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.
0xd000390 Processor: 2 x Intel Xeon Platinum 8380 @ 3.40GHz (80 Cores / 160 Threads), Motherboard: Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS), Chipset: Intel Ice Lake IEH, Memory: 512GB, Disk: 7682GB INTEL SSDPF2KX076TZ, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel X710 for 10GBASE-T + 2 x Intel E810-C for QSFP
OS: Ubuntu 22.10, Kernel: 6.5.0-060500rc4daily20230804-generic (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080
Kernel Notes: Transparent Huge Pages: madviseCompiler Notes: --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 -vProcessor Notes: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0xd000390Python Notes: Python 3.10.7Security Notes: 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 OS: Ubuntu 22.10, Kernel: 6.5.0-rc5-phx-tues (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080
Kernel Notes: Transparent Huge Pages: madviseCompiler Notes: --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 -vProcessor Notes: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0xd0003a5Python Notes: Python 3.10.7Security Notes: 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 OpenBenchmarking.org Phoronix Test Suite 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) 6.5.0-rc5-phx-tues (x86_64) GNOME Shell 43.0 X Server 1.21.1.3 1.3.224 GCC 12.2.0 ext4 1920x1080 Processor Motherboard Chipset Memory Disk Graphics Monitor Network OS Kernels Desktop Display Server Vulkan Compiler File-System Screen Resolution Xeon Platinum 8380 AVX-512 Workloads Performance System Logs - Transparent Huge Pages: madvise - --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 - 0xd000390: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0xd000390 - 0xd0003a5: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0xd0003a5 - Python 3.10.7 - 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
0xd000390 vs. 0xd0003a5 Comparison Phoronix Test Suite Baseline +13.1% +13.1% +26.2% +26.2% +39.3% +39.3% 17.2% 12.9% 10.4% 10% 8% 7.1% 6.1% 5.8% 5% 3.9% 3.7% 3.6% 3.4% 3.2% 3% 2.9% 2.9% 2.8% 2.7% 2.2% 2.1% 2% 2% 5.1% particle_volume/scivis/real_time 52.3% particle_volume/ao/real_time 50.3% Garlicoin 32.2% B.L.N.Q.A.S.I - A.M.S 21% B.L.N.Q.A.S.I - A.M.S 21% FeCO6_b3lyp_gms 20.8% CPU - regnety_400m LargeRand gravity_spheres_volume/dim_512/ao/real_time 11.6% gravity_spheres_volume/dim_512/scivis/real_time 11.5% N.T.C.B.b.u.S.S.I - A.M.S 11.3% N.T.C.B.b.u.S.S.I - A.M.S 11.3% CPU - 512 - AlexNet Kostya particle_volume/pathtracer/real_time 9.8% CPU - 256 - AlexNet CPU - googlenet 7.9% CPU - resnet50 7.9% CPU - vgg16 7.8% R.5.S.I - A.M.S 7.7% R.5.S.I - A.M.S 7.6% 64 C.D.Y.C.S.I - A.M.S 7.1% C.D.Y.C.S.I - A.M.S 7.1% vklBenchmark ISPC 6.5% r2c - FFTW - double - 128 GPT-2 - CPU - Standard Pathtracer ISPC - Crown 5.7% fcn-resnet101-11 - CPU - Standard 5.6% CPU - FastestDet N.T.C.B.b.u.S - A.M.S 5% CPU - resnet18 5% CPU - squeezenet_ssd 5% N.T.C.B.b.u.S - A.M.S 4.9% CaffeNet 12-int8 - CPU - Standard 4.8% yolov4 - CPU - Standard 4.5% CPU - blazeface 4.4% B.L.N.Q.A - A.M.S 4.3% simple-H2O 4.3% N.T.C.D.m - A.M.S 3.9% CPU - 256 - GoogLeNet N.T.C.D.m - A.M.S 3.9% B.L.N.Q.A - A.M.S 3.9% CPU - vision_transformer CPU - 256 - ResNet-50 Pathtracer ISPC - Asian Dragon 3.5% DistinctUserID PartialTweets Tomographic Model OpenMP - BM2 OpenMP - BM2 c2c - FFTW - float - 128 TopTweet C.D.Y.C - A.M.S 2.5% bertsquad-12 - CPU - Standard 2.5% C.D.Y.C - A.M.S 2.4% Speed 5 - Bosphorus 4K 2.4% CPU - mnasnet R.v.1.i - CPU - Standard 2.1% CPU - 512 - GoogLeNet FeCO6_b3lyp_gms R.N.N.T - bf16bf16bf16 - CPU GPT-2 - CPU - Standard yolov4 - CPU - Standard 4.7% bertsquad-12 - CPU - Standard 2.8% CaffeNet 12-int8 - CPU - Standard 5.3% fcn-resnet101-11 - CPU - Standard 6.5% R.v.1.i - CPU - Standard 2.1% super-resolution-10 - CPU - Standard 6.4% OSPRay OSPRay Cpuminer-Opt Neural Magic DeepSparse Neural Magic DeepSparse QMCPACK NCNN simdjson OSPRay OSPRay Neural Magic DeepSparse Neural Magic DeepSparse TensorFlow simdjson OSPRay TensorFlow NCNN NCNN NCNN Neural Magic DeepSparse Neural Magic DeepSparse libxsmm Neural Magic DeepSparse Neural Magic DeepSparse OpenVKL HeFFTe - Highly Efficient FFT for Exascale ONNX Runtime Embree ONNX Runtime NCNN Neural Magic DeepSparse NCNN NCNN Neural Magic DeepSparse ONNX Runtime ONNX Runtime NCNN Neural Magic DeepSparse QMCPACK Neural Magic DeepSparse TensorFlow Neural Magic DeepSparse Neural Magic DeepSparse NCNN TensorFlow Embree simdjson simdjson SPECFEM3D miniBUDE miniBUDE HeFFTe - Highly Efficient FFT for Exascale simdjson Neural Magic DeepSparse ONNX Runtime Neural Magic DeepSparse VP9 libvpx Encoding NCNN ONNX Runtime TensorFlow QMCPACK oneDNN ONNX Runtime ONNX Runtime ONNX Runtime ONNX Runtime ONNX Runtime ONNX Runtime ONNX Runtime 0xd000390 0xd0003a5
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 simulates acoustic (fluid), elastic (solid), coupled acoustic/elastic, poroelastic or seismic wave propagation in any type of conforming mesh of hexahedra. This test profile currently relies on CPU-based execution for SPECFEM3D and using a variety of their built-in examples/models for benchmarking. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Seconds, Fewer Is Better SPECFEM3D 4.0 Model: Homogeneous Halfspace 0xd000390 0xd0003a5 4 8 12 16 20 SE +/- 0.15, N = 3 SE +/- 0.16, N = 3 18.02 17.75 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 MiniBUDE is a mini application for the the core computation of the Bristol University Docking Engine (BUDE). This test profile currently makes use of the OpenMP implementation of miniBUDE for CPU benchmarking. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org GFInst/s, More Is Better miniBUDE 20210901 Implementation: OpenMP - Input Deck: BM1 0xd000390 0xd0003a5 500 1000 1500 2000 2500 SE +/- 3.60, N = 3 SE +/- 10.90, N = 3 2353.39 2372.42 1. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm
HeFFTe - Highly Efficient FFT for Exascale HeFFTe is the Highly Efficient FFT for Exascale software developed as part of the Exascale Computing Project. This test profile uses HeFFTe's built-in speed benchmarks under a variety of configuration options and currently catering to CPU/processor testing. Learn more via the OpenBenchmarking.org test page.
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 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.15, N = 3 SE +/- 0.28, N = 3 93.09 93.92 1. (CXX) g++ options: -O3
Palabos The Palabos library is a framework for general purpose Computational Fluid Dynamics (CFD). Palabos uses a kernel based on the Lattice Boltzmann method. This test profile uses the Palabos MPI-based Cavity3D benchmark. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Mega Site Updates Per Second, More Is Better Palabos 2.3 Grid Size: 500 0xd000390 0xd0003a5 90 180 270 360 450 SE +/- 0.42, N = 3 SE +/- 0.72, N = 3 413.21 417.48 1. (CXX) g++ options: -std=c++17 -pedantic -O3 -rdynamic -lcrypto -lcurl -lsz -lz -ldl -lm
HeFFTe - Highly Efficient FFT for Exascale HeFFTe is the Highly Efficient FFT for Exascale software developed as part of the Exascale Computing Project. This test profile uses HeFFTe's built-in speed benchmarks under a variety of configuration options and currently catering to CPU/processor testing. Learn more via the OpenBenchmarking.org test page.
