Xeon Platinum 8380 AVX-512 Workloads Benchmarks for a future article. 2 x Intel Xeon Platinum 8380 testing with a Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS) and ASPEED on Ubuntu 22.10 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2308099-NE-XEONPLATI49&grw&sor .
Xeon Platinum 8380 AVX-512 Workloads Processor Motherboard Chipset Memory Disk Graphics Monitor Network OS Kernel Desktop Display Server Vulkan Compiler File-System Screen Resolution 0xd000390 0xd0003a5 2 x Intel Xeon Platinum 8380 @ 3.40GHz (80 Cores / 160 Threads) Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS) Intel Ice Lake IEH 512GB 7682GB INTEL SSDPF2KX076TZ ASPEED VE228 2 x Intel X710 for 10GBASE-T + 2 x Intel E810-C for QSFP Ubuntu 22.10 6.5.0-060500rc4daily20230804-generic (x86_64) GNOME Shell 43.0 X Server 1.21.1.3 1.3.224 GCC 12.2.0 ext4 1920x1080 6.5.0-rc5-phx-tues (x86_64) OpenBenchmarking.org Kernel Details - Transparent Huge Pages: madvise Compiler Details - --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-12-U8K4Qv/gcc-12-12.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v Processor Details - 0xd000390: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0xd000390 - 0xd0003a5: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0xd0003a5 Python Details - Python 3.10.7 Security Details - 0xd000390: itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected - 0xd0003a5: gather_data_sampling: Mitigation of Microcode + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Not affected + spec_rstack_overflow: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Not affected + tsx_async_abort: Not affected
Xeon Platinum 8380 AVX-512 Workloads specfem3d: Homogeneous Halfspace minibude: OpenMP - BM1 heffte: c2c - FFTW - double - 128 heffte: c2c - FFTW - double - 256 heffte: r2c - FFTW - float - 128 heffte: r2c - FFTW - float - 256 palabos: 500 heffte: c2c - FFTW - float - 256 laghos: Triple Point Problem palabos: 100 libxsmm: 32 libxsmm: 64 libxsmm: 256 libxsmm: 128 minibude: OpenMP - BM1 heffte: r2c - FFTW - double - 128 laghos: Sedov Blast Wave, ube_922_hex.mesh heffte: r2c - FFTW - double - 256 heffte: c2c - FFTW - float - 128 palabos: 400 specfem3d: Water-layered Halfspace minibude: OpenMP - BM2 specfem3d: Tomographic Model specfem3d: Layered Halfspace specfem3d: Mount St. Helens minibude: OpenMP - BM2 mrbayes: Primate Phylogeny Analysis tensorflow: CPU - 256 - AlexNet tensorflow: CPU - 512 - AlexNet remhos: Sample Remap Example tensorflow: CPU - 256 - GoogLeNet tensorflow: CPU - 256 - ResNet-50 tensorflow: CPU - 512 - GoogLeNet tensorflow: CPU - 512 - ResNet-50 cloverleaf: Lagrangian-Eulerian Hydrodynamics deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream deepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Stream deepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Stream deepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Stream deepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Stream deepsparse: ResNet-50, Baseline - Asynchronous Multi-Stream deepsparse: ResNet-50, Baseline - Asynchronous Multi-Stream deepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream deepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream deepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream deepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream deepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream deepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream deepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Stream deepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream deepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream onnx: GPT-2 - CPU - Standard onnx: yolov4 - CPU - Standard onnx: bertsquad-12 - CPU - Standard onnx: CaffeNet 12-int8 - CPU - Standard onnx: fcn-resnet101-11 - CPU - Standard onnx: ArcFace ResNet-100 - CPU - Standard onnx: ResNet50 v1-12-int8 - CPU - Standard onnx: super-resolution-10 - CPU - Standard ncnn: CPU - mobilenet ncnn: CPU-v2-v2 - mobilenet-v2 ncnn: CPU-v3-v3 - mobilenet-v3 ncnn: CPU - shufflenet-v2 ncnn: CPU - mnasnet ncnn: CPU - efficientnet-b0 ncnn: CPU - blazeface ncnn: CPU - googlenet ncnn: CPU - vgg16 ncnn: CPU - resnet18 ncnn: CPU - alexnet ncnn: CPU - resnet50 ncnn: CPU - yolov4-tiny ncnn: CPU - squeezenet_ssd ncnn: CPU - regnety_400m ncnn: CPU - vision_transformer ncnn: CPU - FastestDet gromacs: MPI CPU - water_GMX50_bare onednn: IP Shapes 3D - bf16bf16bf16 - CPU onednn: Convolution Batch Shapes Auto - bf16bf16bf16 - CPU onednn: Deconvolution Batch shapes_1d - bf16bf16bf16 - CPU onednn: Deconvolution Batch shapes_3d - bf16bf16bf16 - CPU onednn: Recurrent Neural Network Training - bf16bf16bf16 - CPU onednn: Recurrent Neural Network Inference - bf16bf16bf16 - CPU openvino: Face Detection FP16 - CPU openvino: Face Detection FP16 - CPU openvino: Person Detection FP16 - CPU openvino: Person Detection FP16 - CPU openvino: Person Detection FP32 - CPU openvino: Person Detection FP32 - CPU openvino: Vehicle Detection FP16 - CPU openvino: Vehicle Detection FP16 - CPU openvino: Face Detection FP16-INT8 - CPU openvino: Face Detection FP16-INT8 - CPU openvino: Vehicle Detection FP16-INT8 - CPU openvino: Vehicle Detection FP16-INT8 - CPU openvino: Weld Porosity Detection FP16 - CPU openvino: Weld Porosity Detection FP16 - CPU openvino: Machine Translation EN To DE FP16 - CPU openvino: Machine Translation EN To DE FP16 - CPU openvino: Weld Porosity Detection FP16-INT8 - CPU openvino: Weld Porosity Detection FP16-INT8 - CPU openvino: Person Vehicle Bike Detection FP16 - CPU openvino: Person Vehicle Bike Detection FP16 - CPU openvino: Age Gender Recognition Retail 0013 FP16 - CPU openvino: Age Gender Recognition Retail 0013 FP16 - CPU openvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPU openvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPU qmcpack: Li2_STO_ae qmcpack: simple-H2O qmcpack: FeCO6_b3lyp_gms qmcpack: FeCO6_b3lyp_gms incompact3d: input.i3d 193 Cells Per Direction cpuminer-opt: Magi cpuminer-opt: x25x cpuminer-opt: scrypt cpuminer-opt: Deepcoin cpuminer-opt: Blake-2 S cpuminer-opt: Garlicoin cpuminer-opt: Skeincoin cpuminer-opt: Myriad-Groestl cpuminer-opt: LBC, LBRY Credits cpuminer-opt: Quad SHA-256, Pyrite cpuminer-opt: Triple SHA-256, Onecoin vpxenc: Speed 5 - Bosphorus 4K dav1d: Chimera 1080p dav1d: Summer Nature 4K svt-av1: Preset 8 - Bosphorus 4K svt-av1: Preset 12 - Bosphorus 4K svt-av1: Preset 13 - Bosphorus 4K svt-hevc: 1 - Bosphorus 4K svt-hevc: 7 - Bosphorus 4K svt-hevc: 10 - Bosphorus 4K blender: BMW27 - CPU-Only blender: Fishy Cat - CPU-Only vvenc: Bosphorus 4K - Fast vvenc: Bosphorus 4K - Faster embree: Pathtracer ISPC - Crown embree: Pathtracer ISPC - Asian Dragon oidn: RT.ldr_alb_nrm.3840x2160 - CPU-Only