7950x3d

AMD Ryzen 9 7950X3D 16-Core testing with a ASRockRack B650D4U-2L2T/BCM (2.09 BIOS) and ASPEED 512MB on Ubuntu 22.04 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2311220-NE-7950X3D3814
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a
November 21 2023
  3 Hours, 55 Minutes
b
November 21 2023
  9 Hours, 1 Minute
c
November 22 2023
  3 Hours, 55 Minutes
d
November 22 2023
  3 Hours, 55 Minutes
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  5 Hours, 11 Minutes

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7950x3dOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 9 7950X3D 16-Core @ 5.76GHz (16 Cores / 32 Threads)ASRockRack B650D4U-2L2T/BCM (2.09 BIOS)AMD Device 14d82 x 32 GB DDR5-4800MT/s MTC20C2085S1EC48BA13201GB Micron_7450_MTFDKCC3T2TFS + 0GB Virtual HDisk0 + 0GB Virtual HDisk1 + 0GB Virtual HDisk2 + 0GB Virtual HDisk3ASPEED 512MBAMD Device 1640VA24312 x Intel I210 + 2 x Broadcom BCM57416 NetXtreme-E Dual-Media 10G RDMAUbuntu 22.046.6.0-rc4-phx-amd-pref-core (x86_64)GNOME Shell 42.9X Server1.3.238GCC 11.4.0ext41920x1200ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerVulkanCompilerFile-SystemScreen Resolution7950x3d BenchmarksSystem Logs- Transparent Huge Pages: madvise- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-11-XeT9lY/gcc-11-11.4.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-11-XeT9lY/gcc-11-11.4.0/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-build-config=bootstrap-lto-lean --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 - Scaling Governor: amd-pstate-epp performance (EPP: performance) - CPU Microcode: 0xa601203- OpenJDK Runtime Environment (build 11.0.20+8-post-Ubuntu-1ubuntu122.04)- Python 3.10.12- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_rstack_overflow: Mitigation of safe RET no microcode + 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 STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

abcdResult OverviewPhoronix Test Suite100%101%101%102%102%easyWaveTimed Gem5 CompilationWebP2 Image EncodePyTorchQuantLibQMCPACKTimed FFmpeg CompilationoneDNNCpuminer-OptFFmpegCloverLeafOpenVKLEmbreeOpenSSLOpenVINODaCapo BenchmarkOSPRay StudioIntel Open Image Denoise