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 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.27, N = 3 SE +/- 0.20, N = 3 101.98 102.92 1. (CXX) g++ options: -O3
Laghos Laghos (LAGrangian High-Order Solver) is a miniapp that solves the time-dependent Euler equations of compressible gas dynamics in a moving Lagrangian frame using unstructured high-order finite element spatial discretization and explicit high-order time-stepping. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Major Kernels Total Rate, More Is Better Laghos 3.1 Test: Triple Point Problem 0xd000390 0xd0003a5 60 120 180 240 300 SE +/- 0.32, N = 3 SE +/- 0.94, N = 3 256.27 256.87 1. (CXX) g++ options: -O3 -std=c++11 -lmfem -lHYPRE -lmetis -lrt -lmpi_cxx -lmpi
Palabos The Palabos library is a framework for general purpose Computational Fluid Dynamics (CFD). Palabos uses a kernel based on the Lattice Boltzmann method. This test profile uses the Palabos MPI-based Cavity3D benchmark. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Mega Site Updates Per Second, More Is Better Palabos 2.3 Grid Size: 100 0xd000390 0xd0003a5 70 140 210 280 350 SE +/- 0.89, N = 3 SE +/- 0.20, N = 3 312.20 312.53 1. (CXX) g++ options: -std=c++17 -pedantic -O3 -rdynamic -lcrypto -lcurl -lsz -lz -ldl -lm
libxsmm Libxsmm is an open-source library for specialized dense and sparse matrix operations and deep learning primitives. Libxsmm supports making use of Intel AMX, AVX-512, and other modern CPU instruction set capabilities. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org GFLOPS/s, More Is Better libxsmm 2-1.17-3645 M N K: 32 0xd000390 0xd0003a5 130 260 390 520 650 SE +/- 8.71, N = 15 SE +/- 4.92, N = 3 604.7 609.2 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
OpenBenchmarking.org GFLOPS/s, More Is Better libxsmm 2-1.17-3645 M N K: 64 0xd000390 0xd0003a5 300 600 900 1200 1500 SE +/- 8.60, N = 3 SE +/- 13.09, N = 15 1098.8 1177.1 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
OpenBenchmarking.org GFLOPS/s, More Is Better libxsmm 2-1.17-3645 M N K: 256 0xd000390 0xd0003a5 130 260 390 520 650 SE +/- 2.33, N = 3 SE +/- 2.54, N = 3 594.6 600.2 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
OpenBenchmarking.org GFLOPS/s, More Is Better libxsmm 2-1.17-3645 M N K: 128 0xd000390 0xd0003a5 400 800 1200 1600 2000 SE +/- 32.53, N = 7 SE +/- 20.92, N = 3 1941.1 1978.9 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 MiniBUDE is a mini application for the the core computation of the Bristol University Docking Engine (BUDE). This test profile currently makes use of the OpenMP implementation of miniBUDE for CPU benchmarking. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Billion Interactions/s, More Is Better miniBUDE 20210901 Implementation: OpenMP - Input Deck: BM1 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.14, N = 3 SE +/- 0.44, N = 3 94.14 94.90 1. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm
HeFFTe - Highly Efficient FFT for Exascale HeFFTe is the Highly Efficient FFT for Exascale software developed as part of the Exascale Computing Project. This test profile uses HeFFTe's built-in speed benchmarks under a variety of configuration options and currently catering to CPU/processor testing. Learn more via the OpenBenchmarking.org test page.
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 0xd000390 0xd0003a5 30 60 90 120 150 SE +/- 1.81, N = 4 SE +/- 1.71, N = 3 144.94 153.79 1. (CXX) g++ options: -O3
Laghos Laghos (LAGrangian High-Order Solver) is a miniapp that solves the time-dependent Euler equations of compressible gas dynamics in a moving Lagrangian frame using unstructured high-order finite element spatial discretization and explicit high-order time-stepping. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Major Kernels Total Rate, More Is Better Laghos 3.1 Test: Sedov Blast Wave, ube_922_hex.mesh 0xd000390 0xd0003a5 80 160 240 320 400 SE +/- 0.23, N = 3 SE +/- 0.80, N = 3 385.89 386.08 1. (CXX) g++ options: -O3 -std=c++11 -lmfem -lHYPRE -lmetis -lrt -lmpi_cxx -lmpi
HeFFTe - Highly Efficient FFT for Exascale HeFFTe is the Highly Efficient FFT for Exascale software developed as part of the Exascale Computing Project. This test profile uses HeFFTe's built-in speed benchmarks under a variety of configuration options and currently catering to CPU/processor testing. Learn more via the OpenBenchmarking.org test page.
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 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 1.09, N = 3 SE +/- 1.13, N = 3 93.75 94.86 1. (CXX) g++ options: -O3
Palabos The Palabos library is a framework for general purpose Computational Fluid Dynamics (CFD). Palabos uses a kernel based on the Lattice Boltzmann method. This test profile uses the Palabos MPI-based Cavity3D benchmark. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Mega Site Updates Per Second, More Is Better Palabos 2.3 Grid Size: 400 0xd000390 0xd0003a5 90 180 270 360 450 SE +/- 0.94, N = 3 SE +/- 0.06, N = 3 388.48 393.84 1. (CXX) g++ options: -std=c++17 -pedantic -O3 -rdynamic -lcrypto -lcurl -lsz -lz -ldl -lm
SPECFEM3D simulates acoustic (fluid), elastic (solid), coupled acoustic/elastic, poroelastic or seismic wave propagation in any type of conforming mesh of hexahedra. This test profile currently relies on CPU-based execution for SPECFEM3D and using a variety of their built-in examples/models for benchmarking. Learn more via the OpenBenchmarking.org test page.
