Amazon EC2 m6i.8xlarge
KVM testing on Ubuntu 20.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2108184-TJ-2108173TJ20&rdt&grs.
oneDNN
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
Apache HTTP Server
Concurrent Requests: 200
oneDNN
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
Apache HTTP Server
Concurrent Requests: 1000
OSPray
Demo: Magnetic Reconnection - Renderer: Path Tracer
GROMACS
Implementation: MPI CPU - Input: water_GMX50_bare
SVT-AV1
Encoder Mode: Preset 8 - Input: Bosphorus 4K
TNN
Target: CPU - Model: SqueezeNet v2
QuantLib
NAMD
ATPase Simulation - 327,506 Atoms
Pennant
Test: sedovbig
SVT-HEVC
Tuning: 1 - Input: Bosphorus 1080p
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
Pennant
Test: leblancbig
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
SVT-HEVC
Tuning: 10 - Input: Bosphorus 1080p
OSPray
Demo: NASA Streamlines - Renderer: Path Tracer
OSPray
Demo: XFrog Forest - Renderer: Path Tracer
Apache HTTP Server
Concurrent Requests: 500
SVT-HEVC
Tuning: 7 - Input: Bosphorus 1080p
Timed FFmpeg Compilation
Time To Compile
LULESH
Intel Open Image Denoise
Run: RT.ldr_alb_nrm.3840x2160
Intel Open Image Denoise
Run: RT.hdr_alb_nrm.3840x2160
Intel Open Image Denoise
Run: RTLightmap.hdr.4096x4096
OpenVKL
Benchmark: vklBenchmark ISPC
Timed LLVM Compilation
Build System: Unix Makefiles
Timed Linux Kernel Compilation
Time To Compile
oneDNN
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
Xcompact3d Incompact3d
Input: input.i3d 193 Cells Per Direction
TNN
Target: CPU - Model: MobileNet v2
OSPray
Demo: San Miguel - Renderer: Path Tracer
nginx
Concurrent Requests: 200
Timed Node.js Compilation
Time To Compile
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
Xcompact3d Incompact3d
Input: input.i3d 129 Cells Per Direction
OpenVKL
Benchmark: vklBenchmark Scalar
nginx
Concurrent Requests: 1000
oneDNN
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
nginx
Concurrent Requests: 500
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
Appleseed
Scene: Material Tester
Appleseed
Scene: Emily
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
Appleseed
Scene: Disney Material
TNN
Target: CPU - Model: SqueezeNet v1.1
High Performance Conjugate Gradient
TNN
Target: CPU - Model: DenseNet
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
YafaRay
Total Time For Sample Scene
Phoronix Test Suite v10.8.4