7950x3donednn: IP Shapes 3D - u8s8f32 - CPUdacapobench: Avrora AVR Simulation Frameworkdacapobench: Spring Bootpytorch: CPU - 32 - ResNet-50dacapobench: Apache Lucene Search Indexdacapobench: PMD Source Code Analyzeronednn: IP Shapes 3D - bf16bf16bf16 - CPUwebp2: Defaultdacapobench: H2O In-Memory Platform For Machine Learningpytorch: CPU - 256 - ResNet-50dacapobench: Jythondacapobench: Tradesoapqmcpack: H4_aedacapobench: GraphChipytorch: CPU - 64 - ResNet-50pytorch: CPU - 16 - ResNet-50openvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUcpuminer-opt: Garlicoinpytorch: CPU - 1 - ResNet-152pytorch: CPU - 16 - Efficientnet_v2_lwebp2: Quality 75, Compression Effort 7dacapobench: BioJava Biological Data Frameworkcpuminer-opt: Ringcoineasywave: e2Asean Grid + BengkuluSept2007 Source - 240ffmpeg: libx264 - Livedacapobench: Apache Lucene Search Enginepytorch: CPU - 512 - ResNet-152onednn: Recurrent Neural Network Inference - f32 - CPUdacapobench: Tradebeansonednn: IP Shapes 1D - f32 - CPUpytorch: CPU - 64 - ResNet-152qmcpack: O_ae_pyscf_UHFdacapobench: Eclipseopenvino: Road Segmentation ADAS FP16-INT8 - CPUopenvino: Road Segmentation ADAS FP16-INT8 - CPUpytorch: CPU - 1 - ResNet-50easywave: e2Asean Grid + BengkuluSept2007 Source - 1200onednn: Recurrent Neural Network Inference - u8s8f32 - CPUpytorch: CPU - 512 - Efficientnet_v2_lbuild-gem5: Time To Compilepytorch: CPU - 1 - Efficientnet_v2_ldacapobench: Batik SVG Toolkitdacapobench: Zxing 1D/2D Barcode Image Processingdacapobench: Apache Cassandraquantlib: Single-Threadedffmpeg: libx264 - Uploadonednn: Convolution Batch Shapes Auto - u8s8f32 - CPUospray-studio: 1 - 4K - 16 - Path Tracer - CPUopenvino: Handwritten English Recognition FP16-INT8 - CPUpytorch: CPU - 512 - ResNet-50openvino: Handwritten English Recognition FP16-INT8 - CPUpytorch: CPU - 256 - Efficientnet_v2_lpytorch: CPU - 256 - ResNet-152openvino: Person Vehicle Bike Detection FP16 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUdacapobench: Apache Xalan XSLTospray-studio: 3 - 1080p - 32 - Path Tracer - CPUonednn: Recurrent Neural Network Inference - bf16bf16bf16 - CPUopenvino: Handwritten English Recognition FP16 - CPUopenvino: Handwritten English Recognition FP16 - CPUopenvino: Person Detection FP16 - CPUopenvino: Person Detection FP16 - CPUopenvino: Road Segmentation ADAS FP16 - CPUopenvino: Road Segmentation ADAS FP16 - CPUqmcpack: simple-H2Odacapobench: H2 Database Engineonednn: Recurrent Neural Network Training - u8s8f32 - CPUpytorch: CPU - 32 - Efficientnet_v2_lpytorch: CPU - 64 - Efficientnet_v2_lopenvino: Person Detection FP32 - CPUopenvino: Person Detection FP32 - CPUopenssl: SHA256cpuminer-opt: Myriad-Groestlqmcpack: LiH_ae_MSDonednn: Deconvolution Batch shapes_1d - f32 - CPUcloverleaf: clover_bmffmpeg: libx265 - Video On Demandospray-studio: 2 - 1080p - 32 - Path Tracer - CPUonednn: Recurrent Neural Network Training - f32 - CPUembree: Pathtracer ISPC - Crownopenvino: Machine Translation EN To DE FP16 - CPUonednn: Convolution Batch Shapes Auto - f32 - CPUcpuminer-opt: Blake-2 Sffmpeg: libx264 - Video On Demandopenssl: RSA4096cpuminer-opt: Magiopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Face Detection Retail FP16-INT8 - CPUqmcpack: Li2_STO_aeospray-studio: 1 - 4K - 1 - Path Tracer - CPUonednn: Recurrent Neural Network Training - bf16bf16bf16 - CPUpytorch: CPU - 16 - ResNet-152ospray-studio: 1 - 1080p - 32 - Path Tracer - CPUospray-studio: 2 - 4K - 1 - Path Tracer - CPUonednn: IP Shapes 3D - f32 - CPUopenvino: Face Detection FP16 - CPUopenvino: Face Detection Retail FP16-INT8 - CPUpytorch: CPU - 32 - ResNet-152build-ffmpeg: Time To Compileospray-studio: 1 - 4K - 32 - Path Tracer - CPUffmpeg: libx265 - Platformospray-studio: 2 - 4K - 16 - Path Tracer - CPUonednn: Convolution Batch Shapes Auto - bf16bf16bf16 - CPUospray-studio: 2 - 1080p - 16 - Path Tracer - CPUospray-studio: 3 - 4K - 16 - Path Tracer - CPUffmpeg: libx264 - Platformcpuminer-opt: LBC, LBRY Creditsospray-studio: 3 - 1080p - 16 - Path Tracer - CPUffmpeg: libx265 - Liveopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUopenvkl: vklBenchmarkCPU Scalardacapobench: Apache Tomcatopenvino: Vehicle Detection FP16-INT8 - CPUqmcpack: FeCO6_b3lyp_gmsonednn: Deconvolution Batch shapes_3d - bf16bf16bf16 - CPUopenvino: Face Detection Retail FP16 - CPUembree: Pathtracer - Crownospray-studio: 3 - 4K - 32 - Path Tracer - CPUembree: Pathtracer ISPC - Asian Dragonopenvino: Face Detection FP16 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUonednn: Deconvolution Batch shapes_3d - u8s8f32 - CPUembree: Pathtracer ISPC - Asian Dragon Objembree: Pathtracer - Asian Dragonwebp2: Quality 100, Compression Effort 5ospray-studio: 3 - 4K - 1 - Path Tracer - CPUffmpeg: libx265 - Uploadonednn: IP Shapes 1D - bf16bf16bf16 - CPUcpuminer-opt: scryptospray-studio: 2 - 4K - 32 - Path Tracer - CPUopenssl: ChaCha20openvino: Vehicle Detection FP16 - CPUopenvino: Face Detection FP16-INT8 - CPUquantlib: Multi-Threadedospray-studio: 1 - 1080p - 16 - Path Tracer - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUospray-studio: 3 - 1080p - 1 - Path Tracer - CPUopenvino: Face Detection Retail FP16 - CPUopenvino: Face Detection FP16-INT8 - CPUembree: Pathtracer - Asian Dragon Objcpuminer-opt: Deepcoinopenvino: Vehicle Detection FP16 - CPUopenvino: Weld Porosity Detection FP16 - CPUospray-studio: 2 - 1080p - 1 - Path Tracer - CPUonednn: Deconvolution Batch shapes_1d - u8s8f32 - CPUonednn: Deconvolution Batch shapes_1d - bf16bf16bf16 - CPUopenvino: Weld Porosity Detection FP16 - CPUopenssl: RSA4096openvkl: vklBenchmarkCPU ISPCopenvino: Weld Porosity Detection FP16-INT8 - CPUcloverleaf: clover_bm16dacapobench: Apache Kafkaopenssl: ChaCha20-Poly1305openssl: SHA512cpuminer-opt: Quad SHA-256, Pyritecpuminer-opt: Skeincoinospray-studio: 1 - 1080p - 1 - Path Tracer - CPUopenvino: Age Gender Recognition Retail 0013 FP16 - CPUdacapobench: jMonkeyEngineopenssl: AES-128-GCMopenssl: AES-256-GCMcpuminer-opt: Triple SHA-256, Onecoincloverleaf: clover_bm64_shortonednn: Deconvolution Batch shapes_3d - f32 - CPUopenvino: Age Gender Recognition Retail 0013 FP16 - CPUoidn: RTLightmap.hdr.4096x4096 - CPU-Onlyoidn: RT.ldr_alb_nrm.3840x2160 - CPU-Onlyoidn: RT.hdr_alb_nrm.3840x2160 - CPU-Onlywebp2: Quality 100, Lossless Compressionwebp2: Quality 95, Compression Effort 7onednn: IP Shapes 1D - u8s8f32 - CPUdacapobench: FOP Print Formatterabcd0.2629562548152344.97284212441.2371213.51259245.094258280812.16232845.4545.700.291796.7126.9910.620.3149463455.062.148282.96133118.30620.71439121.8937917.80132.777923524.5815.2369.4181.625633.9110.93284.1514.20110461254803980.316.903.7677971605584.146.3127.3610.8417.961572.385.0652644670632.072739.5621.6194.284.8318.16439.7518.23918551231.9310.8110.8684.9994.05326317087101144073.4682.9853631.8468.48388521227.5831.9195130.054.0178113466064.165501.2640.361.384527.76135.642511236.2918.133849943032.8018313.413.4418.0421.9113995668.39731121.09425172668439763.981585020148179.5547376.5924234324.92125.461.483852.4930.495816525333.7784595.551613.220.6475528.60830.93338.73502533.540.694296304.461415501254886520107.71313.0582133.5171162608.3912673070.7725.5227.57837978.241034.251353.9110830.4555312.3432911.79358677.56036.11496.78509589461502230106384699606208034440106933511.7968129896008144092480786980105880175.912.598960.430.390.810.810.030.150.5236594140.2791282801166045.98279811761.1855813.05259546.294147271412.51238347.0146.730.291783.7626.7310.860.3149353355.732.089282.92132017.81636.49738651.9408718.07131.367872522.6415.2870.3981.030635.30011.04283.21514.12109261655484042.616.673.7165072346585.5546.3627.2910.9318.111587.745.0252244437633.611731.4221.8593.9585.0718.32435.7718.43318531236.6810.8910.9384.4394.66329073949101149074.1023.0109931.7968.57387401229.8031.9653130.954.0341813417763.785508.4635.6660.984537.36135.9542671231.6818.023827943232.8173713.383.4318.0621.89514034268.36728151.09760173168466063.851578320226180.0647316.1724234244.91125.491.484962.530.448416503433.7272595.361617.790.64686628.707430.88748.73502733.530.693123304.621417731251274309607.73312.5382020.0171162604.1212663063.5525.5627.51667965.211032.481352.8910830.4563472.3412511.8359044.26036.111495.65509189582688390106358355306212734440106933483.5368159895833518092512692630105897175.922.598380.430.390.810.810.030.150.4987414170.2487882838162245.91268912071.1837213.38259146.164081269912.63241146.1045.940.31783.9427.0210.720.3247943367.452.101284.24129518.16632.39438241.9256617.75129.987747532.9414.9969.0580.873624.13810.85284.04314.01108660655544025.416.813.7278172590587.4746.4127.2110.8818.111591.84552844682628.308733.8121.7794.2984.7418.24437.7618.38718401236.2810.8710.8285.2793.76328331531901140073.6013.0090632.0268.14387981234.1431.7047130.064.0111413480064.145468.4636.5261.384512.34135.642391228.2518.033844843162.8037713.333.4418.0521.91814011968.29728881.09942172358478663.691579020186180.0247255.3624234384.9124.981.487522.530.391616462533.6516597.21617.820.64622328.644430.83168.72503533.460.695339305.221416981253385800407.73312.4881958170972609.5712653067.5225.5627.54037982.9410321353.6210830.456252.3392211.79358701.36046.11494.53509889478928250106490755006208034430106933482.5468119898771934092479315690105890175.922.598350.430.390.810.810.030.150.5345764550.2569622537165747.70268311861.1766813.71270947.064136277812.62238745.4345.160.291844.5927.6310.970.3149303350.862.09276.55130117.95625.50439201.9351818.19129.657773521.4615.3268.9980.098634.66310.94288.15914.2511016115570403316.643.7619271802579.5646.9327.5710.7918.191572.565.0652444177635.305733.3621.7994.9984.1418.36434.9818.29718591224.1110.9210.9084.7494.28329301274101139073.4882.9878532.0668.7139062122431.8535131.094.0431913520064.265500.8635.7960.944544.57135.0242511234.5718.023851242972.8187313.393.4218.1422.01213963168.63727621.09782172898457363.751578020139179.2947453.4324134374.9125.131.481532.4930.511216464533.7248594.981619.160.64523128.701930.82638.75504233.570.695171305.361413601252770638507.72313.2582159.4170752603.3512643069.3525.527.53167973.871033.331351.1710850.4555532.341111.81358447.16036.111496.30509489488403930106392313706205034470106833491.8368159901103049092479134650105860175.862.598860.430.390.810.810.030.150.520575430OpenBenchmarking.org