oidn: RTLightmap.hdr.4096x4096 - CPU-Only openvkl: vklBenchmark ISPC ospray: particle_volume/ao/real_time ospray: particle_volume/scivis/real_time ospray: particle_volume/pathtracer/real_time ospray: gravity_spheres_volume/dim_512/ao/real_time ospray: gravity_spheres_volume/dim_512/scivis/real_time simdjson: Kostya simdjson: TopTweet simdjson: LargeRand simdjson: PartialTweets simdjson: DistinctUserID onnx: GPT-2 - CPU - Standard onnx: yolov4 - CPU - Standard onnx: bertsquad-12 - CPU - Standard onnx: CaffeNet 12-int8 - CPU - Standard onnx: fcn-resnet101-11 - CPU - Standard onnx: ArcFace ResNet-100 - CPU - Standard onnx: ResNet50 v1-12-int8 - CPU - Standard onnx: super-resolution-10 - CPU - Standard 0xd000390 0xd0003a5 18.022236470 2353.389 93.0906 46.3516 195.199 224.417 413.207 101.977 256.27 312.195 604.7 1098.8 594.6 1941.1 94.136 144.942 385.89 93.7474 154.731 388.476 31.146951238 2526.887 14.574192022 29.497987898 13.148692362 101.076 166.528 723.27 760.15 12.245 309.63 83.89 317.27 85.97 12.04 72.0674 551.1195 2301.1554 17.3504 922.9302 43.3033 230.0525 173.7945 1006.3407 39.6954 7633.2598 5.2210 422.1167 94.6126 98.3394 405.9662 1005.5843 39.7308 426.9495 93.5399 644.4430 62.0099 84.0181 474.6917 1051.7868 37.9943 307.8729 129.8095 72.1788 551.8180 180.163 11.6605 16.7110 696.725 9.08067 39.1157 221.020 158.355 15.46 8.03 8.88 9.89 7.57 11.62 4.35 15.36 23.86 8.97 5.39 17.15 23.71 15.34 45.54 46.92 10.20 9.234 2.59271 2.06936 3.90360 3.62526 832.452 524.381 24.04 827.51 13.29 1490.76 13.03 1517.69 1121.56 17.80 95.42 209.31 4419.17 4.51 2344.97 33.89 251.00 79.47 9396.52 8.50 2039.63 9.77 59274.06 1.33 67604.00 1.16 124.23 39.555 147.51 268.56 11.0240278 2309.47 2659.17 2319.31 64677 4462327 29203 613333 43127 421660 921730 1332237 12.63 515.81 281.36 67.170 180.967 175.102 10.46 138.75 184.38 23.83 30.74 5.722 10.364 88.1941 104.6844 3.03 1.46 912 24.7473 24.9506 150.281 21.0761 20.5780 2.61 5.60 0.85 4.62 5.52 5.54783 85.7987 59.8415 1.43407 110.323 25.5687 4.52403 6.31428 17.751628972 2372.418 93.9207 46.9833 198.869 226.783 417.483 102.920 256.87 312.530 609.2 1177.1 600.2 1978.9 94.897 153.790 386.08 94.8614 159.100 393.844 31.381798528 2601.212 14.145999546 29.373606551 12.948759795 104.048 165.419 781.25 839.41 12.401 321.72 86.93 323.79 84.84 11.98 71.8868 553.2682 2068.3047 19.3090 930.7506 42.9405 231.4277 172.5579 1013.8215 39.4131 7092.5030 5.6210 412.0531 96.9429 94.2573 421.7544 987.5452 40.4508 398.7153 100.1856 620.2548 64.4439 83.2041 478.8510 869.0393 45.9597 293.1241 136.1780 71.5436 555.0876 190.570 11.1539 16.3098 664.537 8.59908 38.7185 216.481 158.014 15.66 7.96 8.76 9.76 7.41 11.71 4.54 16.58 25.71 9.42 5.46 18.51 24.10 16.10 38.85 45.23 9.71 9.094 2.57437 2.06967 3.91322 3.62266 816.508 521.742 24.18 823.09 13.25 1496.19 13.04 1519.84 1117.09 17.87 95.54 209.02 4442.98 4.49 2362.84 33.63 255.09 78.18 9419.77 8.48 2070.72 9.63 59377.96 1.33 67754.34 1.16 123.26 41.246 178.19 263.23 11.0041968 2308.66 2659.55 2321.74 64897 4466653 22086.25 617130 43450 423130 926277 1333117 12.33 514.58 280.84 66.460 177.721 177.203 10.45 138.49 182.74 23.72 30.90 5.705 10.415 83.4621 101.0959 3.03 1.46 856 16.4611 16.3849 136.853 18.8862 18.4626 2.87 5.75 0.96 4.77 5.71 5.28095 89.8299 61.5091 1.51029 117.545 25.8601 4.62085 6.71968 OpenBenchmarking.org
SPECFEM3D Model: Homogeneous Halfspace OpenBenchmarking.org Seconds, Fewer Is Better SPECFEM3D 4.0 Model: Homogeneous Halfspace 0xd0003a5 0xd000390 4 8 12 16 20 SE +/- 0.16, N = 3 SE +/- 0.15, N = 3 17.75 18.02 1. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
miniBUDE Implementation: OpenMP - Input Deck: BM1 OpenBenchmarking.org GFInst/s, More Is Better miniBUDE 20210901 Implementation: OpenMP - Input Deck: BM1 0xd0003a5 0xd000390 500 1000 1500 2000 2500 SE +/- 10.90, N = 3 SE +/- 3.60, N = 3 2372.42 2353.39 1. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm
HeFFTe - Highly Efficient FFT for Exascale Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128 OpenBenchmarking.org GFLOP/s, More Is Better HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128 0xd0003a5 0xd000390 20 40 60 80 100 SE +/- 0.28, N = 3 SE +/- 0.15, N = 3 93.92 93.09 1. (CXX) g++ options: -O3
HeFFTe - Highly Efficient FFT for Exascale Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256 OpenBenchmarking.org GFLOP/s, More Is Better HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256 0xd0003a5 0xd000390 11 22 33 44 55 SE +/- 0.61, N = 3 SE +/- 0.25, N = 3 46.98 46.35 1. (CXX) g++ options: -O3
HeFFTe - Highly Efficient FFT for Exascale Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128 OpenBenchmarking.org GFLOP/s, More Is Better HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128 0xd0003a5 0xd000390 40 80 120 160 200 SE +/- 1.08, N = 3 SE +/- 0.92, N = 3 198.87 195.20 1. (CXX) g++ options: -O3
HeFFTe - Highly Efficient FFT for Exascale Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256 OpenBenchmarking.org GFLOP/s, More Is Better HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256 0xd0003a5 0xd000390 50 100 150 200 250 SE +/- 2.85, N = 3 SE +/- 2.25, N = 3 226.78 224.42 1. (CXX) g++ options: -O3
Palabos Grid Size: 500 OpenBenchmarking.org Mega Site Updates Per Second, More Is Better Palabos 2.3 Grid Size: 500 0xd0003a5 0xd000390 90 180 270 360 450 SE +/- 0.72, N = 3 SE +/- 0.42, N = 3 417.48 413.21 1. (CXX) g++ options: -std=c++17 -pedantic -O3 -rdynamic -lcrypto -lcurl -lsz -lz -ldl -lm
HeFFTe - Highly Efficient FFT for Exascale Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256 OpenBenchmarking.org GFLOP/s, More Is Better HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256 0xd0003a5 0xd000390 20 40 60 80 100 SE +/- 0.20, N = 3 SE +/- 0.27, N = 3 102.92 101.98 1. (CXX) g++ options: -O3
Laghos Test: Triple Point Problem OpenBenchmarking.org Major Kernels Total Rate, More Is Better Laghos 3.1 Test: Triple Point Problem 0xd0003a5 0xd000390 60 120 180 240 300 SE +/- 0.94, N = 3 SE +/- 0.32, N = 3 256.87 256.27 1. (CXX) g++ options: -O3 -std=c++11 -lmfem -lHYPRE -lmetis -lrt -lmpi_cxx -lmpi
Palabos Grid Size: 100 OpenBenchmarking.org Mega Site Updates Per Second, More Is Better Palabos 2.3 Grid Size: 100 0xd0003a5 0xd000390 70 140 210 280 350 SE +/- 0.20, N = 3 SE +/- 0.89, N = 3 312.53 312.20 1. (CXX) g++ options: -std=c++17 -pedantic -O3 -rdynamic -lcrypto -lcurl -lsz -lz -ldl -lm
libxsmm M N K: 32 OpenBenchmarking.org GFLOPS/s, More Is Better libxsmm 2-1.17-3645 M N K: 32 0xd0003a5 0xd000390 130 260 390 520 650 SE +/- 4.92, N = 3 SE +/- 8.71, N = 15 609.2 604.7 1. (CXX) g++ options: -dynamic -Bstatic -static-libgcc -lgomp -lm -lrt -ldl -lquadmath -lstdc++ -pthread -fPIC -std=c++14 -O2 -fopenmp-simd -funroll-loops -ftree-vectorize -fdata-sections -ffunction-sections -fvisibility=hidden -msse4.2
