aug

Intel Core i9-10980XE testing with a ASRock X299 Steel Legend (P1.30 BIOS) and llvmpipe 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 2311069-PTS-AUG6407629
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:

C++ Boost Tests 3 Tests
CPU Massive 3 Tests
Creator Workloads 6 Tests
Fortran Tests 2 Tests
Game Development 2 Tests
HPC - High Performance Computing 4 Tests
Machine Learning 2 Tests
Multi-Core 8 Tests
Intel oneAPI 6 Tests
OpenMPI Tests 3 Tests
Programmer / Developer System Benchmarks 2 Tests
Python Tests 3 Tests
Scientific Computing 2 Tests
Server CPU Tests 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
November 05 2023
  2 Hours, 43 Minutes
b
November 05 2023
  2 Hours, 41 Minutes
Invert Hiding All Results Option
  2 Hours, 42 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):

aug Intel Core i9-10980XE testing with a ASRock X299 Steel Legend (P1.30 BIOS) and llvmpipe on Ubuntu 22.04 via the Phoronix Test Suite. ,,"a","b" Processor,,Intel Core i9-10980XE @ 4.80GHz (18 Cores / 36 Threads),Intel Core i9-10980XE @ 4.80GHz (18 Cores / 36 Threads) Motherboard,,ASRock X299 Steel Legend (P1.30 BIOS),ASRock X299 Steel Legend (P1.30 BIOS) Chipset,,Intel Sky Lake-E DMI3 Registers,Intel Sky Lake-E DMI3 Registers Memory,,32GB,32GB Disk,,Samsung SSD 970 PRO 512GB,Samsung SSD 970 PRO 512GB Graphics,,llvmpipe,llvmpipe Audio,,Realtek ALC1220,Realtek ALC1220 Network,,Intel I219-V + Intel I211,Intel I219-V + Intel I211 OS,,Ubuntu 22.04,Ubuntu 22.04 Kernel,,6.2.0-33-generic (x86_64),6.2.0-33-generic (x86_64) Desktop,,GNOME Shell 42.2,GNOME Shell 42.2 Display Server,,X Server 1.21.1.3,X Server 1.21.1.3 OpenGL,,4.5 Mesa 22.0.1 (LLVM 13.0.1 256 bits),4.5 Mesa 22.0.1 (LLVM 13.0.1 256 bits) Vulkan,,1.2.204,1.2.204 Compiler,,GCC 11.4.0,GCC 11.4.0 File-System,,ext4,ext4 Screen Resolution,,1024x768,1024x768 ,,"a","b" "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (ms)",LIB,86.37,119.54 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (FPS)",HIB,208.24,150.46 "C-Blosc - Test: blosclz shuffle - Buffer Size: 8MB (MB/s)",HIB,7438.3,6824.9 "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.618773,0.578244 "QMCPACK - Input: H4_ae (Execution Time - sec)",LIB,20.61,19.31 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,941.957,898.306 "HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256 (GFLOP/s)",HIB,16.3164,15.8081 "HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128 (GFLOP/s)",HIB,20.9461,21.5749 "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1.20555,1.24078 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,1722.09,1677.89 "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,5.38829,5.25114 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,897.021,919.84 "HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128 (GFLOP/s)",HIB,26.2733,25.6418 "Intel Open Image Denoise - Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only (Images / Sec)",HIB,0.42,0.41 "oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,2.91534,2.85512 "HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256 (GFLOP/s)",HIB,31.0169,31.6018 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,11.3386,11.1414 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.633996,0.643669 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (FPS)",HIB,171.99,174.6 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (ms)",LIB,104.57,103.02 "HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128 (GFLOP/s)",HIB,9.01432,9.1424 "QMCPACK - Input: O_ae_pyscf_UHF (Execution Time - sec)",LIB,238,241.13 "C-Blosc - Test: blosclz bitshuffle - Buffer Size: 16MB (MB/s)",HIB,8107.4,8211.3 "QMCPACK - Input: Li2_STO_ae (Execution Time - sec)",LIB,219.65,222.42 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,140.54,142.3 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,85.3,84.25 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (ms)",LIB,51.6,52.23 "C-Blosc - Test: blosclz noshuffle - Buffer Size: 16MB (MB/s)",HIB,9855.4,9737.2 "Cpuminer-Opt - Algorithm: Skeincoin (kH/s)",HIB,23470,23190 "HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256 (GFLOP/s)",HIB,8.82564,8.9321 