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.orgms, Fewer Is BetteroneDNN 3.3Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPUabcd0.06280.12560.18840.25120.314SE +/- 0.003205, N = 130.2629560.2791280.2487880.256962MIN: 0.25MIN: 0.24MIN: 0.24MIN: 0.241. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Avrora AVR Simulation Frameworkabcd6001200180024003000SE +/- 25.80, N = 152548280128382537

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Spring Bootabcd400800120016002000SE +/- 9.84, N = 31523166016221657

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-50abcd112233445544.9745.9845.9147.70MIN: 42.18 / MAX: 46.53MIN: 43.4 / MAX: 46.68MIN: 41.91 / MAX: 46.67MIN: 44.77 / MAX: 48.1

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Apache Lucene Search Indexabcd6001200180024003000SE +/- 6.81, N = 32842279826892683

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: PMD Source Code Analyzerabcd30060090012001500SE +/- 5.04, N = 31244117612071186

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.orgms, Fewer Is BetteroneDNN 3.3Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPUabcd0.27840.55680.83521.11361.392SE +/- 0.01251, N = 51.237121.185581.183721.17668MIN: 1.17MIN: 1.08MIN: 1.11MIN: 1.11. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

WebP2 Image Encode

This is a test of Google's libwebp2 library with the WebP2 image encode utility and using a sample 6000x4000 pixel JPEG image as the input, similar to the WebP/libwebp test profile. WebP2 is currently experimental and under heavy development as ultimately the successor to WebP. WebP2 supports 10-bit HDR, more efficienct lossy compression, improved lossless compression, animation support, and full multi-threading support compared to WebP. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Defaultabcd48121620SE +/- 0.18, N = 313.5113.0513.3813.711. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: H2O In-Memory Platform For Machine Learningabcd6001200180024003000SE +/- 1.45, N = 32592259525912709

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-50abcd112233445545.0946.2946.1647.06MIN: 43.25 / MAX: 45.61MIN: 42.39 / MAX: 46.81MIN: 42.11 / MAX: 46.89MIN: 42.87 / MAX: 47.87

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Jythonabcd9001800270036004500SE +/- 38.35, N = 34258414740814136

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Tradesoapabcd6001200180024003000SE +/- 19.80, N = 32808271426992778

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.orgTotal Execution Time - Seconds, Fewer Is BetterQMCPACK 3.17.1Input: H4_aeabcd3691215SE +/- 0.09, N = 1512.1612.5112.6312.621. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: GraphChiabcd5001000150020002500SE +/- 9.87, N = 32328238324112387

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-50abcd112233445545.4547.0146.1045.43MIN: 34.94 / MAX: 45.89MIN: 42.75 / MAX: 47.57MIN: 35.44 / MAX: 46.83MIN: 43.43 / MAX: 46.04

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-50abcd112233445545.7046.7345.9445.16MIN: 43.16 / MAX: 46.31MIN: 43.51 / MAX: 47.16MIN: 36.22 / MAX: 46.45MIN: 42.72 / MAX: 46.38

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.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUabcd0.06750.1350.20250.270.33750.290.290.300.29MIN: 0.17 / MAX: 7.87MIN: 0.17 / MAX: 7.59MIN: 0.17 / MAX: 7.08MIN: 0.17 / MAX: 7.661. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

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.orgkH/s, More Is BetterCpuminer-Opt 23.5Algorithm: Garlicoinabcd400800120016002000SE +/- 2.78, N = 31796.711783.761783.941844.591. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-152abcd71421283526.9926.7327.0227.63MIN: 25.92 / MAX: 27.21MIN: 25.73 / MAX: 27.4MIN: 26.41 / MAX: 27.29MIN: 26.37 / MAX: 27.91

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_labcd369121510.6210.8610.7210.97MIN: 9.39 / MAX: 10.82MIN: 9.5 / MAX: 11.07MIN: 9.34 / MAX: 10.88MIN: 9.39 / MAX: 11.28

WebP2 Image Encode

This is a test of Google's libwebp2 library with the WebP2 image encode utility and using a sample 6000x4000 pixel JPEG image as the input, similar to the WebP/libwebp test profile. WebP2 is currently experimental and under heavy development as ultimately the successor to WebP. WebP2 supports 10-bit HDR, more efficienct lossy compression, improved lossless compression, animation support, and full multi-threading support compared to WebP. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 75, Compression Effort 7abcd0.0720.1440.2160.2880.36SE +/- 0.00, N = 30.310.310.320.311. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: BioJava Biological Data Frameworkabcd11002200330044005500SE +/- 70.68, N = 34946493547944930

