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.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2304136-NE-8490HAPRI45
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:
Processor: 2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads), Motherboard: Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS), Chipset: Intel Device 1bce, Memory: 16 x 64 GB 4800MT/s Samsung M321R8GA0BB0-CQKEG, Disk: 2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007 + 960GB INTEL SSDSC2KG96, Graphics: ASPEED, Monitor: VGA HDMI, Network: 4 x Intel E810-C for QSFP + 2 x Intel X710 for 10GBASE-T
OS: Ubuntu 22.04, Kernel: 6.2.0-060200rc7daily20230208-generic (x86_64), Desktop: GNOME Shell 42.2, Display Server: X Server 1.21.1.3, Vulkan: 1.2.204, Compiler: GCC 11.3.0 + Clang 14.0.0-1ubuntu1, File-System: ext4, Screen Resolution: 1920x1080
b:
Processor: 2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads), Motherboard: Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS), Chipset: Intel Device 1bce, Memory: 16 x 64 GB 4800MT/s Samsung M321R8GA0BB0-CQKEG, Disk: 2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007 + 960GB INTEL SSDSC2KG96, Graphics: ASPEED, Monitor: VGA HDMI, Network: 4 x Intel E810-C for QSFP + 2 x Intel X710 for 10GBASE-T
OS: Ubuntu 22.04, Kernel: 6.2.0-060200rc7daily20230208-generic (x86_64), Desktop: GNOME Shell 42.2, Display Server: X Server 1.21.1.3, Vulkan: 1.2.204, Compiler: GCC 11.3.0 + Clang 14.0.0-1ubuntu1, File-System: ext4, Screen Resolution: 1920x1080
c:
Processor: 2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads), Motherboard: Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS), Chipset: Intel Device 1bce, Memory: 16 x 64 GB 4800MT/s Samsung M321R8GA0BB0-CQKEG, Disk: 2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007 + 960GB INTEL SSDSC2KG96, Graphics: ASPEED, Monitor: VGA HDMI, Network: 4 x Intel E810-C for QSFP + 2 x Intel X710 for 10GBASE-T
OS: Ubuntu 22.04, Kernel: 6.2.0-060200rc7daily20230208-generic (x86_64), Desktop: GNOME Shell 42.2, Display Server: X Server 1.21.1.3, Vulkan: 1.2.204, Compiler: GCC 11.3.0 + Clang 14.0.0-1ubuntu1, File-System: ext4, Screen Resolution: 1920x1080
d:
Processor: 2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads), Motherboard: Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS), Chipset: Intel Device 1bce, Memory: 16 x 64 GB 4800MT/s Samsung M321R8GA0BB0-CQKEG, Disk: 2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007 + 960GB INTEL SSDSC2KG96, Graphics: ASPEED, Monitor: VGA HDMI, Network: 4 x Intel E810-C for QSFP + 2 x Intel X710 for 10GBASE-T
OS: Ubuntu 22.04, Kernel: 6.2.0-060200rc7daily20230208-generic (x86_64), Desktop: GNOME Shell 42.2, Display Server: X Server 1.21.1.3, Vulkan: 1.2.204, Compiler: GCC 11.3.0 + Clang 14.0.0-1ubuntu1, File-System: ext4, Screen Resolution: 1920x1080
e:
Processor: 2 x Intel Xeon Platinum 8490H @ 3.50GHz (120 Cores / 240 Threads), Motherboard: Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS), Chipset: Intel Device 1bce, Memory: 16 x 64 GB 4800MT/s Samsung M321R8GA0BB0-CQKEG, Disk: 2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007 + 960GB INTEL SSDSC2KG96, Graphics: ASPEED, Monitor: VGA HDMI, Network: 4 x Intel E810-C for QSFP + 2 x Intel X710 for 10GBASE-T
OS: Ubuntu 22.04, Kernel: 6.2.0-060200rc7daily20230208-generic (x86_64), Desktop: GNOME Shell 42.2, Display Server: X Server 1.21.1.3, Vulkan: 1.2.204, Compiler: GCC 11.3.0 + Clang 14.0.0-1ubuntu1, File-System: ext4, Screen Resolution: 1920x1080
srsRAN Project 23.3
Test: Downlink Processor Benchmark
Mbps > Higher Is Better
a . 326.5 |====================================================================
b . 324.2 |===================================================================
c . 326.7 |====================================================================
d . 320.8 |===================================================================
e . 324.1 |===================================================================
srsRAN Project 23.3
Test: PUSCH Processor Benchmark, Throughput Total
Mbps > Higher Is Better
a . 7122.4 |===================================================================
b . 6898.6 |=================================================================
c . 6547.4 |==============================================================
d . 7079.5 |===================================================================
e . 6774.5 |================================================================
srsRAN Project 23.3
Test: PUSCH Processor Benchmark, Throughput Thread
Mbps > Higher Is Better
a . 29.9 |=====================================================================
b . 29.8 |=====================================================================
c . 29.7 |=====================================================================
d . 28.8 |==================================================================
e . 28.9 |===================================================================
VVenC 1.8
Video Input: Bosphorus 4K - Video Preset: Fast
Frames Per Second > Higher Is Better
a . 6.308 |===================================================================
b . 6.332 |===================================================================
c . 6.314 |===================================================================
d . 6.443 |====================================================================
e . 6.388 |===================================================================
VVenC 1.8
Video Input: Bosphorus 4K - Video Preset: Faster
Frames Per Second > Higher Is Better
a . 10.065 |===================================================================
b . 10.055 |===================================================================
c . 9.967 |==================================================================
d . 9.956 |==================================================================
e . 10.067 |===================================================================
VVenC 1.8
Video Input: Bosphorus 1080p - Video Preset: Fast
Frames Per Second > Higher Is Better
a . 17.15 |===================================================================
b . 17.40 |====================================================================
c . 17.24 |===================================================================
d . 17.21 |===================================================================
e . 16.79 |==================================================================
VVenC 1.8
Video Input: Bosphorus 1080p - Video Preset: Faster
Frames Per Second > Higher Is Better
a . 28.66 |===============================================================