libxsmm M N K: 64 OpenBenchmarking.org GFLOPS/s, More Is Better libxsmm 2-1.17-3645 M N K: 64 0xd0003a5 0xd000390 300 600 900 1200 1500 SE +/- 13.09, N = 15 SE +/- 8.60, N = 3 1177.1 1098.8 1. (CXX) g++ options: -dynamic -Bstatic -static-libgcc -lgomp -lm -lrt -ldl -lquadmath -lstdc++ -pthread -fPIC -std=c++14 -O2 -fopenmp-simd -funroll-loops -ftree-vectorize -fdata-sections -ffunction-sections -fvisibility=hidden -msse4.2
libxsmm M N K: 256 OpenBenchmarking.org GFLOPS/s, More Is Better libxsmm 2-1.17-3645 M N K: 256 0xd0003a5 0xd000390 130 260 390 520 650 SE +/- 2.54, N = 3 SE +/- 2.33, N = 3 600.2 594.6 1. (CXX) g++ options: -dynamic -Bstatic -static-libgcc -lgomp -lm -lrt -ldl -lquadmath -lstdc++ -pthread -fPIC -std=c++14 -O2 -fopenmp-simd -funroll-loops -ftree-vectorize -fdata-sections -ffunction-sections -fvisibility=hidden -msse4.2
libxsmm M N K: 128 OpenBenchmarking.org GFLOPS/s, More Is Better libxsmm 2-1.17-3645 M N K: 128 0xd0003a5 0xd000390 400 800 1200 1600 2000 SE +/- 20.92, N = 3 SE +/- 32.53, N = 7 1978.9 1941.1 1. (CXX) g++ options: -dynamic -Bstatic -static-libgcc -lgomp -lm -lrt -ldl -lquadmath -lstdc++ -pthread -fPIC -std=c++14 -O2 -fopenmp-simd -funroll-loops -ftree-vectorize -fdata-sections -ffunction-sections -fvisibility=hidden -msse4.2
miniBUDE Implementation: OpenMP - Input Deck: BM1 OpenBenchmarking.org Billion Interactions/s, More Is Better miniBUDE 20210901 Implementation: OpenMP - Input Deck: BM1 0xd0003a5 0xd000390 20 40 60 80 100 SE +/- 0.44, N = 3 SE +/- 0.14, N = 3 94.90 94.14 1. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm
HeFFTe - Highly Efficient FFT for Exascale Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128 OpenBenchmarking.org GFLOP/s, More Is Better HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128 0xd0003a5 0xd000390 30 60 90 120 150 SE +/- 1.71, N = 3 SE +/- 1.81, N = 4 153.79 144.94 1. (CXX) g++ options: -O3
Laghos Test: Sedov Blast Wave, ube_922_hex.mesh OpenBenchmarking.org Major Kernels Total Rate, More Is Better Laghos 3.1 Test: Sedov Blast Wave, ube_922_hex.mesh 0xd0003a5 0xd000390 80 160 240 320 400 SE +/- 0.80, N = 3 SE +/- 0.23, N = 3 386.08 385.89 1. (CXX) g++ options: -O3 -std=c++11 -lmfem -lHYPRE -lmetis -lrt -lmpi_cxx -lmpi
HeFFTe - Highly Efficient FFT for Exascale Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256 OpenBenchmarking.org GFLOP/s, More Is Better HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256 0xd0003a5 0xd000390 20 40 60 80 100 SE +/- 1.13, N = 3 SE +/- 1.09, N = 3 94.86 93.75 1. (CXX) g++ options: -O3
HeFFTe - Highly Efficient FFT for Exascale Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128 OpenBenchmarking.org GFLOP/s, More Is Better HeFFTe - Highly Efficient FFT for Exascale 2.3 Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128 0xd0003a5 0xd000390 40 80 120 160 200 SE +/- 1.01, N = 3 SE +/- 1.53, N = 5 159.10 154.73 1. (CXX) g++ options: -O3
Palabos Grid Size: 400 OpenBenchmarking.org Mega Site Updates Per Second, More Is Better Palabos 2.3 Grid Size: 400 0xd0003a5 0xd000390 90 180 270 360 450 SE +/- 0.06, N = 3 SE +/- 0.94, N = 3 393.84 388.48 1. (CXX) g++ options: -std=c++17 -pedantic -O3 -rdynamic -lcrypto -lcurl -lsz -lz -ldl -lm
SPECFEM3D Model: Water-layered Halfspace OpenBenchmarking.org Seconds, Fewer Is Better SPECFEM3D 4.0 Model: Water-layered Halfspace 0xd000390 0xd0003a5 7 14 21 28 35 SE +/- 0.24, N = 3 SE +/- 0.31, N = 5 31.15 31.38 1. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
miniBUDE Implementation: OpenMP - Input Deck: BM2 OpenBenchmarking.org GFInst/s, More Is Better miniBUDE 20210901 Implementation: OpenMP - Input Deck: BM2 0xd0003a5 0xd000390 600 1200 1800 2400 3000 SE +/- 9.53, N = 3 SE +/- 9.78, N = 3 2601.21 2526.89 1. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm
SPECFEM3D Model: Tomographic Model OpenBenchmarking.org Seconds, Fewer Is Better SPECFEM3D 4.0 Model: Tomographic Model 0xd0003a5 0xd000390 4 8 12 16 20 SE +/- 0.15, N = 3 SE +/- 0.02, N = 3 14.15 14.57 1. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
SPECFEM3D Model: Layered Halfspace OpenBenchmarking.org Seconds, Fewer Is Better SPECFEM3D 4.0 Model: Layered Halfspace 0xd0003a5 0xd000390 7 14 21 28 35 SE +/- 0.18, N = 3 SE +/- 0.05, N = 3 29.37 29.50 1. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
SPECFEM3D Model: Mount St. Helens OpenBenchmarking.org Seconds, Fewer Is Better SPECFEM3D 4.0 Model: Mount St. Helens 0xd0003a5 0xd000390 3 6 9 12 15 SE +/- 0.12, N = 3 SE +/- 0.18, N = 3 12.95 13.15 1. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
miniBUDE Implementation: OpenMP - Input Deck: BM2 OpenBenchmarking.org Billion Interactions/s, More Is Better miniBUDE 20210901 Implementation: OpenMP - Input Deck: BM2 0xd0003a5 0xd000390 20 40 60 80 100 SE +/- 0.38, N = 3 SE +/- 0.39, N = 3 104.05 101.08 1. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm
Timed MrBayes Analysis Primate Phylogeny Analysis OpenBenchmarking.org Seconds, Fewer Is Better Timed MrBayes Analysis 3.2.7 Primate Phylogeny Analysis 0xd0003a5 0xd000390 40 80 120 160 200 SE +/- 0.98, N = 3 SE +/- 1.36, N = 3 165.42 166.53 1. (CC) gcc options: -mmmx -msse -msse2 -msse3 -mssse3 -msse4.1 -msse4.2 -msha -maes -mavx -mfma -mavx2 -mavx512f -mavx512cd -mavx512vl -mavx512bw -mavx512dq -mavx512ifma -mavx512vbmi -mrdrnd -mbmi -mbmi2 -madx -mabm -O3 -std=c99 -pedantic -lm -lreadline
TensorFlow Device: CPU - Batch Size: 256 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: AlexNet 0xd0003a5 0xd000390 200 400 600 800 1000 SE +/- 4.27, N = 3 SE +/- 2.99, N = 3 781.25 723.27
TensorFlow Device: CPU - Batch Size: 512 - Model: AlexNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: AlexNet 0xd0003a5 0xd000390 200 400 600 800 1000 SE +/- 2.61, N = 3 SE +/- 3.77, N = 3 839.41 760.15
Remhos Test: Sample Remap Example OpenBenchmarking.org Seconds, Fewer Is Better Remhos 1.0 Test: Sample Remap Example 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.03, N = 3 SE +/- 0.04, N = 3 12.25 12.40 1. (CXX) g++ options: -O3 -std=c++11 -lmfem -lHYPRE -lmetis -lrt -lmpi_cxx -lmpi
TensorFlow Device: CPU - Batch Size: 256 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: GoogLeNet 0xd0003a5 0xd000390 70 140 210 280 350 SE +/- 2.10, N = 3 SE +/- 1.89, N = 3 321.72 309.63
TensorFlow Device: CPU - Batch Size: 256 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: ResNet-50 0xd0003a5 0xd000390 20 40 60 80 100 SE +/- 0.80, N = 9 SE +/- 0.37, N = 3 86.93 83.89
TensorFlow Device: CPU - Batch Size: 512 - Model: GoogLeNet OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: GoogLeNet 0xd0003a5 0xd000390 70 140 210 280 350 SE +/- 0.43, N = 3 SE +/- 0.87, N = 3 323.79 317.27