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.orgkH/s, More Is BetterCpuminer-Opt 23.5Algorithm: Ringcoinabcd7001400210028003500SE +/- 2.45, N = 33455.063355.733367.453350.861. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp

easyWave

The easyWave software allows simulating tsunami generation and propagation in the context of early warning systems. EasyWave supports making use of OpenMP for CPU multi-threading and there are also GPU ports available but not currently incorporated as part of this test profile. The easyWave tsunami generation software is run with one of the example/reference input files for measuring the CPU execution time. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BettereasyWave r34Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240abcd0.48330.96661.44991.93322.4165SE +/- 0.004, N = 32.1482.0892.1012.0901. (CXX) g++ options: -O3 -fopenmp

FFmpeg

This is a benchmark of the FFmpeg multimedia framework. The FFmpeg test profile is making use of a modified version of vbench from Columbia University's Architecture and Design Lab (ARCADE) [http://arcade.cs.columbia.edu/vbench/] that is a benchmark for video-as-a-service workloads. The test profile offers the options of a range of vbench scenarios based on freely distributable video content and offers the options of using the x264 or x265 video encoders for transcoding. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 6.1Encoder: libx264 - Scenario: Liveabcd60120180240300SE +/- 1.97, N = 3282.96282.92284.24276.551. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Apache Lucene Search Engineabcd30060090012001500SE +/- 10.95, N = 91331132012951301

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-152abcd51015202518.3017.8118.1617.95MIN: 17.67 / MAX: 18.39MIN: 17.54 / MAX: 18.01MIN: 17.66 / MAX: 18.27MIN: 17.53 / MAX: 18.17

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPUabcd140280420560700SE +/- 0.32, N = 3620.71636.50632.39625.50MIN: 617.84MIN: 632.82MIN: 629.36MIN: 622.671. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Tradebeansabcd8001600240032004000SE +/- 47.36, N = 33912386538243920

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.orgms, Fewer Is BetteroneDNN 3.3Harness: IP Shapes 1D - Data Type: f32 - Engine: CPUabcd0.43670.87341.31011.74682.1835SE +/- 0.02190, N = 31.893791.940871.925661.93518MIN: 1.7MIN: 1.73MIN: 1.71MIN: 1.721. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: ResNet-152abcd4812162017.8018.0717.7518.19MIN: 17.33 / MAX: 18.03MIN: 16.99 / MAX: 18.18MIN: 17.45 / MAX: 17.89MIN: 17.82 / MAX: 18.26

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.orgTotal Execution Time - Seconds, Fewer Is BetterQMCPACK 3.17.1Input: O_ae_pyscf_UHFabcd306090120150SE +/- 1.01, N = 3132.77131.36129.98129.651. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Eclipseabcd2K4K6K8K10KSE +/- 58.05, N = 37923787277477773

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.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Road Segmentation ADAS FP16-INT8 - Device: CPUabcd120240360480600524.58522.64532.94521.461. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Road Segmentation ADAS FP16-INT8 - Device: CPUabcd4812162015.2315.2814.9915.32MIN: 11.89 / MAX: 21.11MIN: 9.17 / MAX: 19.71MIN: 11.64 / MAX: 20.21MIN: 12.62 / MAX: 211. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: ResNet-50abcd163248648069.4170.3969.0568.99MIN: 64.47 / MAX: 71.04MIN: 65.98 / MAX: 71.59MIN: 64.46 / MAX: 70.37MIN: 65.18 / MAX: 70.36

easyWave

The easyWave software allows simulating tsunami generation and propagation in the context of early warning systems. EasyWave supports making use of OpenMP for CPU multi-threading and there are also GPU ports available but not currently incorporated as part of this test profile. The easyWave tsunami generation software is run with one of the example/reference input files for measuring the CPU execution time. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BettereasyWave r34Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200abcd20406080100SE +/- 0.33, N = 381.6381.0380.8780.101. (CXX) g++ options: -O3 -fopenmp

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPUabcd140280420560700SE +/- 1.22, N = 3633.91635.30624.14634.66MIN: 629.4MIN: 629.42MIN: 621.15MIN: 631.751. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_labcd369121510.9311.0410.8510.94MIN: 9.18 / MAX: 11.07MIN: 9.87 / MAX: 11.21MIN: 9.35 / MAX: 11.06MIN: 9.62 / MAX: 11.08

Timed Gem5 Compilation

This test times how long it takes to compile Gem5. Gem5 is a simulator for computer system architecture research. Gem5 is widely used for computer architecture research within the industry, academia, and more. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed Gem5 Compilation 23.0.1Time To Compileabcd60120180240300SE +/- 3.14, N = 3284.15283.22284.04288.16

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_labcd4812162014.2014.1214.0114.25MIN: 14.07 / MAX: 14.34MIN: 13.18 / MAX: 14.23MIN: 13.86 / MAX: 14.16MIN: 14.08 / MAX: 14.36

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Batik SVG Toolkitabcd2004006008001000SE +/- 15.43, N = 31104109210861101

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Zxing 1D/2D Barcode Image Processingabcd130260390520650SE +/- 8.14, N = 3612616606611

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Apache Cassandraabcd12002400360048006000SE +/- 47.51, N = 35480554855545570