b . 30.99 |====================================================================
c . 30.21 |==================================================================
d . 27.62 |=============================================================
e . 30.37 |===================================================================
oneDNN 3.1
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 3.56759 |=================================================================
b . 3.05000 |=======================================================
c . 3.63585 |==================================================================
d . 3.50485 |================================================================
e . 3.44677 |===============================================================
oneDNN 3.1
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 2.49757 |===========================================================
b . 2.67699 |===============================================================
c . 2.52848 |===========================================================
d . 2.37479 |========================================================
e . 2.80869 |==================================================================
oneDNN 3.1
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 5.19379 |================================================================
b . 4.62478 |=========================================================
c . 5.30769 |==================================================================
d . 4.87548 |============================================================
e . 5.34755 |==================================================================
oneDNN 3.1
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.872476 |=================================================
b . 0.978428 |=======================================================
c . 1.153320 |=================================================================
d . 0.981361 |=======================================================
e . 0.989308 |========================================================
oneDNN 3.1
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 5.88472 |==================================================================
b . 5.38734 |============================================================
c . 5.42071 |=============================================================
d . 4.97718 |========================================================
e . 5.55800 |==============================================================
oneDNN 3.1
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 3.16779 |==================================================================
b . 3.04638 |===============================================================
c . 2.91381 |=============================================================
d . 2.83754 |===========================================================
e . 3.02188 |===============================================================
oneDNN 3.1
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 0.408711 |=================================================================
b . 0.402983 |================================================================
c . 0.405677 |=================================================================
d . 0.408416 |=================================================================
e . 0.400325 |================================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 14.27 |==================================================================
b . 14.63 |====================================================================
c . 14.54 |====================================================================
d . 14.49 |===================================================================
e . 14.22 |==================================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 0.724413 |=================================================================
b . 0.718746 |================================================================
c . 0.716419 |================================================================
d . 0.711306 |================================================================
e . 0.712248 |================================================================
oneDNN 3.1
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.239960 |====================================
b . 0.314427 |===============================================
c . 0.433523 |=================================================================
d . 0.296152 |============================================
e . 0.305503 |==============================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.434658 |=================================================================
b . 0.410029 |=============================================================
c . 0.397435 |===========================================================
d . 0.391957 |===========================================================
e . 0.413735 |==============================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.228971 |=================================================================
b . 0.225197 |================================================================
c . 0.219341 |==============================================================
d . 0.225742 |================================================================
e . 0.219348 |==============================================================
oneDNN 3.1
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 1216.99 |==================================================================
b . 1155.77 |===============================================================
c . 1209.39 |==================================================================
d . 1182.32 |================================================================
e . 1120.64 |=============================================================
oneDNN 3.1
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 881.23 |===================================================================
b . 840.96 |================================================================
c . 852.58 |=================================================================
d . 731.10 |========================================================
e . 848.65 |=================================================================
oneDNN 3.1
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 1304.57 |==================================================================
b . 1232.87 |==============================================================
c . 1081.70 |=======================================================
d . 1205.38 |=============================================================