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (FPS)",HIB,232.23,229.51 "HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: float - X Y Z: 512 (GFLOP/s)",HIB,35.6982,36.1147 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,1650.24,1632.03 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,26.48,26.21 "C-Blosc - Test: blosclz bitshuffle - Buffer Size: 128MB (MB/s)",HIB,5578.8,5636.2 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,453.11,457.7 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,452.46,457 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,26.51,26.25 "HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: double - X Y Z: 512 (GFLOP/s)",HIB,18.8937,18.7258 "C-Blosc - Test: blosclz shuffle - Buffer Size: 32MB (MB/s)",HIB,7361.7,7424 "oneDNN - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,5.49542,5.54192 "HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: float - X Y Z: 512 (GFLOP/s)",HIB,19.8916,20.059 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (ms)",LIB,15.78,15.65 "C-Blosc - Test: blosclz bitshuffle - Buffer Size: 32MB (MB/s)",HIB,7590,7651.2 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (FPS)",HIB,759.1,765.16 "HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256 (GFLOP/s)",HIB,16.7333,16.863 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,78.93,78.36 "C-Blosc - Test: blosclz shuffle - Buffer Size: 256MB (MB/s)",HIB,4399.7,4429.6 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,35356.18,35584.44 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,151.95,152.93 "Embree - Binary: Pathtracer - Model: Asian Dragon (FPS)",HIB,22.9979,23.1394 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1615.72,1625.41 "C-Blosc - Test: blosclz bitshuffle - Buffer Size: 64MB (MB/s)",HIB,6752.2,6790.6 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,6.9011,6.86291 "QuantLib - Configuration: Multi-Threaded (MFLOPS)",HIB,44641,44888.6 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,1758.87,1768.54 "Embree - Binary: Pathtracer - Model: Crown (FPS)",HIB,20.2696,20.16 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,7813,7854 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,113740,114336 "CloverLeaf - Input: clover_bm64_short (sec)",LIB,212.72,211.67 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,6.77,6.74 "C-Blosc - Test: blosclz shuffle - Buffer Size: 16MB (MB/s)",HIB,7457.4,7424.4 "Cpuminer-Opt - Algorithm: Garlicoin (kH/s)",HIB,1603.79,1596.74 "C-Blosc - Test: blosclz noshuffle - Buffer Size: 128MB (MB/s)",HIB,5467.9,5490.8 "C-Blosc - Test: blosclz bitshuffle - Buffer Size: 256MB (MB/s)",HIB,4497.3,4516 "Cpuminer-Opt - Algorithm: scrypt (kH/s)",HIB,182.09,182.84 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,132846,132304 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,23.43,23.52 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,11.0649,11.0229 "Embree - Binary: Pathtracer ISPC - Model: Crown (FPS)",HIB,17.7179,17.6515 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,766.69,763.83 "OSPRay Studio - Camera: 1 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,1636,1630 "OSPRay Studio - Camera: 2 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,34183,34063 "OSPRay Studio - Camera: 1 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,33567,33453 "C-Blosc - Test: blosclz noshuffle - Buffer Size: 8MB (MB/s)",HIB,10371.6,10336.6 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,2725.1,2734.29 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,756.48,758.98 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,2.70456,2.69575 "HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: double - X Y Z: 512 (GFLOP/s)",HIB,10.3095,10.2769 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,15.82,15.77 "Cpuminer-Opt - Algorithm: Myriad-Groestl (kH/s)",HIB,7629.51,7651.61 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,6625,6606 "HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128 (GFLOP/s)",HIB,55.0703,54.9198 "C-Blosc - Test: blosclz noshuffle - Buffer Size: 64MB (MB/s)",HIB,6761.1,6779.6 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,322.01,322.81 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,37.23,37.14 "C-Blosc - Test: blosclz noshuffle - Buffer Size: 32MB (MB/s)",HIB,7828.9,7847.7 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,906.092,908.247 "QMCPACK - Input: LiH_ae_MSD (Execution Time - sec)",LIB,106.57,106.32 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,9.0344,9.05479 "Cpuminer-Opt - Algorithm: Triple SHA-256, Onecoin (kH/s)",HIB,53370,53490 "C-Blosc - Test: blosclz noshuffle - Buffer Size: 256MB (MB/s)",HIB,4239.4,4248.9 "OSPRay Studio - Camera: 3 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,1968,1972 "C-Blosc - Test: blosclz shuffle - Buffer Size: 128MB (MB/s)",HIB,5468.8,5457.9 "OSPRay Studio - Camera: 2 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,60718,60600 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,257612,257164 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,18225.98,18196.6 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (FPS)",HIB,58.37,58.28 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,6.58,6.57 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (ms)",LIB,205.41,205.72 "OSPRay Studio - Camera: 3 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,70355,70459 "QMCPACK - Input: FeCO6_b3lyp_gms (Execution Time - sec)",LIB,183.42,183.17 "Embree - Binary: Pathtracer - Model: Asian Dragon Obj (FPS)",HIB,20.7169,20.6897 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.525652,0.525029 "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,2.49348,2.49628 "C-Blosc - Test: blosclz bitshuffle - Buffer Size: 8MB (MB/s)",HIB,8267.5,8258.4 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,9.87746,9.88829 "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,214480,214262 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon Obj (FPS)",HIB,19.6108,19.6293 "easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 (sec)",LIB,207.533,207.714 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,7.85872,7.86557 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (FPS)",HIB,3190.1,3192.8 "QuantLib - Configuration: Single-Threaded (MFLOPS)",HIB,2682,2684.2 "OSPRay Studio - Camera: 3 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,38826,38857 "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,6468,6463 "QMCPACK - Input: simple-H2O (Execution Time - sec)",LIB,42.511,42.48 "Timed Gem5 Compilation - Time To Compile (sec)",LIB,413.862,413.585 "Cpuminer-Opt - Algorithm: Deepcoin (kH/s)",HIB,5431.94,5428.44 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,444.06,444.29 "easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 (sec)",LIB,8.422,8.418 "C-Blosc - Test: blosclz shuffle - Buffer Size: 64MB (MB/s)",HIB,6560.7,6563.5 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,26.96,26.97 "Cpuminer-Opt - Algorithm: Ringcoin (kH/s)",HIB,2421.85,2421.09 "Cpuminer-Opt - Algorithm: Magi (kH/s)",HIB,422.16,422.06 "Cpuminer-Opt - Algorithm: LBC, LBRY Credits (kH/s)",HIB,9720.14,9722.17 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon (FPS)",HIB,22.8478,22.8438 "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,110942,110960 "OSPRay Studio - Camera: 1 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,59518,59525 "CloverLeaf - Input: clover_bm (sec)",LIB,88.76,88.75 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,218906,218882 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,0.48,0.48 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,0.97,0.97 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (ms)",LIB,5.59,5.59 "Cpuminer-Opt - Algorithm: Quad SHA-256, Pyrite (kH/s)",HIB,37850,37850 "Cpuminer-Opt - Algorithm: Blake-2 S (kH/s)",HIB,103360,103360 "OSPRay Studio - Camera: 2 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,1667,1667 "OpenVKL - Benchmark: vklBenchmarkCPU Scalar (Items / Sec)",HIB,157,157 "OpenVKL - Benchmark: vklBenchmarkCPU ISPC (Items / Sec)",HIB,411,411 "Intel Open Image Denoise - Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only (Images / Sec)",HIB,0.88,0.88 "Intel Open Image Denoise - Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only (Images / Sec)",HIB,0.88,0.88 "DuckDB - Benchmark: TPC-H Parquet (sec)",LIB,, "DuckDB - Benchmark: IMDB (sec)",LIB,,