QuantLib

QuantLib is an open-source library/framework around quantitative finance for modeling, trading and risk management scenarios. QuantLib is written in C++ with Boost and its built-in benchmark used reports the QuantLib Benchmark Index benchmark score. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMFLOPS, More Is BetterQuantLib 1.32Configuration: Single-Threadedabcd9001800270036004500SE +/- 9.84, N = 33980.34042.64025.44033.01. (CXX) g++ options: -O3 -march=native -fPIE -pie

FFmpeg

This is a benchmark of the FFmpeg multimedia framework. The FFmpeg test profile is making use of a modified version of vbench from Columbia University's Architecture and Design Lab (ARCADE) [http://arcade.cs.columbia.edu/vbench/] that is a benchmark for video-as-a-service workloads. The test profile offers the options of a range of vbench scenarios based on freely distributable video content and offers the options of using the x264 or x265 video encoders for transcoding. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 6.1Encoder: libx264 - Scenario: Uploadabcd48121620SE +/- 0.14, N = 316.9016.6716.8116.641. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPUabcd0.84781.69562.54343.39124.239SE +/- 0.01733, N = 33.767793.716503.727813.76192MIN: 3.68MIN: 3.63MIN: 3.66MIN: 3.671. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPUabcd16K32K48K64K80KSE +/- 88.19, N = 371605723467259071802

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.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Handwritten English Recognition FP16-INT8 - Device: CPUabcd130260390520650584.10585.55587.47579.561. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 512 - Model: ResNet-50abcd112233445546.3146.3646.4146.93MIN: 43.53 / MAX: 47.23MIN: 42.96 / MAX: 47.26MIN: 43.7 / MAX: 46.95MIN: 44.02 / MAX: 47.5

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.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Handwritten English Recognition FP16-INT8 - Device: CPUabcd61218243027.3627.2927.2127.57MIN: 19.64 / MAX: 35.39MIN: 21.85 / MAX: 35.83MIN: 22.22 / MAX: 34.96MIN: 20.3 / MAX: 33.351. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_labcd369121510.8410.9310.8810.79MIN: 9.5 / MAX: 10.98MIN: 9.59 / MAX: 11.11MIN: 9.61 / MAX: 11.08MIN: 9.49 / MAX: 10.96

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 256 - Model: ResNet-152abcd4812162017.9618.1118.1118.19MIN: 17.36 / MAX: 18.16MIN: 17.67 / MAX: 18.23MIN: 17.68 / MAX: 18.22MIN: 14.43 / MAX: 18.68

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.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Person Vehicle Bike Detection FP16 - Device: CPUabcd300600900120015001572.381587.741591.841572.561. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Person Vehicle Bike Detection FP16 - Device: CPUabcd1.13852.2773.41554.5545.69255.065.025.005.06MIN: 3.62 / MAX: 13.34MIN: 3.6 / MAX: 10.68MIN: 3.63 / MAX: 11.88MIN: 3.24 / MAX: 9.551. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Apache Xalan XSLTabcd110220330440550SE +/- 1.00, N = 3526522528524

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 3 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPUabcd10K20K30K40K50KSE +/- 116.17, N = 344670444374468244177

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPUabcd140280420560700SE +/- 2.14, N = 3632.07633.61628.31635.31MIN: 628.15MIN: 626.34MIN: 625.84MIN: 632.151. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

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.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Handwritten English Recognition FP16 - Device: CPUabcd160320480640800739.56731.42733.81733.361. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Handwritten English Recognition FP16 - Device: CPUabcd51015202521.6121.8521.7721.79MIN: 15.02 / MAX: 30.51MIN: 14.62 / MAX: 38.4MIN: 17.91 / MAX: 28.95MIN: 14.67 / MAX: 31.511. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Person Detection FP16 - Device: CPUabcd2040608010094.2093.9594.2994.991. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Person Detection FP16 - Device: CPUabcd2040608010084.8385.0784.7484.14MIN: 51.55 / MAX: 110.45MIN: 55.85 / MAX: 113.02MIN: 44.18 / MAX: 109.72MIN: 49.25 / MAX: 110.521. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Road Segmentation ADAS FP16 - Device: CPUabcd51015202518.1618.3218.2418.36MIN: 9.71 / MAX: 27.64MIN: 12.74 / MAX: 30.01MIN: 12.24 / MAX: 26.29MIN: 9.65 / MAX: 27.171. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Road Segmentation ADAS FP16 - Device: CPUabcd100200300400500439.75435.77437.76434.981. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

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.orgTotal Execution Time - Seconds, Fewer Is BetterQMCPACK 3.17.1Input: simple-H2Oabcd510152025SE +/- 0.03, N = 318.2418.4318.3918.301. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: H2 Database Engineabcd400800120016002000SE +/- 9.96, N = 31855185318401859

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPUabcd30060090012001500SE +/- 0.95, N = 31231.931236.681236.281224.11MIN: 1227.81MIN: 1230.73MIN: 1233.21MIN: 1220.231. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_labcd369121510.8110.8910.8710.92MIN: 9.24 / MAX: 10.96MIN: 9.54 / MAX: 11.07MIN: 9.55 / MAX: 11.02MIN: 9.26 / MAX: 11.09

