8490h april

2 x Intel Xeon Platinum 8490H testing with a Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) and ASPEED on Ubuntu 22.04 via the Phoronix Test Suite.

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C/C++ Compiler Tests 2 Tests
CPU Massive 5 Tests
Creator Workloads 3 Tests
HPC - High Performance Computing 2 Tests
Machine Learning 2 Tests
Multi-Core 3 Tests
Server 2 Tests
Server CPU Tests 2 Tests

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  Test
  Duration
a
April 12 2023
  52 Minutes
b
April 12 2023
  4 Hours, 49 Minutes
c
April 13 2023
  52 Minutes
d
April 13 2023
  52 Minutes
e
April 13 2023
  52 Minutes
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8490h april 2 x Intel Xeon Platinum 8490H testing with a Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) and ASPEED on Ubuntu 22.04 via the Phoronix Test Suite. ,,"a","b","c","d","e" Processor,,2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads),2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads),2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads),2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads),2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads) Motherboard,,Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS),Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS),Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS),Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS),Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) Chipset,,Intel Device 1bce,Intel Device 1bce,Intel Device 1bce,Intel Device 1bce,Intel Device 1bce Memory,,16 x 64 GB 4800MT/s Samsung M321R8GA0BB0-CQKEG,16 x 64 GB 4800MT/s Samsung M321R8GA0BB0-CQKEG,16 x 64 GB 4800MT/s Samsung M321R8GA0BB0-CQKEG,16 x 64 GB 4800MT/s Samsung M321R8GA0BB0-CQKEG,16 x 64 GB 4800MT/s Samsung M321R8GA0BB0-CQKEG Disk,,2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007 + 960GB INTEL SSDSC2KG96,2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007 + 960GB INTEL SSDSC2KG96,2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007 + 960GB INTEL SSDSC2KG96,2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007 + 960GB INTEL SSDSC2KG96,2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007 + 960GB INTEL SSDSC2KG96 Graphics,,ASPEED,ASPEED,ASPEED,ASPEED,ASPEED Monitor,,VGA HDMI,VGA HDMI,VGA HDMI,VGA HDMI,VGA HDMI Network,,4 x Intel E810-C for QSFP + 2 x Intel X710 for 10GBASE-T,4 x Intel E810-C for QSFP + 2 x Intel X710 for 10GBASE-T,4 x Intel E810-C for QSFP + 2 x Intel X710 for 10GBASE-T,4 x Intel E810-C for QSFP + 2 x Intel X710 for 10GBASE-T,4 x Intel E810-C for QSFP + 2 x Intel X710 for 10GBASE-T OS,,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04 Kernel,,6.2.0-060200rc7daily20230208-generic (x86_64),6.2.0-060200rc7daily20230208-generic (x86_64),6.2.0-060200rc7daily20230208-generic (x86_64),6.2.0-060200rc7daily20230208-generic (x86_64),6.2.0-060200rc7daily20230208-generic (x86_64) Desktop,,GNOME Shell 42.2,GNOME Shell 42.2,GNOME Shell 42.2,GNOME Shell 42.2,GNOME Shell 42.2 Display Server,,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3 Vulkan,,1.2.204,1.2.204,1.2.204,1.2.204,1.2.204 Compiler,,GCC 11.3.0 + Clang 14.0.0-1ubuntu1,GCC 11.3.0 + Clang 14.0.0-1ubuntu1,GCC 11.3.0 + Clang 14.0.0-1ubuntu1,GCC 11.3.0 + Clang 14.0.0-1ubuntu1,GCC 11.3.0 + Clang 14.0.0-1ubuntu1 File-System,,ext4,ext4,ext4,ext4,ext4 Screen Resolution,,1920x1080,1920x1080,1920x1080,1920x1080,1920x1080 ,,"a","b","c","d","e" "srsRAN Project - Test: Downlink Processor Benchmark (Mbps)",HIB,326.5,324.2,326.7,320.8,324.1 "srsRAN Project - Test: PUSCH Processor Benchmark, Throughput Total (Mbps)",HIB,7122.4,6898.6,6547.4,7079.5,6774.5 "srsRAN Project - Test: PUSCH Processor Benchmark, Throughput Thread (Mbps)",HIB,29.9,29.8,29.7,28.8,28.9 "VVenC - Video Input: Bosphorus 4K - Video Preset: Fast (FPS)",HIB,6.308,6.332,6.314,6.443,6.388 "VVenC - Video Input: Bosphorus 4K - Video Preset: Faster (FPS)",HIB,10.065,10.055,9.967,9.956,10.067 "VVenC - Video Input: Bosphorus 1080p - Video Preset: Fast (FPS)",HIB,17.147,17.396,17.244,17.211,16.789 "VVenC - Video Input: Bosphorus 1080p - Video Preset: Faster (FPS)",HIB,28.66,30.988,30.211,27.619,30.369 "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,3.56759,3.05000,3.63585,3.50485,3.44677 "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,2.49757,2.67699,2.52848,2.37479,2.80869 "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,5.19379,4.62478,5.30769,4.87548,5.34755 "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.872476,0.978428,1.15332,0.981361,0.989308 "oneDNN - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,5.88472,5.38734,5.42071,4.97718,5.558 "oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,3.16779,3.04638,2.91381,2.83754,3.02188 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,0.408711,0.402983,0.405677,0.408416,0.400325 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,14.2667,14.6284,14.5444,14.4891,14.2212 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,0.724413,0.718746,0.716419,0.711306,0.712248 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.23996,0.314427,0.433523,0.296152,0.305503 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.434658,0.410029,0.397435,0.391957,0.413735 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.228971,0.225197,0.219341,0.225742,0.219348 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,1216.99,1155.77,1209.39,1182.32,1120.64 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,881.232,840.956,852.577,731.095,848.652 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1304.57,1232.87,1081.7,1205.38,1200.19 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,0.228741,0.223142,0.21742,0.21949,0.22202 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,0.470336,0.451393,0.457893,0.44041,0.446232 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,0.464269,0.466045,0.457996,0.462589,0.453885 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,873.147,832.338,844.358,832.574,845.726 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,1205.29,1184.14,1228.77,1184.12,1112.04 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,861.148,878.489,904.268,888.732,818.438 "TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,372.88,386.55,370.67,391.88,386.34 "TensorFlow - Device: CPU - Batch Size: 32 - Model: AlexNet (images/sec)",HIB,531.68,556.34,557.68,536.63,564.79 "TensorFlow - Device: CPU - Batch Size: 64 - Model: AlexNet (images/sec)",HIB,743.73,741.87,751.67,739.02,745.33 "TensorFlow - Device: CPU - Batch Size: 256 - Model: AlexNet (images/sec)",HIB,1091.42,1077.57,1063.47,1071.62,1062.06 "TensorFlow - Device: CPU - Batch Size: 512 - Model: AlexNet (images/sec)",HIB,1227.69,1231.85,1214.36,1225.54,1230.3 "TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,173.64,185.78,176.84,184.8,185.22 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,64.28,64.31,63.78,63.97,64.96 "TensorFlow - Device: CPU - Batch Size: 32 - Model: GoogLeNet (images/sec)",HIB,257.33,267.02,265.08,249.74,270.31 "TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,83.13,84.42,84.98,84.17,83.45 "TensorFlow - Device: CPU - Batch Size: 64 - Model: GoogLeNet (images/sec)",HIB,348,342.26,334.11,346.11,346.2 "TensorFlow - Device: CPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,103.48,102.21,104.52,103.14,102.87 "TensorFlow - Device: CPU - Batch Size: 256 - Model: GoogLeNet (images/sec)",HIB,444.17,442.93,437.97,441.29,441.44 "TensorFlow - Device: CPU - Batch Size: 256 - Model: ResNet-50 (images/sec)",HIB,130.44,128.89,127.52,128.8,128.23 "TensorFlow - Device: CPU - Batch Size: 512 - Model: GoogLeNet (images/sec)",HIB,472.26,465.31,467.33,462.37,469.14 "TensorFlow - Device: CPU - Batch Size: 512 - Model: ResNet-50 (images/sec)",HIB,135.88,134.34,135.22,134.76,133.9 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,14.03,14.20,14.21,14.04,14.3 "Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,36.5,36.66,36.79,36.31,36.36 "Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,19.36,19.70,19.94,20.13,19.54 "Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,147.25,147.73,146.59,147.18,148.11 "Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,48.81,47.84,47.65,47.73,47.43 "nginx - Connections: 100 (Reqs/sec)",HIB,,,,, "nginx - Connections: 200 (Reqs/sec)",HIB,,,,, "nginx - Connections: 500 (Reqs/sec)",HIB,250533.37,246156.11,246619.54,247581.64,248416.85 "nginx - Connections: 1000 (Reqs/sec)",HIB,,,,, "Apache HTTP Server - Concurrent Requests: 100 (Reqs/sec)",HIB,,,,, "Apache HTTP Server - Concurrent Requests: 200 (Reqs/sec)",HIB,,,,, "Apache HTTP Server - Concurrent Requests: 500 (Reqs/sec)",HIB,80395.59,83834.81,77777.03,84694.76,85357.84 "Apache HTTP Server - Concurrent Requests: 1000 (Reqs/sec)",HIB,,,,,