TensorFlow Device: CPU - Batch Size: 512 - Model: ResNet-50 OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: ResNet-50 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 1.13, N = 3 SE +/- 1.17, N = 3 85.97 84.84
CloverLeaf Lagrangian-Eulerian Hydrodynamics OpenBenchmarking.org Seconds, Fewer Is Better CloverLeaf Lagrangian-Eulerian Hydrodynamics 0xd0003a5 0xd000390 3 6 9 12 15 SE +/- 0.09, N = 3 SE +/- 0.06, N = 3 11.98 12.04 1. (F9X) gfortran options: -O3 -march=native -funroll-loops -fopenmp
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 16 32 48 64 80 SE +/- 0.09, N = 3 SE +/- 0.10, N = 3 72.07 71.89
Neural Magic DeepSparse Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 120 240 360 480 600 SE +/- 0.77, N = 3 SE +/- 1.19, N = 3 551.12 553.27
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 500 1000 1500 2000 2500 SE +/- 1.14, N = 3 SE +/- 3.15, N = 3 2301.16 2068.30
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 5 10 15 20 25 SE +/- 0.01, N = 3 SE +/- 0.03, N = 3 17.35 19.31
Neural Magic DeepSparse Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream 0xd0003a5 0xd000390 200 400 600 800 1000 SE +/- 1.22, N = 3 SE +/- 11.47, N = 3 930.75 922.93
Neural Magic DeepSparse Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream 0xd0003a5 0xd000390 10 20 30 40 50 SE +/- 0.06, N = 3 SE +/- 0.53, N = 3 42.94 43.30
Neural Magic DeepSparse Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream 0xd0003a5 0xd000390 50 100 150 200 250 SE +/- 2.37, N = 3 SE +/- 0.46, N = 3 231.43 230.05
Neural Magic DeepSparse Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream 0xd0003a5 0xd000390 40 80 120 160 200 SE +/- 1.64, N = 3 SE +/- 0.34, N = 3 172.56 173.79
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream 0xd0003a5 0xd000390 200 400 600 800 1000 SE +/- 0.31, N = 3 SE +/- 0.56, N = 3 1013.82 1006.34
Neural Magic DeepSparse Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream 0xd0003a5 0xd000390 9 18 27 36 45 SE +/- 0.01, N = 3 SE +/- 0.02, N = 3 39.41 39.70
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 1600 3200 4800 6400 8000 SE +/- 8.14, N = 3 SE +/- 2.44, N = 3 7633.26 7092.50
Neural Magic DeepSparse Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 1.2647 2.5294 3.7941 5.0588 6.3235 SE +/- 0.0056, N = 3 SE +/- 0.0018, N = 3 5.2210 5.6210
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 90 180 270 360 450 SE +/- 0.36, N = 3 SE +/- 1.24, N = 3 422.12 412.05
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.07, N = 3 SE +/- 0.29, N = 3 94.61 96.94
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.12, N = 3 SE +/- 0.51, N = 3 98.34 94.26
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 90 180 270 360 450 SE +/- 0.42, N = 3 SE +/- 2.15, N = 3 405.97 421.75
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 200 400 600 800 1000 SE +/- 1.41, N = 3 SE +/- 2.17, N = 3 1005.58 987.55
Neural Magic DeepSparse Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 9 18 27 36 45 SE +/- 0.06, N = 3 SE +/- 0.10, N = 3 39.73 40.45
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 90 180 270 360 450 SE +/- 0.21, N = 3 SE +/- 4.97, N = 4 426.95 398.72
Neural Magic DeepSparse Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.07, N = 3 SE +/- 1.28, N = 4 93.54 100.19
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 140 280 420 560 700 SE +/- 2.45, N = 3 SE +/- 7.47, N = 3 644.44 620.25
Neural Magic DeepSparse Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 14 28 42 56 70 SE +/- 0.23, N = 3 SE +/- 0.78, N = 3 62.01 64.44
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.32, N = 3 SE +/- 0.18, N = 3 84.02 83.20
Neural Magic DeepSparse Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 100 200 300 400 500 SE +/- 1.00, N = 3 SE +/- 1.39, N = 3 474.69 478.85
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 200 400 600 800 1000 SE +/- 1.88, N = 3 SE +/- 1.83, N = 3 1051.79 869.04
Neural Magic DeepSparse Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 10 20 30 40 50 SE +/- 0.07, N = 3 SE +/- 0.10, N = 3 37.99 45.96
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 70 140 210 280 350 SE +/- 0.57, N = 3 SE +/- 1.05, N = 3 307.87 293.12
Neural Magic DeepSparse Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 30 60 90 120 150 SE +/- 0.25, N = 3 SE +/- 0.60, N = 3 129.81 136.18
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org items/sec, More Is Better Neural Magic DeepSparse 1.5 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 16 32 48 64 80 SE +/- 0.15, N = 3 SE +/- 0.12, N = 3 72.18 71.54
Neural Magic DeepSparse Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream OpenBenchmarking.org ms/batch, Fewer Is Better Neural Magic DeepSparse 1.5 Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream 0xd000390 0xd0003a5 120 240 360 480 600 SE +/- 0.62, N = 3 SE +/- 1.12, N = 3 551.82 555.09
ONNX Runtime Model: GPT-2 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.14 Model: GPT-2 - Device: CPU - Executor: Standard 0xd0003a5 0xd000390 40 80 120 160 200 SE +/- 4.37, N = 15 SE +/- 0.08, N = 3 190.57 180.16 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.14 Model: yolov4 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.11, N = 7 SE +/- 0.13, N = 15 11.66 11.15 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: bertsquad-12 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.14 Model: bertsquad-12 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 4 8 12 16 20 SE +/- 0.08, N = 3 SE +/- 0.26, N = 12 16.71 16.31 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.14 Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 150 300 450 600 750 SE +/- 3.08, N = 3 SE +/- 12.01, N = 15 696.73 664.54 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.14 Model: fcn-resnet101-11 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.10283, N = 15 SE +/- 0.23491, N = 15 9.08067 8.59908 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.14 Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 9 18 27 36 45 SE +/- 0.46, N = 3 SE +/- 0.38, N = 15 39.12 38.72 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.14 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 50 100 150 200 250 SE +/- 1.39, N = 3 SE +/- 1.91, N = 8 221.02 216.48 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Standard OpenBenchmarking.org Inferences Per Second, More Is Better ONNX Runtime 1.14 Model: super-resolution-10 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 30 60 90 120 150 SE +/- 0.13, N = 3 SE +/- 10.25, N = 15 158.36 158.01 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