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_labcd369121510.8610.9310.8210.90MIN: 9.49 / MAX: 11MIN: 8.84 / MAX: 11.11MIN: 9.26 / MAX: 10.96MIN: 9.57 / MAX: 11.05

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.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Person Detection FP32 - Device: CPUabcd2040608010084.9984.4385.2784.74MIN: 54.5 / MAX: 109.88MIN: 38.96 / MAX: 118.46MIN: 56.98 / MAX: 111.16MIN: 43.16 / MAX: 116.661. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Person Detection FP32 - Device: CPUabcd2040608010094.0594.6693.7694.281. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. The system/openssl test profiles relies on benchmarking the system/OS-supplied openssl binary rather than the pts/openssl test profile that uses the locally-built OpenSSL for benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSLAlgorithm: SHA256abcd7000M14000M21000M28000M35000M326317087103290739491032833153190329301274101. OpenSSL 3.0.2 15 Mar 2022 (Library: OpenSSL 3.0.2 15 Mar 2022)

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.orgkH/s, More Is BetterCpuminer-Opt 23.5Algorithm: Myriad-Groestlabcd2K4K6K8K10KSE +/- 50.00, N = 3114401149011400113901. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp

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.orgTotal Execution Time - Seconds, Fewer Is BetterQMCPACK 3.17.1Input: LiH_ae_MSDabcd1632486480SE +/- 0.40, N = 373.4774.1073.6073.491. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPUabcd0.67751.3552.03252.713.3875SE +/- 0.00759, N = 32.985363.010993.009062.98785MIN: 2.51MIN: 2.51MIN: 2.51MIN: 2.511. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

CloverLeaf

CloverLeaf is a Lagrangian-Eulerian hydrodynamics benchmark. This test profile currently makes use of CloverLeaf's OpenMP version. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterCloverLeaf 1.3Input: clover_bmabcd714212835SE +/- 0.08, N = 331.8431.7932.0232.061. (F9X) gfortran options: -O3 -march=native -funroll-loops -fopenmp

FFmpeg

This is a benchmark of the FFmpeg multimedia framework. The FFmpeg test profile is making use of a modified version of vbench from Columbia University's Architecture and Design Lab (ARCADE) [http://arcade.cs.columbia.edu/vbench/] that is a benchmark for video-as-a-service workloads. The test profile offers the options of a range of vbench scenarios based on freely distributable video content and offers the options of using the x264 or x265 video encoders for transcoding. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 6.1Encoder: libx265 - Scenario: Video On Demandabcd1530456075SE +/- 0.07, N = 368.4868.5768.1468.711. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 2 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPUabcd8K16K24K32K40KSE +/- 34.12, N = 338852387403879839062

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPUabcd30060090012001500SE +/- 0.51, N = 31227.581229.801234.141224.00MIN: 1224.62MIN: 1224.55MIN: 1229.87MIN: 1220.911. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

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.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Crownabcd714212835SE +/- 0.04, N = 331.9231.9731.7031.85MIN: 31.66 / MAX: 32.67MIN: 31.62 / MAX: 32.67MIN: 31.4 / MAX: 32.36MIN: 31.57 / MAX: 32.5

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.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Machine Translation EN To DE FP16 - Device: CPUabcd306090120150130.05130.95130.06131.091. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPUabcd0.90971.81942.72913.63884.5485SE +/- 0.00942, N = 34.017814.034184.011144.04319MIN: 3.96MIN: 3.94MIN: 3.96MIN: 3.981. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

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.orgkH/s, More Is BetterCpuminer-Opt 23.5Algorithm: Blake-2 Sabcd30K60K90K120K150KSE +/- 3.33, N = 31346601341771348001352001. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp

FFmpeg

This is a benchmark of the FFmpeg multimedia framework. The FFmpeg test profile is making use of a modified version of vbench from Columbia University's Architecture and Design Lab (ARCADE) [http://arcade.cs.columbia.edu/vbench/] that is a benchmark for video-as-a-service workloads. The test profile offers the options of a range of vbench scenarios based on freely distributable video content and offers the options of using the x264 or x265 video encoders for transcoding. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 6.1Encoder: libx264 - Scenario: Video On Demandabcd1428425670SE +/- 0.11, N = 364.1663.7864.1464.261. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. The system/openssl test profiles relies on benchmarking the system/OS-supplied openssl binary rather than the pts/openssl test profile that uses the locally-built OpenSSL for benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgsign/s, More Is BetterOpenSSLAlgorithm: RSA4096abcd120024003600480060005501.25508.45468.45500.81. OpenSSL 3.0.2 15 Mar 2022 (Library: OpenSSL 3.0.2 15 Mar 2022)

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.orgkH/s, More Is BetterCpuminer-Opt 23.5Algorithm: Magiabcd140280420560700SE +/- 0.61, N = 3640.30635.66636.52635.791. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp

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.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Machine Translation EN To DE FP16 - Device: CPUabcd142842567061.3860.9861.3860.94MIN: 46.13 / MAX: 71.27MIN: 27.92 / MAX: 70.86MIN: 44.4 / MAX: 70.65MIN: 46.99 / MAX: 72.581. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Face Detection Retail FP16-INT8 - Device: CPUabcd100020003000400050004527.764537.364512.344544.571. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

