Intel Xeon Gold 6226R testing with a Supermicro X11SPL-F v1.02 (3.1 BIOS) and llvmpipe on Ubuntu 20.04 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 2012200-HA-XEONGOLD615
Xeon Gold 6226R December
Intel Xeon Gold 6226R testing with a Supermicro X11SPL-F v1.02 (3.1 BIOS) and llvmpipe on Ubuntu 20.04 via the Phoronix Test Suite.
1:
Processor: Intel Xeon Gold 6226R @ 3.90GHz (16 Cores / 32 Threads), Motherboard: Supermicro X11SPL-F v1.02 (3.1 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 188GB, Disk: 3841GB Micron_9300_MTFDHAL3T8TDP, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Intel I210
OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6daily20200921-generic (x86_64) 20200920, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 3.3 Mesa 20.0.8 (LLVM 10.0.0 256 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080
2:
Processor: Intel Xeon Gold 6226R @ 3.90GHz (16 Cores / 32 Threads), Motherboard: Supermicro X11SPL-F v1.02 (3.1 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 188GB, Disk: 3841GB Micron_9300_MTFDHAL3T8TDP, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Intel I210
OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6daily20200921-generic (x86_64) 20200920, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 3.3 Mesa 20.0.8 (LLVM 10.0.0 256 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080
3:
Processor: Intel Xeon Gold 6226R @ 3.90GHz (16 Cores / 32 Threads), Motherboard: Supermicro X11SPL-F v1.02 (3.1 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 188GB, Disk: 3841GB Micron_9300_MTFDHAL3T8TDP, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Intel I210
OS: Ubuntu 20.04, Kernel: 5.9.0-050900rc6daily20200921-generic (x86_64) 20200920, Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 3.3 Mesa 20.0.8 (LLVM 10.0.0 256 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080
CLOMP 1.2
Static OMP Speedup
Speedup > Higher Is Better
1 . 26.8 |=====================================================================
2 . 26.2 |===================================================================
3 . 26.5 |====================================================================
BRL-CAD 7.30.8
VGR Performance Metric
VGR Performance Metric > Higher Is Better
1 . 164354 |===================================================================
2 . 163713 |===================================================================
Monkey Audio Encoding 3.99.6
WAV To APE
Seconds < Lower Is Better
1 . 17.53 |====================================================================
2 . 17.54 |====================================================================
3 . 17.54 |====================================================================
WavPack Audio Encoding 5.3
WAV To WavPack
Seconds < Lower Is Better
1 . 16.73 |====================================================================
2 . 16.75 |====================================================================
3 . 16.78 |====================================================================
Timed HMMer Search 3.3.1
Pfam Database Search
Seconds < Lower Is Better
1 . 174.23 |===================================================================
2 . 174.17 |===================================================================
3 . 174.45 |===================================================================
Timed MAFFT Alignment 7.471
Multiple Sequence Alignment - LSU RNA
Seconds < Lower Is Better
1 . 10.64 |====================================================================
2 . 10.59 |====================================================================
3 . 10.55 |===================================================================
NCNN 20201218
Target: CPU - Model: mobilenet
ms < Lower Is Better
1 . 17.77 |====================================================================
2 . 16.96 |=================================================================
3 . 17.73 |====================================================================
NCNN 20201218
Target: CPU-v2-v2 - Model: mobilenet-v2
ms < Lower Is Better
1 . 6.07 |=====================================================================
2 . 5.96 |====================================================================
3 . 5.89 |===================================================================
NCNN 20201218
Target: CPU-v3-v3 - Model: mobilenet-v3
ms < Lower Is Better
1 . 5.21 |=====================================================================
2 . 5.14 |====================================================================
3 . 5.07 |===================================================================
NCNN 20201218
Target: CPU - Model: shufflenet-v2
ms < Lower Is Better
1 . 5.94 |====================================================================
2 . 5.97 |=====================================================================
3 . 6.01 |=====================================================================
NCNN 20201218
Target: CPU - Model: mnasnet
ms < Lower Is Better
1 . 5.56 |=====================================================================
2 . 5.37 |===================================================================
3 . 5.34 |==================================================================
NCNN 20201218
Target: CPU - Model: efficientnet-b0
ms < Lower Is Better
1 . 7.70 |=====================================================================
2 . 7.26 |=================================================================
3 . 7.20 |=================================================================
NCNN 20201218
Target: CPU - Model: blazeface
ms < Lower Is Better
1 . 2.93 |=====================================================================
2 . 2.88 |====================================================================
3 . 2.90 |====================================================================
NCNN 20201218
Target: CPU - Model: googlenet
ms < Lower Is Better
1 . 14.56 |====================================================================
2 . 12.99 |=============================================================
3 . 13.09 |=============================================================
NCNN 20201218
Target: CPU - Model: vgg16
ms < Lower Is Better
1 . 29.65 |====================================================================
2 . 28.04 |================================================================
3 . 29.24 |===================================================================
NCNN 20201218
Target: CPU - Model: resnet18
ms < Lower Is Better
1 . 10.58 |====================================================================
2 . 9.41 |============================================================
3 . 9.49 |=============================================================
NCNN 20201218
Target: CPU - Model: alexnet
ms < Lower Is Better
1 . 8.07 |=====================================================================
2 . 6.73 |==========================================================
3 . 6.99 |============================================================
NCNN 20201218
Target: CPU - Model: resnet50
ms < Lower Is Better
1 . 20.27 |====================================================================
2 . 18.93 |===============================================================
3 . 20.38 |====================================================================
NCNN 20201218
Target: CPU - Model: yolov4-tiny
ms < Lower Is Better
1 . 24.72 |===================================================================
2 . 24.40 |==================================================================
3 . 25.22 |====================================================================
NCNN 20201218
Target: CPU - Model: squeezenet_ssd
ms < Lower Is Better
1 . 16.73 |===================================================================
2 . 16.45 |==================================================================
3 . 16.94 |====================================================================
NCNN 20201218
Target: CPU - Model: regnety_400m
ms < Lower Is Better
1 . 27.19 |===================================================================
2 . 27.13 |===================================================================
3 . 27.41 |====================================================================
oneDNN 2.0
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 2.35358 |==================================================================
2 . 2.35038 |==================================================================
3 . 2.34990 |==================================================================
oneDNN 2.0
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 3.14816 |==================================================================
2 . 3.13707 |==================================================================
3 . 3.15835 |==================================================================
oneDNN 2.0
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 0.496245 |=================================================================
2 . 0.491459 |================================================================
3 . 0.495733 |=================================================================
oneDNN 2.0
