new okt

Intel Xeon Silver 4216 testing with a TYAN S7100AG2NR (V4.02 BIOS) and ASPEED on Debian 12 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 2310241-NE-NEWOKT15298
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results

Limit displaying results to tests within:

AV1 2 Tests
CPU Massive 2 Tests
Creator Workloads 7 Tests
Encoding 2 Tests
Game Development 2 Tests
HPC - High Performance Computing 3 Tests
Machine Learning 2 Tests
Multi-Core 7 Tests
Intel oneAPI 5 Tests
Server CPU Tests 2 Tests
Video Encoding 2 Tests

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Geometric Means Per-Suite/Category
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
a
October 24 2023
  1 Hour, 56 Minutes
b
October 24 2023
  1 Hour, 56 Minutes
Invert Hiding All Results Option
  1 Hour, 56 Minutes
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):

new okt Intel Xeon Silver 4216 testing with a TYAN S7100AG2NR (V4.02 BIOS) and ASPEED on Debian 12 via the Phoronix Test Suite. a: 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: 46GB, Disk: 240GB Corsair Force MP500, Graphics: ASPEED, Audio: Realtek ALC892, Network: 2 x Intel I350 OS: Debian 12, Kernel: 6.1.0-11-amd64 (x86_64), Display Server: X Server, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1024x768 b: 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: 46GB, Disk: 240GB Corsair Force MP500, Graphics: ASPEED, Audio: Realtek ALC892, Network: 2 x Intel I350 OS: Debian 12, Kernel: 6.1.0-11-amd64 (x86_64), Display Server: X Server, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1024x768 OpenVINO 2023.1 Model: Face Detection FP16 - Device: CPU FPS > Higher Is Better a . 2.31 |===================================================================== b . 2.30 |===================================================================== OpenVINO 2023.1 Model: Person Detection FP16 - Device: CPU FPS > Higher Is Better a . 24.51 |==================================================================== b . 24.29 |=================================================================== OpenVINO 2023.1 Model: Person Detection FP32 - Device: CPU FPS > Higher Is Better a . 24.46 |==================================================================== b . 24.52 |==================================================================== OpenVINO 2023.1 Model: Vehicle Detection FP16 - Device: CPU FPS > Higher Is Better a . 170.73 |================================================================== b . 172.22 |=================================================================== OpenVINO 2023.1 Model: Face Detection FP16-INT8 - Device: CPU FPS > Higher Is Better a . 8.53 |===================================================================== b . 8.59 |===================================================================== OpenVINO 2023.1 Model: Face Detection Retail FP16 - Device: CPU FPS > Higher Is Better a . 586.21 |=================================================================== b . 587.05 |=================================================================== OpenVINO 2023.1 Model: Road Segmentation ADAS FP16 - Device: CPU FPS > Higher Is Better a . 80.07 |==================================================================== b . 79.50 |==================================================================== Embree 4.3 Binary: Pathtracer - Model: Crown Frames Per Second > Higher Is Better a . 13.21 |==================================================================== b . 13.11 |=================================================================== Embree 4.3 Binary: Pathtracer ISPC - Model: Crown Frames Per Second > Higher Is Better a . 9.9432 |=================================================================== b . 9.9097 |=================================================================== Embree 4.3 Binary: Pathtracer - Model: Asian Dragon Frames Per Second > Higher Is Better a . 16.09 |==================================================================== b . 15.96 |=================================================================== Embree 4.3 Binary: Pathtracer - Model: Asian Dragon Obj Frames Per Second > Higher Is Better a . 14.47 |==================================================================== b . 14.45 |==================================================================== Embree 4.3 Binary: Pathtracer ISPC - Model: Asian Dragon Frames Per Second > Higher Is Better a . 13.69 |==================================================================== b . 13.62 |==================================================================== Embree 4.3 Binary: Pathtracer ISPC - Model: Asian Dragon Obj Frames Per Second > Higher Is Better a . 11.76 |==================================================================== b . 11.76 |==================================================================== SVT-AV1 1.7 Encoder Mode: Preset 4 - Input: Bosphorus 4K Frames Per Second > Higher Is Better a . 1.938 |==================================================================== b . 1.950 |==================================================================== SVT-AV1 1.7 Encoder Mode: Preset 8 - Input: Bosphorus 4K Frames Per Second > Higher Is Better a . 29.89 |=================================================================== b . 30.16 |==================================================================== SVT-AV1 1.7 Encoder Mode: Preset 12 - Input: Bosphorus 4K Frames Per Second > Higher Is Better a . 73.23 |=================================================================== b . 73.80 |==================================================================== SVT-AV1 1.7 Encoder Mode: Preset 13 - Input: Bosphorus 4K Frames Per Second > Higher Is Better a . 73.36 |==================================================================== b . 73.22 |==================================================================== SVT-AV1 1.7 Encoder Mode: Preset 4 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better a . 5.348 |=================================================================== b . 5.442 |==================================================================== SVT-AV1 1.7 Encoder Mode: Preset 8 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better a . 44.23 |==================================================================== b . 44.41 |==================================================================== SVT-AV1 1.7 Encoder Mode: Preset 12 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better a . 181.54 |=================================================================== b . 177.56 |================================================================== SVT-AV1 1.7 Encoder Mode: Preset 13 - Input: Bosphorus 1080p Frames Per Second > Higher Is Better a . 180.47 |================================================================== b . 182.35 |=================================================================== Intel Open Image Denoise 2.1 Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.32 |===================================================================== b . 0.32 |===================================================================== Intel Open Image Denoise 2.1 Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.32 |=================================================================== b . 0.33 |===================================================================== Intel Open Image Denoise 2.1 Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only Images / Sec > Higher Is Better a . 0.15 |===================================================================== b . 0.15 |===================================================================== OpenVKL 2.0.0 Benchmark: vklBenchmarkCPU ISPC Items / Sec > Higher Is Better a . 275 |====================================================================== b . 274 |====================================================================== OpenVKL 2.0.0 Benchmark: vklBenchmarkCPU Scalar Items / Sec > Higher Is Better a . 95 |======================================================================= b . 94 |====================================================================== QuantLib 1.32 Configuration: Multi-Threaded MFLOPS > Higher Is Better a . 31381.4 |================================================================== b . 