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 MiniBUDE is a mini application for the the core computation of the Bristol University Docking Engine (BUDE). This test profile currently makes use of the OpenMP implementation of miniBUDE for CPU benchmarking. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org GFInst/s, More Is Better miniBUDE 20210901 Implementation: OpenMP - Input Deck: BM2 0xd000390 0xd0003a5 600 1200 1800 2400 3000 SE +/- 9.78, N = 3 SE +/- 9.53, N = 3 2526.89 2601.21 1. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm
SPECFEM3D simulates acoustic (fluid), elastic (solid), coupled acoustic/elastic, poroelastic or seismic wave propagation in any type of conforming mesh of hexahedra. This test profile currently relies on CPU-based execution for SPECFEM3D and using a variety of their built-in examples/models for benchmarking. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Seconds, Fewer Is Better SPECFEM3D 4.0 Model: Tomographic Model 0xd000390 0xd0003a5 4 8 12 16 20 SE +/- 0.02, N = 3 SE +/- 0.15, N = 3 14.57 14.15 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
OpenBenchmarking.org Seconds, Fewer Is Better SPECFEM3D 4.0 Model: Layered Halfspace 0xd000390 0xd0003a5 7 14 21 28 35 SE +/- 0.05, N = 3 SE +/- 0.18, N = 3 29.50 29.37 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
OpenBenchmarking.org Seconds, Fewer Is Better SPECFEM3D 4.0 Model: Mount St. Helens 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.18, N = 3 SE +/- 0.12, N = 3 13.15 12.95 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 MiniBUDE is a mini application for the the core computation of the Bristol University Docking Engine (BUDE). This test profile currently makes use of the OpenMP implementation of miniBUDE for CPU benchmarking. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Billion Interactions/s, More Is Better miniBUDE 20210901 Implementation: OpenMP - Input Deck: BM2 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.39, N = 3 SE +/- 0.38, N = 3 101.08 104.05 1. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm
Timed MrBayes Analysis This test performs a bayesian analysis of a set of primate genome sequences in order to estimate their phylogeny. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Seconds, Fewer Is Better Timed MrBayes Analysis 3.2.7 Primate Phylogeny Analysis 0xd000390 0xd0003a5 40 80 120 160 200 SE +/- 1.36, N = 3 SE +/- 0.98, N = 3 166.53 165.42 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 This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: AlexNet 0xd000390 0xd0003a5 200 400 600 800 1000 SE +/- 2.99, N = 3 SE +/- 4.27, N = 3 723.27 781.25
OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: AlexNet 0xd000390 0xd0003a5 200 400 600 800 1000 SE +/- 3.77, N = 3 SE +/- 2.61, N = 3 760.15 839.41
Remhos Remhos (REMap High-Order Solver) is a miniapp that solves the pure advection equations that are used to perform monotonic and conservative discontinuous field interpolation (remap) as part of the Eulerian phase in Arbitrary Lagrangian Eulerian (ALE) simulations. Learn more via the OpenBenchmarking.org test page.
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 This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: GoogLeNet 0xd000390 0xd0003a5 70 140 210 280 350 SE +/- 1.89, N = 3 SE +/- 2.10, N = 3 309.63 321.72
OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: ResNet-50 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.37, N = 3 SE +/- 0.80, N = 9 83.89 86.93
OpenBenchmarking.org images/sec, More Is Better TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: GoogLeNet 0xd000390 0xd0003a5 70 140 210 280 350 SE +/- 0.87, N = 3 SE +/- 0.43, N = 3 317.27 323.79
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 CloverLeaf is a Lagrangian-Eulerian hydrodynamics benchmark. This test profile currently makes use of CloverLeaf's OpenMP version and benchmarked with the clover_bm.in input file (Problem 5). Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Seconds, Fewer Is Better CloverLeaf Lagrangian-Eulerian Hydrodynamics 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.06, N = 3 SE +/- 0.09, N = 3 12.04 11.98 1. (F9X) gfortran options: -O3 -march=native -funroll-loops -fopenmp
Neural Magic DeepSparse 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
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
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
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
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 0xd000390 0xd0003a5 200 400 600 800 1000 SE +/- 11.47, N = 3 SE +/- 1.22, N = 3 922.93 930.75
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 0xd000390 0xd0003a5 10 20 30 40 50 SE +/- 0.53, N = 3 SE +/- 0.06, N = 3 43.30 42.94
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 0xd000390 0xd0003a5 50 100 150 200 250 SE +/- 0.46, N = 3 SE +/- 2.37, N = 3 230.05 231.43
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 0xd000390 0xd0003a5 40 80 120 160 200 SE +/- 0.34, N = 3 SE +/- 1.64, N = 3 173.79 172.56
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
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
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
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
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
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
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
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
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
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
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 ONNX Runtime is developed by Microsoft and partners as a open-source, cross-platform, high performance machine learning inferencing and training accelerator. This test profile runs the ONNX Runtime with various models available from the ONNX Model Zoo. Learn more via the OpenBenchmarking.org test page.