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 1.24491 |==================================================================
2 . 1.24781 |==================================================================
3 . 1.24380 |==================================================================
oneDNN 2.0
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
1 . 5.61346 |==================================================================
2 . 5.60704 |==================================================================
3 . 5.60477 |==================================================================
oneDNN 2.0
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
1 . 2.59610 |==================================================================
2 . 2.58806 |=================================================================
3 . 2.61350 |==================================================================
oneDNN 2.0
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 4.34336 |==================================================================
2 . 4.34082 |==================================================================
3 . 4.35080 |==================================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 2.75497 |==================================================================
2 . 2.76210 |==================================================================
3 . 2.75619 |==================================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 3.21126 |==================================================================
2 . 3.20825 |==================================================================
3 . 3.21278 |==================================================================
oneDNN 2.0
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 4.16656 |==================================================================
2 . 4.16237 |==================================================================
3 . 4.17445 |==================================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 0.526692 |=================================================================
2 . 0.527252 |=================================================================
3 . 0.526437 |=================================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 0.867040 |=================================================================
2 . 0.860663 |=================================================================
3 . 0.862856 |=================================================================
oneDNN 2.0
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 1643.22 |==================================================================
2 . 1645.08 |==================================================================
3 . 1647.12 |==================================================================
oneDNN 2.0
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 922.99 |===================================================================
2 . 923.54 |===================================================================
3 . 922.27 |===================================================================
oneDNN 2.0
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 1643.59 |==================================================================
2 . 1645.64 |==================================================================
3 . 1645.91 |==================================================================
oneDNN 2.0
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
1 . 9.42174 |==================================================================
2 . 9.42301 |==================================================================
3 . 9.42277 |==================================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
1 . 11.29 |====================================================================
2 . 11.29 |====================================================================
3 . 11.32 |====================================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
1 . 12.53 |====================================================================
2 . 12.54 |====================================================================
3 . 12.53 |====================================================================
oneDNN 2.0
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 922.87 |===================================================================
2 . 923.37 |===================================================================
3 . 923.34 |===================================================================
oneDNN 2.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 0.974624 |=================================================================
2 . 0.973874 |=================================================================
3 . 0.977073 |=================================================================
oneDNN 2.0
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
1 . 1645.61 |==================================================================
2 . 1643.67 |==================================================================
3 . 1644.83 |==================================================================
oneDNN 2.0
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
1 . 921.67 |===================================================================
2 . 923.44 |===================================================================
3 . 920.86 |===================================================================
oneDNN 2.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 0.486440 |=================================================================
2 . 0.477951 |================================================================
3 . 0.479216 |================================================================
oneDNN 2.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
1 . 2.05419 |==================================================================
2 . 2.05772 |==================================================================
3 . 2.05675 |==================================================================
Coremark 1.0
CoreMark Size 666 - Iterations Per Second
Iterations/Sec > Higher Is Better
1 . 537952.81 |================================================================
2 . 535255.63 |================================================================
3 . 534883.16 |================================================================
Timed FFmpeg Compilation 4.2.2
Time To Compile
Seconds < Lower Is Better
1 . 43.50 |====================================================================
2 . 43.24 |====================================================================
3 . 43.45 |====================================================================
Build2 0.13
Time To Compile
Seconds < Lower Is Better
1 . 95.58 |====================================================================
2 . 95.69 |====================================================================
3 . 95.53 |====================================================================
Timed Eigen Compilation 3.3.9
Time To Compile
Seconds < Lower Is Better
1 . 85.53 |====================================================================
2 . 85.55 |====================================================================
3 . 85.78 |====================================================================
SQLite Speedtest 3.30
Timed Time - Size 1,000
Seconds < Lower Is Better
1 . 65.36 |===================================================================
2 . 65.85 |====================================================================
3 . 65.59 |====================================================================
Node.js V8 Web Tooling Benchmark
runs/s > Higher Is Better
1 . 10.78 |====================================================================
2 . 10.69 |===================================================================
3 . 10.68 |===================================================================
simdjson 0.7.1
Throughput Test: Kostya
GB/s > Higher Is Better
1 . 0.56 |=====================================================================
2 . 0.56 |=====================================================================
3 . 0.56 |=====================================================================
simdjson 0.7.1
Throughput Test: LargeRandom
GB/s > Higher Is Better
1 . 0.39 |=====================================================================
2 . 0.39 |=====================================================================
3 . 0.39 |=====================================================================
simdjson 0.7.1
Throughput Test: PartialTweets
GB/s > Higher Is Better
1 . 0.57 |=====================================================================
2 . 0.57 |=====================================================================
3 . 0.57 |=====================================================================
simdjson 0.7.1
Throughput Test: DistinctUserID
GB/s > Higher Is Better
1 . 0.58 |=====================================================================
2 . 0.58 |=====================================================================
3 . 0.58 |=====================================================================