31348.2 |================================================================== QuantLib 1.32 Configuration: Single-Threaded MFLOPS > Higher Is Better a . 2056.3 |================================================================== b . 2076.5 |=================================================================== oneDNN 3.3 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 1.28255 |================================================================== b . 1.17212 |============================================================ oneDNN 3.3 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 1.54511 |================================================================== b . 1.46367 |=============================================================== oneDNN 3.3 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 10.58 |=============================================================== b . 11.44 |==================================================================== oneDNN 3.3 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 3.12064 |================================================================== b . 3.10326 |================================================================== oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 6.25910 |================================================================== b . 6.14564 |================================================================= oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 2.59994 |================================================================== b . 1.38733 |=================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 1.90079 |================================================================== b . 1.90069 |================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 4007.84 |================================================================== b . 3545.80 |========================================================== oneDNN 3.3 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 16.30 |==================================================================== b . 16.31 |==================================================================== oneDNN 3.3 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 23.78 |==================================================================== b . 22.27 |================================================================ oneDNN 3.3 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 21.90 |==================================================================== b . 21.91 |==================================================================== oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better a . 1824.27 |================================================================== b . 1807.68 |================================================================= oneDNN 3.3 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 3614.70 |================================================================== b . 3495.73 |================================================================ oneDNN 3.3 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better a . 1797.93 |========================================================= b . 2094.22 |================================================================== OpenVINO 2023.1 Model: Face Detection FP16 - Device: CPU ms < Lower Is Better a . 3455.53 |================================================================== b . 3462.59 |================================================================== OpenVINO 2023.1 Model: Person Detection FP16 - Device: CPU ms < Lower Is Better a . 325.69 |================================================================== b . 328.78 |=================================================================== OpenVINO 2023.1 Model: Person Detection FP32 - Device: CPU ms < Lower Is Better a . 326.23 |=================================================================== b . 325.91 |=================================================================== OpenVINO 2023.1 Model: Vehicle Detection FP16 - Device: CPU ms < Lower Is Better a . 46.82 |==================================================================== b . 46.41 |=================================================================== OpenVINO 2023.1 Model: Face Detection FP16-INT8 - Device: CPU ms < Lower Is Better a . 935.89 |=================================================================== b . 925.88 |================================================================== OpenVINO 2023.1 Model: Face Detection Retail FP16 - Device: CPU ms < Lower Is Better a . 13.62 |==================================================================== b . 13.60 |==================================================================== OpenVINO 2023.1 Model: Road Segmentation ADAS FP16 - Device: CPU ms < Lower Is Better a . 99.82 |=================================================================== b . 100.56 |=================================================================== OpenRadioss 2023.09.15 Model: Bumper Beam Seconds < Lower Is Better a . 187.21 |================================================================== b . 188.77 |=================================================================== OpenRadioss 2023.09.15 Model: Chrysler Neon 1M Seconds < Lower Is Better a . 871.61 |=================================================================== b . 864.38 |================================================================== OpenRadioss 2023.09.15 Model: Cell Phone Drop Test Seconds < Lower Is Better a . 116.59 |=================================================================== b . 115.90 |=================================================================== OpenRadioss 2023.09.15 Model: Bird Strike on Windshield Seconds < Lower Is Better a . 312.50 |=================================================================== b . 311.49 |=================================================================== OpenRadioss 2023.09.15 Model: Rubber O-Ring Seal Installation Seconds < Lower Is Better a . 200.86 |=================================================================== b . 198.69 |================================================================== OpenRadioss 2023.09.15 Model: INIVOL and Fluid Structure Interaction Drop Container Seconds < Lower Is Better a . 553.97 |=================================================================== b . 553.87 |=================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 Seconds < Lower Is Better a . 7.976 |==================================================================== b . 7.159 |============================================================= easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 Seconds < Lower Is Better a . 157.06 |================================================================= b . 161.84 |=================================================================== easyWave r34 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400 Seconds < Lower Is Better a . 404.94 |=================================================================== b . 396.88 |================================================================== libavif avifenc 1.0 Encoder Speed: 0 Seconds < Lower Is Better a . 189.70 |=================================================================== b . 188.63 |=================================================================== libavif avifenc 1.0 Encoder Speed: 2 Seconds < Lower Is Better a . 90.46 |=================================================================== b . 91.59 |==================================================================== libavif avifenc 1.0 Encoder Speed: 6 Seconds < Lower Is Better a . 7.867 |================================================================== b . 8.129 |==================================================================== libavif avifenc 1.0 Encoder Speed: 6, Lossless Seconds < Lower Is Better a . 13.27 |==================================================================== b . 13.36 |==================================================================== libavif avifenc 1.0 Encoder Speed: 10, Lossless Seconds < Lower Is Better a . 7.667 |==================================================================== b . 7.708 |====================================================================