NCNN NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.
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
OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU-v2-v2 - Model: mobilenet-v2 0xd000390 0xd0003a5 2 4 6 8 10 SE +/- 0.09, N = 3 SE +/- 0.04, N = 3 8.03 7.96 MIN: 7.8 / MAX: 31.34 MIN: 7.75 / MAX: 31.75 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU-v3-v3 - Model: mobilenet-v3 0xd000390 0xd0003a5 2 4 6 8 10 SE +/- 0.05, N = 3 SE +/- 0.12, N = 3 8.88 8.76 MIN: 8.69 / MAX: 32.33 MIN: 8.3 / MAX: 32.6 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: shufflenet-v2 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.10, N = 3 SE +/- 0.13, N = 3 9.89 9.76 MIN: 9.61 / MAX: 33.59 MIN: 9.32 / MAX: 33.4 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: mnasnet 0xd000390 0xd0003a5 2 4 6 8 10 SE +/- 0.10, N = 3 SE +/- 0.04, N = 3 7.57 7.41 MIN: 7.28 / MAX: 30.26 MIN: 7.15 / MAX: 31.17 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
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
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
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
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
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
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
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
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
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
OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: regnety_400m 0xd000390 0xd0003a5 10 20 30 40 50 SE +/- 7.69, N = 3 SE +/- 0.50, N = 3 45.54 38.85 MIN: 36.01 / MAX: 3343.68 MIN: 37.13 / MAX: 233.44 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: vision_transformer 0xd000390 0xd0003a5 11 22 33 44 55 SE +/- 1.17, N = 3 SE +/- 0.31, N = 3 46.92 45.23 MIN: 43.11 / MAX: 881.49 MIN: 43.43 / MAX: 73.39 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
OpenBenchmarking.org ms, Fewer Is Better NCNN 20230517 Target: CPU - Model: FastestDet 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.64, N = 3 SE +/- 0.07, N = 3 10.20 9.71 MIN: 9.01 / MAX: 500.17 MIN: 9.22 / MAX: 27.29 1. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
GROMACS The GROMACS (GROningen MAchine for Chemical Simulations) molecular dynamics package testing with the water_GMX50 data. This test profile allows selecting between CPU and GPU-based GROMACS builds. Learn more via the OpenBenchmarking.org test page.
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 This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the Intel oneAPI toolkit. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.1 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU 0xd000390 0xd0003a5 0.5834 1.1668 1.7502 2.3336 2.917 SE +/- 0.03265, N = 15 SE +/- 0.02489, N = 15 2.59271 2.57437 MIN: 1.91 MIN: 1.99 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
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
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
OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.1 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU 0xd000390 0xd0003a5 0.8157 1.6314 2.4471 3.2628 4.0785 SE +/- 0.00896, N = 3 SE +/- 0.00622, N = 3 3.62526 3.62266 MIN: 3.54 MIN: 3.53 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.1 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU 0xd000390 0xd0003a5 200 400 600 800 1000 SE +/- 16.59, N = 15 SE +/- 14.20, N = 12 832.45 816.51 MIN: 714.88 MIN: 723.44 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
OpenBenchmarking.org ms, Fewer Is Better oneDNN 3.1 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU 0xd000390 0xd0003a5 110 220 330 440 550 SE +/- 7.19, N = 3 SE +/- 2.82, N = 3 524.38 521.74 MIN: 499.75 MIN: 505.72 1. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread
OpenVINO This is a test of the Intel OpenVINO, a toolkit around neural networks, using its built-in benchmarking support and analyzing the throughput and latency for various models. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Face Detection FP16 - Device: CPU 0xd000390 0xd0003a5 6 12 18 24 30 SE +/- 0.01, N = 3 SE +/- 0.02, N = 3 24.04 24.18 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
OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Face Detection FP16 - Device: CPU 0xd000390 0xd0003a5 200 400 600 800 1000 SE +/- 0.42, N = 3 SE +/- 0.71, N = 3 827.51 823.09 MIN: 628.21 / MAX: 980.48 MIN: 550.41 / MAX: 926.21 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
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
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
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Person Detection FP32 - Device: CPU 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.00, N = 3 SE +/- 0.01, N = 3 13.03 13.04 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
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
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
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
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Face Detection FP16-INT8 - Device: CPU 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.01, N = 3 SE +/- 0.07, N = 3 95.42 95.54 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
OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Face Detection FP16-INT8 - Device: CPU 0xd000390 0xd0003a5 50 100 150 200 250 SE +/- 0.03, N = 3 SE +/- 0.14, N = 3 209.31 209.02 MIN: 160.46 / MAX: 249.09 MIN: 152.83 / MAX: 234.28 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
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Vehicle Detection FP16-INT8 - Device: CPU 0xd000390 0xd0003a5 1000 2000 3000 4000 5000 SE +/- 3.29, N = 3 SE +/- 2.64, N = 3 4419.17 4442.98 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
OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Vehicle Detection FP16-INT8 - Device: CPU 0xd000390 0xd0003a5 1.0148 2.0296 3.0444 4.0592 5.074 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 4.51 4.49 MIN: 4.02 / MAX: 13.95 MIN: 4.04 / MAX: 15.85 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
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Weld Porosity Detection FP16 - Device: CPU 0xd000390 0xd0003a5 500 1000 1500 2000 2500 SE +/- 3.01, N = 3 SE +/- 3.91, N = 3 2344.97 2362.84 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
OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Weld Porosity Detection FP16 - Device: CPU 0xd000390 0xd0003a5 8 16 24 32 40 SE +/- 0.05, N = 3 SE +/- 0.06, N = 3 33.89 33.63 MIN: 29.83 / MAX: 113.13 MIN: 29.54 / MAX: 113.55 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
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Machine Translation EN To DE FP16 - Device: CPU 0xd000390 0xd0003a5 60 120 180 240 300 SE +/- 0.68, N = 3 SE +/- 0.63, N = 3 251.00 255.09 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
OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Machine Translation EN To DE FP16 - Device: CPU 0xd000390 0xd0003a5 20 40 60 80 100 SE +/- 0.22, N = 3 SE +/- 0.19, N = 3 79.47 78.18 MIN: 62.57 / MAX: 232.01 MIN: 66.08 / MAX: 194.38 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
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Weld Porosity Detection FP16-INT8 - Device: CPU 0xd000390 0xd0003a5 2K 4K 6K 8K 10K SE +/- 3.74, N = 3 SE +/- 2.19, N = 3 9396.52 9419.77 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
OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Weld Porosity Detection FP16-INT8 - Device: CPU 0xd000390 0xd0003a5 2 4 6 8 10 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 8.50 8.48 MIN: 7.17 / MAX: 18.39 MIN: 7.15 / MAX: 22.53 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
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Person Vehicle Bike Detection FP16 - Device: CPU 0xd000390 0xd0003a5 400 800 1200 1600 2000 SE +/- 1.77, N = 3 SE +/- 3.75, N = 3 2039.63 2070.72 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
OpenBenchmarking.org ms, Fewer Is Better OpenVINO 2022.3 Model: Person Vehicle Bike Detection FP16 - Device: CPU 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.01, N = 3 SE +/- 0.02, N = 3 9.77 9.63 MIN: 7.83 / MAX: 20.01 MIN: 8.33 / MAX: 19.33 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
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU 0xd000390 0xd0003a5 13K 26K 39K 52K 65K SE +/- 17.38, N = 3 SE +/- 18.68, N = 3 59274.06 59377.96 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
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
OpenBenchmarking.org FPS, More Is Better OpenVINO 2022.3 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU 0xd000390 0xd0003a5 15K 30K 45K 60K 75K SE +/- 17.48, N = 3 SE +/- 29.72, N = 3 67604.00 67754.34 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
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 QMCPACK is a modern high-performance open-source Quantum Monte Carlo (QMC) simulation code making use of MPI for this benchmark of the H20 example code. QMCPACK is an open-source production level many-body ab initio Quantum Monte Carlo code for computing the electronic structure of atoms, molecules, and solids. QMCPACK is supported by the U.S. Department of Energy. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Total Execution Time - Seconds, Fewer Is Better QMCPACK 3.16 Input: Li2_STO_ae 0xd000390 0xd0003a5 30 60 90 120 150 SE +/- 1.55, N = 3 SE +/- 1.03, N = 3 124.23 123.26 1. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl
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
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
OpenBenchmarking.org Total Execution Time - Seconds, Fewer Is Better QMCPACK 3.16 Input: FeCO6_b3lyp_gms 0xd000390 0xd0003a5 60 120 180 240 300 SE +/- 3.60, N = 3 SE +/- 2.19, N = 3 268.56 263.23 1. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl
Xcompact3d Incompact3d Xcompact3d Incompact3d is a Fortran-MPI based, finite difference high-performance code for solving the incompressible Navier-Stokes equation and as many as you need scalar transport equations. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org Seconds, Fewer Is Better Xcompact3d Incompact3d 2021-03-11 Input: input.i3d 193 Cells Per Direction 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.02, N = 3 SE +/- 0.02, N = 3 11.02 11.00 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 Cpuminer-Opt is a fork of cpuminer-multi that carries a wide range of CPU performance optimizations for measuring the potential cryptocurrency mining performance of the CPU/processor with a wide variety of cryptocurrencies. The benchmark reports the hash speed for the CPU mining performance for the selected cryptocurrency. Learn more via the OpenBenchmarking.org test page.