e . 1200.19 |=============================================================
oneDNN 3.1
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 0.228741 |=================================================================
b . 0.223142 |===============================================================
c . 0.217420 |==============================================================
d . 0.219490 |==============================================================
e . 0.222020 |===============================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 0.470336 |=================================================================
b . 0.451393 |==============================================================
c . 0.457893 |===============================================================
d . 0.440410 |=============================================================
e . 0.446232 |==============================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 0.464269 |=================================================================
b . 0.466045 |=================================================================
c . 0.457996 |================================================================
d . 0.462589 |=================================================================
e . 0.453885 |===============================================================
oneDNN 3.1
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 873.15 |===================================================================
b . 832.34 |================================================================
c . 844.36 |=================================================================
d . 832.57 |================================================================
e . 845.73 |=================================================================
oneDNN 3.1
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 1205.29 |=================================================================
b . 1184.14 |================================================================
c . 1228.77 |==================================================================
d . 1184.12 |================================================================
e . 1112.04 |============================================================
oneDNN 3.1
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 861.15 |================================================================
b . 878.49 |=================================================================
c . 904.27 |===================================================================
d . 888.73 |==================================================================
e . 818.44 |=============================================================
TensorFlow 2.12
Device: CPU - Batch Size: 16 - Model: AlexNet
images/sec > Higher Is Better
a . 372.88 |================================================================
b . 386.55 |==================================================================
c . 370.67 |===============================================================
d . 391.88 |===================================================================
e . 386.34 |==================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 32 - Model: AlexNet
images/sec > Higher Is Better
a . 531.68 |===============================================================
b . 556.34 |==================================================================
c . 557.68 |==================================================================
d . 536.63 |================================================================
e . 564.79 |===================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 64 - Model: AlexNet
images/sec > Higher Is Better
a . 743.73 |==================================================================
b . 741.87 |==================================================================
c . 751.67 |===================================================================
d . 739.02 |==================================================================
e . 745.33 |==================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 256 - Model: AlexNet
images/sec > Higher Is Better
a . 1091.42 |==================================================================
b . 1077.57 |=================================================================
c . 1063.47 |================================================================
d . 1071.62 |=================================================================
e . 1062.06 |================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 512 - Model: AlexNet
images/sec > Higher Is Better
a . 1227.69 |==================================================================
b . 1231.85 |==================================================================
c . 1214.36 |=================================================================
d . 1225.54 |==================================================================
e . 1230.30 |==================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 16 - Model: GoogLeNet
images/sec > Higher Is Better
a . 173.64 |===============================================================
b . 185.78 |===================================================================
c . 176.84 |================================================================
d . 184.80 |===================================================================
e . 185.22 |===================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 16 - Model: ResNet-50
images/sec > Higher Is Better
a . 64.28 |===================================================================
b . 64.31 |===================================================================
c . 63.78 |===================================================================
d . 63.97 |===================================================================
e . 64.96 |====================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 32 - Model: GoogLeNet
images/sec > Higher Is Better
a . 257.33 |================================================================
b . 267.02 |==================================================================
c . 265.08 |==================================================================
d . 249.74 |==============================================================
e . 270.31 |===================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 32 - Model: ResNet-50
images/sec > Higher Is Better
a . 83.13 |===================================================================
b . 84.42 |====================================================================
c . 84.98 |====================================================================
d . 84.17 |===================================================================
