better 1 . 31665033 |================================================================= 2 . 31089824 |================================================================ 3 . 31357896 |================================================================ AOM AV1 2.1-rc Encoder Mode: Speed 8 Realtime Frames Per Second > Higher Is Better 1 . 30.06 |==================================================================== 2 . 30.00 |==================================================================== 3 . 29.64 |=================================================================== Mobile Neural Network 1.1.3 Model: resnet-v2-50 ms < Lower Is Better 1 . 43.66 |=================================================================== 2 . 43.99 |==================================================================== 3 . 44.23 |==================================================================== Mobile Neural Network 1.1.3 Model: SqueezeNetV1.0 ms < Lower Is Better 1 . 8.133 |=================================================================== 2 . 8.130 |=================================================================== 3 . 8.236 |==================================================================== SVT-VP9 0.3 Tuning: VMAF Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 216.44 |================================================================== 2 . 218.37 |=================================================================== 3 . 219.08 |=================================================================== Mobile Neural Network 1.1.3 Model: MobileNetV2_224 ms < Lower Is Better 1 . 5.038 |=================================================================== 2 . 5.095 |==================================================================== 3 . 5.099 |==================================================================== LuaRadio 0.9.1 Test: Complex Phase MiB/s > Higher Is Better 1 . 434.1 |=================================================================== 2 . 438.2 |==================================================================== 3 . 439.2 |==================================================================== SVT-HEVC 1.5.0 Tuning: 10 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 258.29 |=================================================================== 2 . 257.29 |=================================================================== 3 . 255.87 |================================================================== Xcompact3d Incompact3d 2021-03-11 Input: input.i3d 129 Cells Per Direction Seconds < Lower Is Better 1 . 16.70 |=================================================================== 2 . 16.85 |==================================================================== 3 . 16.86 |==================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 6.57468 |================================================================== 2 . 6.51645 |================================================================= 3 . 6.55537 |================================================================== simdjson 0.8.2 Throughput Test: Kostya GB/s > Higher Is Better 1 . 1.13 |===================================================================== 2 . 1.12 |==================================================================== 3 . 1.13 |===================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 1.61069 |================================================================== 2 . 1.61866 |================================================================== 3 . 1.60482 |================================================================= Xcompact3d Incompact3d 2021-03-11 Input: input.i3d 193 Cells Per Direction Seconds < Lower Is Better 1 . 66.45 |==================================================================== 2 . 66.67 |==================================================================== 3 . 66.16 |=================================================================== simdjson 0.8.2 Throughput Test: PartialTweets GB/s > Higher Is Better 1 . 1.37 |===================================================================== 2 . 1.37 |===================================================================== 3 . 1.36 |==================================================================== AOM AV1 2.1-rc Encoder Mode: Speed 6 Realtime Frames Per Second > Higher Is Better 1 . 11.76 |==================================================================== 2 . 11.68 |==================================================================== 3 . 11.70 |==================================================================== SVT-VP9 0.3 Tuning: Visual Quality Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 169.62 |=================================================================== 2 . 169.81 |=================================================================== 3 . 170.48 |=================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 0.833192 |================================================================= 2 . 0.834428 |================================================================= 3 . 0.830736 |================================================================= Sysbench 1.0.20 Test: RAM / Memory MiB/sec > Higher Is Better 1 . 15699.71 |================================================================= 2 . 15632.40 |================================================================= 3 . 15678.47 |================================================================= SVT-HEVC 1.5.0 Tuning: 1 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 7.88 |===================================================================== 2 . 7.86 |===================================================================== 3 . 7.89 |===================================================================== SVT-HEVC 1.5.0 Tuning: 7 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 120.65 |=================================================================== 2 . 121.08 |=================================================================== 3 . 120.93 |=================================================================== LuaRadio 0.9.1 Test: FM Deemphasis Filter MiB/s > Higher Is Better 1 . 254.2 |==================================================================== 2 . 254.0 |==================================================================== 3 . 253.3 |==================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.26827 |================================================================== 2 . 1.27125 |================================================================== 3 . 1.27234 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 4.75486 |================================================================== 2 . 4.74640 |================================================================== 3 . 4.74045 |================================================================== LuaRadio 0.9.1 Test: Five Back to Back FIR Filters MiB/s > Higher Is Better 1 . 808.6 |==================================================================== 2 . 807.8 |==================================================================== 3 . 810.2 |==================================================================== Basis Universal 1.13 Settings: ETC1S Seconds < Lower Is Better 1 . 32.71 |==================================================================== 2 . 32.66 |==================================================================== 3 . 