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
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: x25x 0xd000390 0xd0003a5 600 1200 1800 2400 3000 SE +/- 4.75, N = 3 SE +/- 5.54, N = 3 2659.17 2659.55 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: scrypt 0xd000390 0xd0003a5 500 1000 1500 2000 2500 SE +/- 1.08, N = 3 SE +/- 8.35, N = 3 2319.31 2321.74 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Deepcoin 0xd000390 0xd0003a5 14K 28K 42K 56K 70K SE +/- 89.69, N = 3 SE +/- 187.02, N = 3 64677 64897 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Blake-2 S 0xd000390 0xd0003a5 1000K 2000K 3000K 4000K 5000K SE +/- 8325.80, N = 3 SE +/- 8676.85, N = 3 4462327 4466653 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
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
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Skeincoin 0xd000390 0xd0003a5 130K 260K 390K 520K 650K SE +/- 1652.89, N = 3 SE +/- 3788.20, N = 3 613333 617130 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Myriad-Groestl 0xd000390 0xd0003a5 9K 18K 27K 36K 45K SE +/- 386.00, N = 15 SE +/- 406.32, N = 3 43127 43450 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: LBC, LBRY Credits 0xd000390 0xd0003a5 90K 180K 270K 360K 450K SE +/- 313.42, N = 3 SE +/- 860.95, N = 3 421660 423130 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Quad SHA-256, Pyrite 0xd000390 0xd0003a5 200K 400K 600K 800K 1000K SE +/- 3352.41, N = 3 SE +/- 1690.96, N = 3 921730 926277 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
OpenBenchmarking.org kH/s, More Is Better Cpuminer-Opt 3.20.3 Algorithm: Triple SHA-256, Onecoin 0xd000390 0xd0003a5 300K 600K 900K 1200K 1500K SE +/- 7105.40, N = 3 SE +/- 7235.75, N = 3 1332237 1333117 1. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp
dav1d Dav1d is an open-source, speedy AV1 video decoder supporting modern SIMD CPU features. This test profile times how long it takes to decode sample AV1 video content. Learn more via the OpenBenchmarking.org test page.
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
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 This is a benchmark of the SVT-AV1 open-source video encoder/decoder. SVT-AV1 was originally developed by Intel as part of their Open Visual Cloud / Scalable Video Technology (SVT). Development of SVT-AV1 has since moved to the Alliance for Open Media as part of upstream AV1 development. SVT-AV1 is a CPU-based multi-threaded video encoder for the AV1 video format with a sample YUV video file. Learn more via the OpenBenchmarking.org test page.
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
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
OpenBenchmarking.org Frames Per Second, More Is Better SVT-AV1 1.6 Encoder Mode: Preset 13 - Input: Bosphorus 4K 0xd000390 0xd0003a5 40 80 120 160 200 SE +/- 0.79, N = 3 SE +/- 2.02, N = 3 175.10 177.20 1. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq
SVT-HEVC This is a test of the Intel Open Visual Cloud Scalable Video Technology SVT-HEVC CPU-based multi-threaded video encoder for the HEVC / H.265 video format with a sample YUV video file. Learn more via the OpenBenchmarking.org test page.
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
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
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 OpenBenchmarking.org Seconds, Fewer Is Better Blender 3.6 Blend File: BMW27 - Compute: CPU-Only 0xd000390 0xd0003a5 6 12 18 24 30 SE +/- 0.06, N = 3 SE +/- 0.03, N = 3 23.83 23.72
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 VVenC is the Fraunhofer Versatile Video Encoder as a fast/efficient H.266/VVC encoder. The vvenc encoder makes use of SIMD Everywhere (SIMDe). The vvenc software is published under the Clear BSD License. Learn more via the OpenBenchmarking.org test page.