NCNN Target: CPU - Model: mobilenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: mobilenet 0xd000390 0xd0003a5 4 8 12 16 20 SE +/- 0.09, N = 3 SE +/- 0.07, N = 3 15.46 15.66 MIN: 14.85 / MAX: 106.56 MIN: 15.24 / MAX: 38.75 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU-v2-v2 - Model: mobilenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU-v2-v2 - Model: mobilenet-v2 0xd0003a5 0xd000390 2 4 6 8 10 SE +/- 0.04, N = 3 SE +/- 0.09, N = 3 7.96 8.03 MIN: 7.75 / MAX: 31.75 MIN: 7.8 / MAX: 31.34 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU-v3-v3 - Model: mobilenet-v3 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU-v3-v3 - Model: mobilenet-v3 0xd0003a5 0xd000390 2 4 6 8 10 SE +/- 0.12, N = 3 SE +/- 0.05, N = 3 8.76 8.88 MIN: 8.3 / MAX: 32.6 MIN: 8.69 / MAX: 32.33 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: shufflenet-v2 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: shufflenet-v2 0xd0003a5 0xd000390 3 6 9 12 15 SE +/- 0.13, N = 3 SE +/- 0.10, N = 3 9.76 9.89 MIN: 9.32 / MAX: 33.4 MIN: 9.61 / MAX: 33.59 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: mnasnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: mnasnet 0xd0003a5 0xd000390 2 4 6 8 10 SE +/- 0.04, N = 3 SE +/- 0.10, N = 3 7.41 7.57 MIN: 7.15 / MAX: 31.17 MIN: 7.28 / MAX: 30.26 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: efficientnet-b0 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: efficientnet-b0 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.38, N = 3 SE +/- 0.11, N = 3 11.62 11.71 MIN: 10.82 / MAX: 21.03 MIN: 11.15 / MAX: 19.87 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: blazeface OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: blazeface 0xd000390 0xd0003a5 1.0215 2.043 3.0645 4.086 5.1075 SE +/- 0.06, N = 3 SE +/- 0.07, N = 3 4.35 4.54 MIN: 4.16 / MAX: 4.97 MIN: 4.37 / MAX: 5.17 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: googlenet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: googlenet 0xd000390 0xd0003a5 4 8 12 16 20 SE +/- 0.17, N = 3 SE +/- 0.48, N = 3 15.36 16.58 MIN: 14.6 / MAX: 182.22 MIN: 15.29 / MAX: 39.63 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: vgg16 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: vgg16 0xd000390 0xd0003a5 6 12 18 24 30 SE +/- 0.25, N = 3 SE +/- 0.19, N = 3 23.86 25.71 MIN: 23.05 / MAX: 47.41 MIN: 24.88 / MAX: 62.96 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: resnet18 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: resnet18 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.14, N = 3 SE +/- 0.09, N = 2 8.97 9.42 MIN: 8.63 / MAX: 27.27 MIN: 9.21 / MAX: 32.8 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: alexnet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: alexnet 0xd000390 0xd0003a5 1.2285 2.457 3.6855 4.914 6.1425 SE +/- 0.16, N = 3 SE +/- 0.15, N = 3 5.39 5.46 MIN: 4.83 / MAX: 151.52 MIN: 5.01 / MAX: 29.14 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: resnet50 OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: resnet50 0xd000390 0xd0003a5 5 10 15 20 25 SE +/- 0.52, N = 3 SE +/- 0.69, N = 3 17.15 18.51 MIN: 16.19 / MAX: 41.83 MIN: 17.32 / MAX: 299.93 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: yolov4-tiny OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: yolov4-tiny 0xd000390 0xd0003a5 6 12 18 24 30 SE +/- 0.22, N = 3 SE +/- 0.17, N = 3 23.71 24.10 MIN: 22.57 / MAX: 46.11 MIN: 23.25 / MAX: 51.47 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: squeezenet_ssd OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: squeezenet_ssd 0xd000390 0xd0003a5 4 8 12 16 20 SE +/- 0.12, N = 3 SE +/- 0.25, N = 3 15.34 16.10 MIN: 14.63 / MAX: 39.65 MIN: 15.43 / MAX: 48.02 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: regnety_400m OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: regnety_400m 0xd0003a5 0xd000390 10 20 30 40 50 SE +/- 0.50, N = 3 SE +/- 7.69, N = 3 38.85 45.54 MIN: 37.13 / MAX: 233.44 MIN: 36.01 / MAX: 3343.68 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: vision_transformer OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: vision_transformer 0xd0003a5 0xd000390 11 22 33 44 55 SE +/- 0.31, N = 3 SE +/- 1.17, N = 3 45.23 46.92 MIN: 43.43 / MAX: 73.39 MIN: 43.11 / MAX: 881.49 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
NCNN Target: CPU - Model: FastestDet OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: FastestDet 0xd0003a5 0xd000390 3 6 9 12 15 SE +/- 0.07, N = 3 SE +/- 0.64, N = 3 9.71 10.20 MIN: 9.22 / MAX: 27.29 MIN: 9.01 / MAX: 500.17 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
GROMACS Implementation: MPI CPU - Input: water_GMX50_bare OpenBenchmarking.org Ns Per Day, More Is Better GROMACS 2023 Implementation: MPI CPU - Input: water_GMX50_bare 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.021, N = 3 SE +/- 0.026, N = 3 9.234 9.094 1. (CXX) g++ options: -O3
oneDNN Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.1 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU 0xd0003a5 0xd000390 0.5834 1.1668 1.7502 2.3336 2.917 SE +/- 0.02489, N = 15 SE +/- 0.03265, N = 15 2.57437 2.59271 MIN: 1.99 MIN: 1.91 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.1 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU 0xd000390 0xd0003a5 0.4657 0.9314 1.3971 1.8628 2.3285 SE +/- 0.00038, N = 3 SE +/- 0.00191, N = 3 2.06936 2.06967 MIN: 2.03 MIN: 2.03 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.1 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU 0xd000390 0xd0003a5 0.8805 1.761 2.6415 3.522 4.4025 SE +/- 0.00202, N = 3 SE +/- 0.00080, N = 3 3.90360 3.91322 MIN: 3.68 MIN: 3.69 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.1 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU 0xd0003a5 0xd000390 0.8157 1.6314 2.4471 3.2628 4.0785 SE +/- 0.00622, N = 3 SE +/- 0.00896, N = 3 3.62266 3.62526 MIN: 3.53 MIN: 3.54 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.1 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU 0xd0003a5 0xd000390 200 400 600 800 1000 SE +/- 14.20, N = 12 SE +/- 16.59, N = 15 816.51 832.45 MIN: 723.44 MIN: 714.88 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
oneDNN Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.1 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU 0xd0003a5 0xd000390 110 220 330 440 550 SE +/- 2.82, N = 3 SE +/- 7.19, N = 3 521.74 524.38 MIN: 505.72 MIN: 499.75 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