32.61 |==================================================================== AOM AV1 2.1-rc Encoder Mode: Speed 4 Two-Pass Frames Per Second > Higher Is Better 1 . 3.77 |===================================================================== 2 . 3.77 |===================================================================== 3 . 3.76 |===================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 1859.12 |================================================================== 2 . 1856.49 |================================================================== 3 . 1854.28 |================================================================== SVT-VP9 0.3 Tuning: PSNR/SSIM Optimized - Input: Bosphorus 1080p Frames Per Second > Higher Is Better 1 . 221.50 |=================================================================== 2 . 221.91 |=================================================================== 3 . 222.05 |=================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 11.76 |==================================================================== 2 . 11.75 |==================================================================== 3 . 11.78 |==================================================================== oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.05123 |================================================================== 2 . 1.04921 |================================================================== 3 . 1.04890 |================================================================== AOM AV1 2.1-rc Encoder Mode: Speed 6 Two-Pass Frames Per Second > Higher Is Better 1 . 9.29 |===================================================================== 2 . 9.31 |===================================================================== 3 . 9.31 |===================================================================== Basis Universal 1.13 Settings: UASTC Level 3 Seconds < Lower Is Better 1 . 57.17 |==================================================================== 2 . 57.19 |==================================================================== 3 . 57.06 |==================================================================== oneDNN 2.1.2 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3.12486 |================================================================== 2 . 3.12458 |================================================================== 3 . 3.13052 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 7.65076 |================================================================== 2 . 7.64752 |================================================================== 3 . 7.63646 |================================================================== Basis Universal 1.13 Settings: UASTC Level 0 Seconds < Lower Is Better 1 . 10.08 |==================================================================== 2 . 10.07 |==================================================================== 3 . 10.07 |==================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better 1 . 3586.70 |================================================================== 2 . 3588.58 |================================================================== 3 . 3592.55 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3589.54 |================================================================== 2 . 3593.32 |================================================================== 3 . 3588.05 |================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 1853.05 |================================================================== 2 . 1853.25 |================================================================== 3 . 1855.59 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1.88615 |================================================================== 2 . 1.88395 |================================================================== 3 . 1.88619 |================================================================== oneDNN 2.1.2 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 10.60 |==================================================================== 2 . 10.59 |==================================================================== 3 . 10.59 |==================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 1852.81 |================================================================== 2 . 1853.80 |================================================================== 3 . 1852.34 |================================================================== oneDNN 2.1.2 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 16.28 |==================================================================== 2 . 16.29 |==================================================================== 3 . 16.28 |==================================================================== Basis Universal 1.13 Settings: UASTC Level 2 Seconds < Lower Is Better 1 . 31.89 |==================================================================== 2 . 31.87 |==================================================================== 3 . 31.87 |==================================================================== oneDNN 2.1.2 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better 1 . 3589.87 |================================================================== 2 . 3589.97 |================================================================== 3 . 3592.30 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 18.43 |==================================================================== 2 . 18.43 |==================================================================== 3 . 18.44 |==================================================================== oneDNN 2.1.2 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 3.56301 |================================================================== 2 . 3.56246 |================================================================== 3 . 3.56446 |================================================================== oneDNN 2.1.2 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 1 . 21.77 |==================================================================== 2 . 21.77 |==================================================================== 3 . 21.78 |==================================================================== Sysbench 1.0.20 Test: CPU Events Per Second > Higher Is Better 1 . 22943.61 |================================================================= 2 . 22942.83 |================================================================= 3 . 22943.20 |================================================================= LuaRadio 0.9.1 Test: Hilbert Transform MiB/s > Higher Is Better 1 . 55.4 |===================================================================== 2 . 55.4 |===================================================================== 3 . 55.4 |===================================================================== simdjson 0.8.2 Throughput Test: DistinctUserID GB/s > Higher Is Better 1 . 1.48 |===================================================================== 2 . 1.48 |===================================================================== 3 . 1.48 |===================================================================== simdjson 0.8.2 Throughput Test: LargeRandom GB/s > Higher Is Better 1 . 0.38 |===================================================================== 2 . 0.38 |===================================================================== 3 . 0.38 |=====================================================================