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
OpenBenchmarking.org Frames Per Second, More Is Better VVenC 1.9 Video Input: Bosphorus 4K - Video Preset: Faster 0xd000390 0xd0003a5 3 6 9 12 15 SE +/- 0.09, N = 3 SE +/- 0.06, N = 3 10.36 10.42 1. (CXX) g++ options: -O3 -flto=auto -fno-fat-lto-objects
Embree Intel Embree is a collection of high-performance ray-tracing kernels for execution on CPUs (and GPUs via SYCL) and supporting instruction sets such as SSE, AVX, AVX2, and AVX-512. Embree also supports making use of the Intel SPMD Program Compiler (ISPC). Learn more via the OpenBenchmarking.org test page.
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
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
OSPRay Intel OSPRay is a portable ray-tracing engine for high-performance, high-fidelity scientific visualizations. OSPRay builds off Intel's Embree and Intel SPMD Program Compiler (ISPC) components as part of the oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.
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
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
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
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
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 This is a benchmark of SIMDJSON, a high performance JSON parser. SIMDJSON aims to be the fastest JSON parser and is used by projects like Microsoft FishStore, Yandex ClickHouse, Shopify, and others. Learn more via the OpenBenchmarking.org test page.
OpenBenchmarking.org GB/s, More Is Better simdjson 2.0 Throughput Test: Kostya 0xd000390 0xd0003a5 0.6458 1.2916 1.9374 2.5832 3.229 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 2.61 2.87 1. (CXX) g++ options: -O3
OpenBenchmarking.org GB/s, More Is Better simdjson 2.0 Throughput Test: TopTweet 0xd000390 0xd0003a5 1.2938 2.5876 3.8814 5.1752 6.469 SE +/- 0.03, N = 3 SE +/- 0.01, N = 3 5.60 5.75 1. (CXX) g++ options: -O3
OpenBenchmarking.org GB/s, More Is Better simdjson 2.0 Throughput Test: LargeRandom 0xd000390 0xd0003a5 0.216 0.432 0.648 0.864 1.08 SE +/- 0.00, N = 3 SE +/- 0.00, N = 3 0.85 0.96 1. (CXX) g++ options: -O3
OpenBenchmarking.org GB/s, More Is Better simdjson 2.0 Throughput Test: PartialTweets 0xd000390 0xd0003a5 1.0733 2.1466 3.2199 4.2932 5.3665 SE +/- 0.01, N = 3 SE +/- 0.01, N = 3 4.62 4.77 1. (CXX) g++ options: -O3
OpenBenchmarking.org GB/s, More Is Better simdjson 2.0 Throughput Test: DistinctUserID 0xd000390 0xd0003a5 1.2848 2.5696 3.8544 5.1392 6.424 SE +/- 0.02, N = 3 SE +/- 0.00, N = 3 5.52 5.71 1. (CXX) g++ options: -O3
0xd000390 Processor: 2 x Intel Xeon Platinum 8380 @ 3.40GHz (80 Cores / 160 Threads), Motherboard: Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS), Chipset: Intel Ice Lake IEH, Memory: 512GB, Disk: 7682GB INTEL SSDPF2KX076TZ, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel X710 for 10GBASE-T + 2 x Intel E810-C for QSFP
OS: Ubuntu 22.10, Kernel: 6.5.0-060500rc4daily20230804-generic (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080
Kernel Notes: Transparent Huge Pages: madviseCompiler Notes: --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 -vProcessor Notes: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0xd000390Python Notes: Python 3.10.7Security Notes: 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
Testing initiated at 6 August 2023 17:43 by user phoronix.
0xd0003a5 Processor: 2 x Intel Xeon Platinum 8380 @ 3.40GHz (80 Cores / 160 Threads), Motherboard: Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS), Chipset: Intel Ice Lake IEH, Memory: 512GB, Disk: 7682GB INTEL SSDPF2KX076TZ, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel X710 for 10GBASE-T + 2 x Intel E810-C for QSFP
OS: Ubuntu 22.10, Kernel: 6.5.0-rc5-phx-tues (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080
Kernel Notes: Transparent Huge Pages: madviseCompiler Notes: --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 -vProcessor Notes: Scaling Governor: intel_pstate performance (EPP: performance) - CPU Microcode: 0xd0003a5Python Notes: Python 3.10.7Security Notes: 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
Testing initiated at 8 August 2023 14:24 by user phoronix.