OpenVINO Model: Face Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Face Detection FP16 - Device: CPU 0xd0003a5 0xd000390 6 12 18 24 30 SE +/- 0.02, N = 3 SE +/- 0.01, N = 3 24.18 24.04 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Face Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Face Detection FP16 - Device: CPU 0xd0003a5 0xd000390 200 400 600 800 1000 SE +/- 0.71, N = 3 SE +/- 0.42, N = 3 823.09 827.51 MIN: 550.41 / MAX: 926.21 MIN: 628.21 / MAX: 980.48 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Person Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Person Detection FP16 - Device: CPU 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.02, N = 3 SE +/- 0.01, N = 3 13.29 13.25 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Person Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Person Detection FP16 - Device: CPU 0xd000390 0xd0003a5 300 600 900 1200 1500 SE +/- 2.35, N = 3 SE +/- 0.81, N = 3 1490.76 1496.19 MIN: 1074.22 / MAX: 1692.48 MIN: 1043.7 / MAX: 1711.44 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Person Detection FP32 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Person Detection FP32 - Device: CPU 0xd0003a5 0xd000390 3 6 9 12 15 SE +/- 0.01, N = 3 SE +/- 0.00, N = 3 13.04 13.03 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Person Detection FP32 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Person Detection FP32 - Device: CPU 0xd000390 0xd0003a5 300 600 900 1200 1500 SE +/- 0.52, N = 3 SE +/- 0.84, N = 3 1517.69 1519.84 MIN: 1081.6 / MAX: 1690.6 MIN: 1074.08 / MAX: 1721.59 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Vehicle Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Vehicle Detection FP16 - Device: CPU 0xd000390 0xd0003a5 200 400 600 800 1000 SE +/- 0.88, N = 3 SE +/- 1.14, N = 3 1121.56 1117.09 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Vehicle Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Vehicle Detection FP16 - Device: CPU 0xd000390 0xd0003a5 4 8 12 16 20 SE +/- 0.01, N = 3 SE +/- 0.02, N = 3 17.80 17.87 MIN: 12.83 / MAX: 32.64 MIN: 12.24 / MAX: 38.45 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Face Detection FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Face Detection FP16-INT8 - Device: CPU 0xd0003a5 0xd000390 20 40 60 80 100 SE +/- 0.07, N = 3 SE +/- 0.01, N = 3 95.54 95.42 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Face Detection FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Face Detection FP16-INT8 - Device: CPU 0xd0003a5 0xd000390 50 100 150 200 250 SE +/- 0.14, N = 3 SE +/- 0.03, N = 3 209.02 209.31 MIN: 152.83 / MAX: 234.28 MIN: 160.46 / MAX: 249.09 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Vehicle Detection FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Vehicle Detection FP16-INT8 - Device: CPU 0xd0003a5 0xd000390 1000 2000 3000 4000 5000 SE +/- 2.64, N = 3 SE +/- 3.29, N = 3 4442.98 4419.17 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Vehicle Detection FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Vehicle Detection FP16-INT8 - Device: CPU 0xd0003a5 0xd000390 1.0148 2.0296 3.0444 4.0592 5.074 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 4.49 4.51 MIN: 4.04 / MAX: 15.85 MIN: 4.02 / MAX: 13.95 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Weld Porosity Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Weld Porosity Detection FP16 - Device: CPU 0xd0003a5 0xd000390 500 1000 1500 2000 2500 SE +/- 3.91, N = 3 SE +/- 3.01, N = 3 2362.84 2344.97 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Weld Porosity Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Weld Porosity Detection FP16 - Device: CPU 0xd0003a5 0xd000390 8 16 24 32 40 SE +/- 0.06, N = 3 SE +/- 0.05, N = 3 33.63 33.89 MIN: 29.54 / MAX: 113.55 MIN: 29.83 / MAX: 113.13 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Machine Translation EN To DE FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Machine Translation EN To DE FP16 - Device: CPU 0xd0003a5 0xd000390 60 120 180 240 300 SE +/- 0.63, N = 3 SE +/- 0.68, N = 3 255.09 251.00 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Machine Translation EN To DE FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Machine Translation EN To DE FP16 - Device: CPU 0xd0003a5 0xd000390 20 40 60 80 100 SE +/- 0.19, N = 3 SE +/- 0.22, N = 3 78.18 79.47 MIN: 66.08 / MAX: 194.38 MIN: 62.57 / MAX: 232.01 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Weld Porosity Detection FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Weld Porosity Detection FP16-INT8 - Device: CPU 0xd0003a5 0xd000390 2K 4K 6K 8K 10K SE +/- 2.19, N = 3 SE +/- 3.74, N = 3 9419.77 9396.52 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Weld Porosity Detection FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Weld Porosity Detection FP16-INT8 - Device: CPU 0xd0003a5 0xd000390 2 4 6 8 10 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 8.48 8.50 MIN: 7.15 / MAX: 22.53 MIN: 7.17 / MAX: 18.39 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Person Vehicle Bike Detection FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Person Vehicle Bike Detection FP16 - Device: CPU 0xd0003a5 0xd000390 400 800 1200 1600 2000 SE +/- 3.75, N = 3 SE +/- 1.77, N = 3 2070.72 2039.63 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Person Vehicle Bike Detection FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Person Vehicle Bike Detection FP16 - Device: CPU 0xd0003a5 0xd000390 3 6 9 12 15 SE +/- 0.02, N = 3 SE +/- 0.01, N = 3 9.63 9.77 MIN: 8.33 / MAX: 19.33 MIN: 7.83 / MAX: 20.01 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU 0xd0003a5 0xd000390 13K 26K 39K 52K 65K SE +/- 18.68, N = 3 SE +/- 17.38, N = 3 59377.96 59274.06 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU 0xd000390 0xd0003a5 0.2993 0.5986 0.8979 1.1972 1.4965 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 1.33 1.33 MIN: 0.97 / MAX: 13 MIN: 0.97 / MAX: 13.43 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU 0xd0003a5 0xd000390 15K 30K 45K 60K 75K SE +/- 29.72, N = 3 SE +/- 17.48, N = 3 67754.34 67604.00 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
OpenVINO Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU 0xd000390 0xd0003a5 0.261 0.522 0.783 1.044 1.305 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 1.16 1.16 MIN: 0.88 / MAX: 12.61 MIN: 0.86 / MAX: 17.98 1. (CXX) g++ options: -isystem -fsigned-char -ffunction-sections -fdata-sections -msse4.1 -msse4.2 -O3 -fno-strict-overflow -fwrapv -fPIC -fvisibility=hidden -Os -std=c++11 -MD -MT -MF
QMCPACK Input: Li2_STO_ae OpenBenchmarking.org Total Execution Time - Seconds, Fewer Is Better QMCPACK 3.16 Input: Li2_STO_ae 0xd0003a5 0xd000390 30 60 90 120 150 SE +/- 1.03, N = 3 SE +/- 1.55, N = 3 123.26 124.23 1. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl
QMCPACK Input: simple-H2O OpenBenchmarking.org Total Execution Time - Seconds, Fewer Is Better QMCPACK 3.16 Input: simple-H2O 0xd000390 0xd0003a5 9 18 27 36 45 SE +/- 0.12, N = 3 SE +/- 0.02, N = 3 39.56 41.25 1. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl
