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
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
Basis Universal 1.13
Settings: ETC1S
Seconds < Lower Is Better
1 . 32.71 |====================================================================
2 . 32.66 |====================================================================
3 . 32.61 |====================================================================
Basis Universal 1.13
Settings: UASTC Level 0
Seconds < Lower Is Better
1 . 10.08 |====================================================================
2 . 10.07 |====================================================================
3 . 10.07 |====================================================================
Basis Universal 1.13
Settings: UASTC Level 2
Seconds < Lower Is Better
1 . 31.89 |====================================================================
2 . 31.87 |====================================================================
3 . 31.87 |====================================================================
Basis Universal 1.13
Settings: UASTC Level 3
Seconds < Lower Is Better
1 . 57.17 |====================================================================
2 . 57.19 |====================================================================
3 . 57.06 |====================================================================
Mobile Neural Network 1.1.3
Model: SqueezeNetV1.0
ms < Lower Is Better
1 . 8.133 |===================================================================
2 . 8.130 |===================================================================
3 . 8.236 |====================================================================
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: MobileNetV2_224
ms < Lower Is Better
1 . 5.038 |===================================================================
2 . 5.095 |====================================================================
3 . 5.099 |====================================================================
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 |===================================================================
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 |==================================================================
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 |==================================================================
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 |==================================================================
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 1D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
1 . 10.60 |====================================================================
2 . 10.59 |====================================================================
3 . 10.59 |====================================================================
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: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 6.57468 |==================================================================
2 . 6.51645 |=================================================================
3 . 6.55537 |==================================================================
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: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 7.65076 |==================================================================
2 . 7.64752 |==================================================================
3 . 7.63646 |==================================================================
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: 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: 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: 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 Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 1859.12 |==================================================================
2 . 1856.49 |==================================================================
3 . 1854.28 |==================================================================
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: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
1 . 16.28 |====================================================================
2 . 16.29 |====================================================================
3 . 16.28 |====================================================================
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: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
1 . 21.77 |====================================================================
2 . 21.77 |====================================================================
3 . 21.78 |====================================================================
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: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 1.61069 |==================================================================
2 . 1.61866 |==================================================================
3 . 1.60482 |=================================================================
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: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 0.833192 |=================================================================
2 . 0.834428 |=================================================================
3 . 0.830736 |=================================================================
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 |==================================================================
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 |====================================================================
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 |===================================================================
Stockfish 13
Total Time
Nodes Per Second > Higher Is Better
1 . 31665033 |=================================================================
2 . 31089824 |================================================================
3 . 31357896 |================================================================
Sysbench 1.0.20
Test: RAM / Memory
MiB/sec > Higher Is Better
1 . 15699.71 |=================================================================
2 . 15632.40 |=================================================================
3 . 15678.47 |=================================================================
Sysbench 1.0.20
Test: CPU
Events Per Second > Higher Is Better
1 . 22943.61 |=================================================================
2 . 22942.83 |=================================================================
3 . 22943.20 |=================================================================
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 |=====================================================================
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 |=====================================================================
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 |====================================================================
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 |=====================================================================
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 |===================================================================
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 |===================================================================
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 |===================================================================
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 |===================================================================
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 |===================================================================
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 |==================================================================
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 |====================================================================
LuaRadio 0.9.1
Test: FM Deemphasis Filter
MiB/s > Higher Is Better
1 . 254.2 |====================================================================
2 . 254.0 |====================================================================
3 . 253.3 |====================================================================
LuaRadio 0.9.1
Test: Hilbert Transform
MiB/s > Higher Is Better
1 . 55.4 |=====================================================================
2 . 55.4 |=====================================================================
3 . 55.4 |=====================================================================
LuaRadio 0.9.1
Test: Complex Phase
MiB/s > Higher Is Better
1 . 434.1 |===================================================================
2 . 438.2 |====================================================================
3 . 439.2 |====================================================================
simdjson 0.8.2
Throughput Test: Kostya
GB/s > Higher Is Better
1 . 1.13 |=====================================================================
2 . 1.12 |====================================================================
3 . 1.13 |=====================================================================
simdjson 0.8.2
Throughput Test: LargeRandom
GB/s > Higher Is Better
1 . 0.38 |=====================================================================
2 . 0.38 |=====================================================================
3 . 0.38 |=====================================================================
simdjson 0.8.2
Throughput Test: PartialTweets
GB/s > Higher Is Better
1 . 1.37 |=====================================================================
2 . 1.37 |=====================================================================
3 . 1.36 |====================================================================
simdjson 0.8.2
Throughput Test: DistinctUserID
GB/s > Higher Is Better
1 . 1.48 |=====================================================================
2 . 1.48 |=====================================================================
3 . 1.48 |=====================================================================