e . 83.45 |===================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 64 - Model: GoogLeNet
images/sec > Higher Is Better
a . 348.00 |===================================================================
b . 342.26 |==================================================================
c . 334.11 |================================================================
d . 346.11 |===================================================================
e . 346.20 |===================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 64 - Model: ResNet-50
images/sec > Higher Is Better
a . 103.48 |==================================================================
b . 102.21 |==================================================================
c . 104.52 |===================================================================
d . 103.14 |==================================================================
e . 102.87 |==================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 256 - Model: GoogLeNet
images/sec > Higher Is Better
a . 444.17 |===================================================================
b . 442.93 |===================================================================
c . 437.97 |==================================================================
d . 441.29 |===================================================================
e . 441.44 |===================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 256 - Model: ResNet-50
images/sec > Higher Is Better
a . 130.44 |===================================================================
b . 128.89 |==================================================================
c . 127.52 |==================================================================
d . 128.80 |==================================================================
e . 128.23 |==================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 512 - Model: GoogLeNet
images/sec > Higher Is Better
a . 472.26 |===================================================================
b . 465.31 |==================================================================
c . 467.33 |==================================================================
d . 462.37 |==================================================================
e . 469.14 |===================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 512 - Model: ResNet-50
images/sec > Higher Is Better
a . 135.88 |===================================================================
b . 134.34 |==================================================================
c . 135.22 |===================================================================
d . 134.76 |==================================================================
e . 133.90 |==================================================================
Blender 3.5
Blend File: BMW27 - Compute: CPU-Only
Seconds < Lower Is Better
a . 14.03 |===================================================================
b . 14.20 |====================================================================
c . 14.21 |====================================================================
d . 14.04 |===================================================================
e . 14.30 |====================================================================
Blender 3.5
Blend File: Classroom - Compute: CPU-Only
Seconds < Lower Is Better
a . 36.50 |===================================================================
b . 36.66 |====================================================================
c . 36.79 |====================================================================
d . 36.31 |===================================================================
e . 36.36 |===================================================================
Blender 3.5
Blend File: Fishy Cat - Compute: CPU-Only
Seconds < Lower Is Better
a . 19.36 |=================================================================
b . 19.70 |===================================================================
c . 19.94 |===================================================================
d . 20.13 |====================================================================
e . 19.54 |==================================================================
Blender 3.5
Blend File: Barbershop - Compute: CPU-Only
Seconds < Lower Is Better
a . 147.25 |===================================================================
b . 147.73 |===================================================================
c . 146.59 |==================================================================
d . 147.18 |===================================================================
e . 148.11 |===================================================================
Blender 3.5
Blend File: Pabellon Barcelona - Compute: CPU-Only
Seconds < Lower Is Better
a . 48.81 |====================================================================
b . 47.84 |===================================================================
c . 47.65 |==================================================================
d . 47.73 |==================================================================
e . 47.43 |==================================================================
nginx 1.23.2
Connections: 100
Requests Per Second > Higher Is Better
nginx 1.23.2
Connections: 200
Requests Per Second > Higher Is Better
nginx 1.23.2
Connections: 500
Requests Per Second > Higher Is Better
a . 250533.37 |================================================================
b . 246156.11 |===============================================================
c . 246619.54 |===============================================================
d . 247581.64 |===============================================================
e . 248416.85 |===============================================================
nginx 1.23.2
Connections: 1000
Requests Per Second > Higher Is Better
Apache HTTP Server 2.4.56
Concurrent Requests: 100
Requests Per Second > Higher Is Better
Apache HTTP Server 2.4.56
Concurrent Requests: 200
Requests Per Second > Higher Is Better
Apache HTTP Server 2.4.56
Concurrent Requests: 500
Requests Per Second > Higher Is Better
a . 80395.59 |=============================================================
b . 83834.81 |================================================================
c . 77777.03 |===========================================================
d . 84694.76 |================================================================
e . 85357.84 |=================================================================
Apache HTTP Server 2.4.56
Concurrent Requests: 1000
Requests Per Second > Higher Is Better