QMCPACK Input: FeCO6_b3lyp_gms OpenBenchmarking.org Total Execution Time - Seconds, Fewer Is Better QMCPACK 3.16 Input: FeCO6_b3lyp_gms 0xd000390 0xd0003a5 40 80 120 160 200 SE +/- 0.12, N = 3 SE +/- 0.31, N = 3 147.51 178.19 1. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl
QMCPACK Input: FeCO6_b3lyp_gms OpenBenchmarking.org Total Execution Time - Seconds, Fewer Is Better QMCPACK 3.16 Input: FeCO6_b3lyp_gms 0xd0003a5 0xd000390 60 120 180 240 300 SE +/- 2.19, N = 3 SE +/- 3.60, N = 3 263.23 268.56 1. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl
Xcompact3d Incompact3d Input: input.i3d 193 Cells Per Direction OpenBenchmarking.org Seconds, Fewer Is Better Xcompact3d Incompact3d 2021-03-11 Input: input.i3d 193 Cells Per Direction 0xd0003a5 0xd000390 3 6 9 12 15 SE +/- 0.02, N = 3 SE +/- 0.02, N = 3 11.00 11.02 1. (F9X) gfortran options: -cpp -O2 -funroll-loops -floop-optimize -fcray-pointer -fbacktrace -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz
Cpuminer-Opt Algorithm: Magi OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Magi 0xd000390 0xd0003a5 500 1000 1500 2000 2500 SE +/- 3.91, N = 3 SE +/- 1.08, N = 3 2309.47 2308.66 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
Cpuminer-Opt Algorithm: x25x OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: x25x 0xd0003a5 0xd000390 600 1200 1800 2400 3000 SE +/- 5.54, N = 3 SE +/- 4.75, N = 3 2659.55 2659.17 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
Cpuminer-Opt Algorithm: scrypt OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: scrypt 0xd0003a5 0xd000390 500 1000 1500 2000 2500 SE +/- 8.35, N = 3 SE +/- 1.08, N = 3 2321.74 2319.31 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
Cpuminer-Opt Algorithm: Deepcoin OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Deepcoin 0xd0003a5 0xd000390 14K 28K 42K 56K 70K SE +/- 187.02, N = 3 SE +/- 89.69, N = 3 64897 64677 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
Cpuminer-Opt Algorithm: Blake-2 S OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Blake-2 S 0xd0003a5 0xd000390 1000K 2000K 3000K 4000K 5000K SE +/- 8676.85, N = 3 SE +/- 8325.80, N = 3 4466653 4462327 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
Cpuminer-Opt Algorithm: Garlicoin OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Garlicoin 0xd000390 0xd0003a5 6K 12K 18K 24K 30K SE +/- 330.17, N = 3 SE +/- 3833.17, N = 12 29203.00 22086.25 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
Cpuminer-Opt Algorithm: Skeincoin OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Skeincoin 0xd0003a5 0xd000390 130K 260K 390K 520K 650K SE +/- 3788.20, N = 3 SE +/- 1652.89, N = 3 617130 613333 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
Cpuminer-Opt Algorithm: Myriad-Groestl OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Myriad-Groestl 0xd0003a5 0xd000390 9K 18K 27K 36K 45K SE +/- 406.32, N = 3 SE +/- 386.00, N = 15 43450 43127 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
Cpuminer-Opt Algorithm: LBC, LBRY Credits OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: LBC, LBRY Credits 0xd0003a5 0xd000390 90K 180K 270K 360K 450K SE +/- 860.95, N = 3 SE +/- 313.42, N = 3 423130 421660 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
Cpuminer-Opt Algorithm: Quad SHA-256, Pyrite OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Quad SHA-256, Pyrite 0xd0003a5 0xd000390 200K 400K 600K 800K 1000K SE +/- 1690.96, N = 3 SE +/- 3352.41, N = 3 926277 921730 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
Cpuminer-Opt Algorithm: Triple SHA-256, Onecoin OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Triple SHA-256, Onecoin 0xd0003a5 0xd000390 300K 600K 900K 1200K 1500K SE +/- 7235.75, N = 3 SE +/- 7105.40, N = 3 1333117 1332237 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
VP9 libvpx Encoding Speed: Speed 5 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better VP9 libvpx Encoding 1.13 Speed: Speed 5 - Input: Bosphorus 4K 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.12, N = 3 SE +/- 0.13, N = 3 12.63 12.33 1. (CXX) g++ options: -m64 -lm -lpthread -O3 -fPIC -U_FORTIFY_SOURCE -std=gnu++11
dav1d Video Input: Chimera 1080p OpenBenchmarking.org FPS, More Is Better dav1d 1.2.1 Video Input: Chimera 1080p 0xd000390 0xd0003a5 110 220 330 440 550 SE +/- 0.80, N = 3 SE +/- 0.51, N = 3 515.81 514.58 1. (CC) gcc options: -pthread -lm
dav1d Video Input: Summer Nature 4K OpenBenchmarking.org FPS, More Is Better dav1d 1.2.1 Video Input: Summer Nature 4K 0xd000390 0xd0003a5 60 120 180 240 300 SE +/- 1.06, N = 3 SE +/- 0.81, N = 3 281.36 280.84 1. (CC) gcc options: -pthread -lm
SVT-AV1 Encoder Mode: Preset 8 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.6 Encoder Mode: Preset 8 - Input: Bosphorus 4K 0xd000390 0xd0003a5 15 30 45 60 75 SE +/- 0.47, N = 3 SE +/- 0.46, N = 3 67.17 66.46 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 12 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.6 Encoder Mode: Preset 12 - Input: Bosphorus 4K 0xd000390 0xd0003a5 40 80 120 160 200 SE +/- 1.29, N = 3 SE +/- 1.20, N = 3 180.97 177.72 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-AV1 Encoder Mode: Preset 13 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.6 Encoder Mode: Preset 13 - Input: Bosphorus 4K 0xd0003a5 0xd000390 40 80 120 160 200 SE +/- 2.02, N = 3 SE +/- 0.79, N = 3 177.20 175.10 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-HEVC Tuning: 1 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-HEVC 1.5.0 Tuning: 1 - Input: Bosphorus 4K 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.06, N = 3 SE +/- 0.01, N = 3 10.46 10.45 1. (CC) gcc options: -fPIE -fPIC -O3 -O2 -pie -rdynamic -lpthread -lrt
SVT-HEVC Tuning: 7 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-HEVC 1.5.0 Tuning: 7 - Input: Bosphorus 4K 0xd000390 0xd0003a5 30 60 90 120 150 SE +/- 0.60, N = 3 SE +/- 0.44, N = 3 138.75 138.49 1. (CC) gcc options: -fPIE -fPIC -O3 -O2 -pie -rdynamic -lpthread -lrt
SVT-HEVC Tuning: 10 - Input: Bosphorus 4K OpenBenchmarking.org Frames Per Second, More Is Better SVT-HEVC 1.5.0 Tuning: 10 - Input: Bosphorus 4K 0xd000390 0xd0003a5 40 80 120 160 200 SE +/- 2.03, N = 3 SE +/- 0.38, N = 3 184.38 182.74 1. (CC) gcc options: -fPIE -fPIC -O3 -O2 -pie -rdynamic -lpthread -lrt
Blender Blend File: BMW27 - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 3.6 Blend File: BMW27 - Compute: CPU-Only 0xd0003a5 0xd000390 6 12 18 24 30 SE +/- 0.03, N = 3 SE +/- 0.06, N = 3 23.72 23.83
Blender Blend File: Fishy Cat - Compute: CPU-Only OpenBenchmarking.org Seconds, Fewer Is Better Blender 3.6 Blend File: Fishy Cat - Compute: CPU-Only 0xd000390 0xd0003a5 7 14 21 28 35 SE +/- 0.06, N = 3 SE +/- 0.02, N = 3 30.74 30.90
VVenC Video Input: Bosphorus 4K - Video Preset: Fast OpenBenchmarking.org Frames Per Second, More Is Better VVenC 1.9 Video Input: Bosphorus 4K - Video Preset: Fast 0xd000390 0xd0003a5 1.2875 2.575 3.8625 5.15 6.4375 SE +/- 0.033, N = 3 SE +/- 0.029, N = 3 5.722 5.705 1. (CXX) g++ options: -O3 -flto=auto -fno-fat-lto-objects
VVenC Video Input: Bosphorus 4K - Video Preset: Faster OpenBenchmarking.org Frames Per Second, More Is Better VVenC 1.9 Video Input: Bosphorus 4K - Video Preset: Faster 0xd0003a5 0xd000390 3 6 9 12 15 SE +/- 0.06, N = 3 SE +/- 0.09, N = 3 10.42 10.36 1. (CXX) g++ options: -O3 -flto=auto -fno-fat-lto-objects
Embree Binary: Pathtracer ISPC - Model: Crown OpenBenchmarking.org Frames Per Second, More Is Better Embree 4.1 Binary: Pathtracer ISPC - Model: Crown 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.07, N = 3 SE +/- 0.32, N = 3 88.19 83.46 MIN: 85.24 / MAX: 92.72 MIN: 80.27 / MAX: 87.69
Embree Binary: Pathtracer ISPC - Model: Asian Dragon OpenBenchmarking.org Frames Per Second, More Is Better Embree 4.1 Binary: Pathtracer ISPC - Model: Asian Dragon 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.43, N = 3 SE +/- 0.19, N = 3 104.68 101.10 MIN: 101.9 / MAX: 109.48 MIN: 98.59 / MAX: 105.53
Intel Open Image Denoise Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only OpenBenchmarking.org Images / Sec, More Is Better Intel Open Image Denoise 2.0 Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only 0xd0003a5 0xd000390 0.6818 1.3636 2.0454 2.7272 3.409 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 3.03 3.03
Intel Open Image Denoise Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only OpenBenchmarking.org Images / Sec, More Is Better Intel Open Image Denoise 2.0 Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only 0xd0003a5 0xd000390 0.3285 0.657 0.9855 1.314 1.6425 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 1.46 1.46
OpenVKL Benchmark: vklBenchmark ISPC OpenBenchmarking.org Items / Sec, More Is Better OpenVKL 1.3.1 Benchmark: vklBenchmark ISPC 0xd000390 0xd0003a5 200 400 600 800 1000 SE +/- 1.53, N = 3 SE +/- 0.88, N = 3 912 856 MIN: 140 / MAX: 7236 MIN: 137 / MAX: 7211
OSPRay Benchmark: particle_volume/ao/real_time OpenBenchmarking.org Items Per Second, More Is Better OSPRay 2.12 Benchmark: particle_volume/ao/real_time 0xd000390 0xd0003a5 6 12 18 24 30 SE +/- 0.11, N = 3 SE +/- 0.01, N = 3 24.75 16.46
OSPRay Benchmark: particle_volume/scivis/real_time OpenBenchmarking.org Items Per Second, More Is Better OSPRay 2.12 Benchmark: particle_volume/scivis/real_time 0xd000390 0xd0003a5 6 12 18 24 30 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 24.95 16.38
OSPRay Benchmark: particle_volume/pathtracer/real_time OpenBenchmarking.org Items Per Second, More Is Better OSPRay 2.12 Benchmark: particle_volume/pathtracer/real_time 0xd000390 0xd0003a5 30 60 90 120 150 SE +/- 0.54, N = 3 SE +/- 0.45, N = 3 150.28 136.85
OSPRay Benchmark: gravity_spheres_volume/dim_512/ao/real_time OpenBenchmarking.org Items Per Second, More Is Better OSPRay 2.12 Benchmark: gravity_spheres_volume/dim_512/ao/real_time 0xd000390 0xd0003a5 5 10 15 20 25 SE +/- 0.05, N = 3 SE +/- 0.02, N = 3 21.08 18.89
OSPRay Benchmark: gravity_spheres_volume/dim_512/scivis/real_time OpenBenchmarking.org Items Per Second, More Is Better OSPRay 2.12 Benchmark: gravity_spheres_volume/dim_512/scivis/real_time 0xd000390 0xd0003a5 5 10 15 20 25 SE +/- 0.08, N = 3 SE +/- 0.03, N = 3 20.58 18.46
simdjson Throughput Test: Kostya OpenBenchmarking.org GB/s, More Is Better simdjson 2.0 Throughput Test: Kostya 0xd0003a5 0xd000390 0.6458 1.2916 1.9374 2.5832 3.229 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 2.87 2.61 1. (CXX) g++ options: -O3
simdjson Throughput Test: TopTweet OpenBenchmarking.org GB/s, More Is Better simdjson 2.0 Throughput Test: TopTweet 0xd0003a5 0xd000390 1.2938 2.5876 3.8814 5.1752 6.469 SE +/- 0.01, N = 3 SE +/- 0.03, N = 3 5.75 5.60 1. (CXX) g++ options: -O3
simdjson Throughput Test: LargeRandom OpenBenchmarking.org GB/s, More Is Better simdjson 2.0 Throughput Test: LargeRandom 0xd0003a5 0xd000390 0.216 0.432 0.648 0.864 1.08 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 0.96 0.85 1. (CXX) g++ options: -O3
simdjson Throughput Test: PartialTweets OpenBenchmarking.org GB/s, More Is Better simdjson 2.0 Throughput Test: PartialTweets 0xd0003a5 0xd000390 1.0733 2.1466 3.2199 4.2932 5.3665 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 4.77 4.62 1. (CXX) g++ options: -O3
simdjson Throughput Test: DistinctUserID OpenBenchmarking.org GB/s, More Is Better simdjson 2.0 Throughput Test: DistinctUserID 0xd0003a5 0xd000390 1.2848 2.5696 3.8544 5.1392 6.424 SE +/- 0.00, N = 3 SE +/- 0.02, N = 3 5.71 5.52 1. (CXX) g++ options: -O3
ONNX Runtime Model: GPT-2 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.14 Model: GPT-2 - Device: CPU - Executor: Standard 0xd0003a5 0xd000390 1.2483 2.4966 3.7449 4.9932 6.2415 SE +/- 0.11956, N = 15 SE +/- 0.00245, N = 3 5.28095 5.54783 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: yolov4 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.14 Model: yolov4 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.79, N = 7 SE +/- 1.07, N = 15 85.80 89.83 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: bertsquad-12 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.14 Model: bertsquad-12 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 14 28 42 56 70 SE +/- 0.28, N = 3 SE +/- 1.14, N = 12 59.84 61.51 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.14 Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 0.3398 0.6796 1.0194 1.3592 1.699 SE +/- 0.00633, N = 3 SE +/- 0.02722, N = 15 1.43407 1.51029 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: fcn-resnet101-11 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.14 Model: fcn-resnet101-11 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 30 60 90 120 150 SE +/- 1.29, N = 15 SE +/- 3.29, N = 15 110.32 117.55 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.14 Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 6 12 18 24 30 SE +/- 0.30, N = 3 SE +/- 0.26, N = 15 25.57 25.86 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.14 Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 1.0397 2.0794 3.1191 4.1588 5.1985 SE +/- 0.02854, N = 3 SE +/- 0.04028, N = 8 4.52403 4.62085 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
ONNX Runtime Model: super-resolution-10 - Device: CPU - Executor: Standard OpenBenchmarking.org Inference Time Cost (ms), Fewer Is Better ONNX Runtime 1.14 Model: super-resolution-10 - Device: CPU - Executor: Standard 0xd000390 0xd0003a5 2 4 6 8 10 SE +/- 0.00502, N = 3 SE +/- 0.43315, N = 15 6.31428 6.71968 1. (CXX) g++ options: -ffunction-sections -fdata-sections -march=native -mtune=native -O3 -flto=auto -fno-fat-lto-objects -ldl -lrt
Phoronix Test Suite v10.8.5