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.orgTotal Execution Time - Seconds, Fewer Is BetterQMCPACK 3.17.1Input: Li2_STO_aeabcd306090120150SE +/- 1.12, N = 3135.60135.95135.60135.021. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPUabcd9001800270036004500SE +/- 3.51, N = 34251426742394251

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPUabcd30060090012001500SE +/- 2.80, N = 31236.291231.681228.251234.57MIN: 1232.2MIN: 1222.84MIN: 1224.56MIN: 12311. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 16 - Model: ResNet-152abcd4812162018.1318.0218.0318.02MIN: 17.59 / MAX: 18.32MIN: 17.53 / MAX: 18.23MIN: 17.54 / MAX: 18.23MIN: 17.66 / MAX: 18.18

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 1 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPUabcd8K16K24K32K40KSE +/- 262.57, N = 338499382793844838512

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 2 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPUabcd9001800270036004500SE +/- 4.41, N = 34303432343164297

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.orgms, Fewer Is BetteroneDNN 3.3Harness: IP Shapes 3D - Data Type: f32 - Engine: CPUabcd0.63421.26841.90262.53683.171SE +/- 0.01747, N = 32.801832.817372.803772.81873MIN: 2.76MIN: 2.75MIN: 2.76MIN: 2.781. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

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.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Face Detection FP16 - Device: CPUabcd369121513.4113.3813.3313.391. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Face Detection Retail FP16-INT8 - Device: CPUabcd0.7741.5482.3223.0963.873.443.433.443.42MIN: 1.95 / MAX: 10.99MIN: 1.96 / MAX: 8.28MIN: 1.94 / MAX: 11.06MIN: 1.94 / MAX: 6.891. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

PyTorch

OpenBenchmarking.orgbatches/sec, More Is BetterPyTorch 2.1Device: CPU - Batch Size: 32 - Model: ResNet-152abcd4812162018.0418.0618.0518.14MIN: 17.52 / MAX: 18.19MIN: 17.57 / MAX: 18.26MIN: 17.55 / MAX: 18.12MIN: 17.74 / MAX: 18.22

Timed FFmpeg Compilation

This test times how long it takes to build the FFmpeg multimedia library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed FFmpeg Compilation 6.1Time To Compileabcd510152025SE +/- 0.01, N = 321.9121.9021.9222.01

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPUabcd30K60K90K120K150KSE +/- 200.00, N = 3139956140342140119139631

FFmpeg

This is a benchmark of the FFmpeg multimedia framework. The FFmpeg test profile is making use of a modified version of vbench from Columbia University's Architecture and Design Lab (ARCADE) [http://arcade.cs.columbia.edu/vbench/] that is a benchmark for video-as-a-service workloads. The test profile offers the options of a range of vbench scenarios based on freely distributable video content and offers the options of using the x264 or x265 video encoders for transcoding. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 6.1Encoder: libx265 - Scenario: Platformabcd1530456075SE +/- 0.09, N = 368.3968.3668.2968.631. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 2 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPUabcd16K32K48K64K80KSE +/- 124.54, N = 373112728157288872762

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPUabcd0.24740.49480.74220.98961.237SE +/- 0.00130, N = 31.094251.097601.099421.09782MIN: 1.07MIN: 1.07MIN: 1.07MIN: 1.071. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 2 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPUabcd4K8K12K16K20KSE +/- 12.45, N = 317266173161723517289

OpenBenchmarking.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPUabcd20K40K60K80K100KSE +/- 192.43, N = 384397846608478684573

FFmpeg

This is a benchmark of the FFmpeg multimedia framework. The FFmpeg test profile is making use of a modified version of vbench from Columbia University's Architecture and Design Lab (ARCADE) [http://arcade.cs.columbia.edu/vbench/] that is a benchmark for video-as-a-service workloads. The test profile offers the options of a range of vbench scenarios based on freely distributable video content and offers the options of using the x264 or x265 video encoders for transcoding. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 6.1Encoder: libx264 - Scenario: Platformabcd1428425670SE +/- 0.08, N = 363.9863.8563.6963.751. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma

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.orgkH/s, More Is BetterCpuminer-Opt 23.5Algorithm: LBC, LBRY Creditsabcd3K6K9K12K15KSE +/- 3.33, N = 3158501578315790157801. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 3 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPUabcd4K8K12K16K20KSE +/- 20.65, N = 320148202262018620139

FFmpeg

This is a benchmark of the FFmpeg multimedia framework. The FFmpeg test profile is making use of a modified version of vbench from Columbia University's Architecture and Design Lab (ARCADE) [http://arcade.cs.columbia.edu/vbench/] that is a benchmark for video-as-a-service workloads. The test profile offers the options of a range of vbench scenarios based on freely distributable video content and offers the options of using the x264 or x265 video encoders for transcoding. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 6.1Encoder: libx265 - Scenario: Liveabcd4080120160200SE +/- 0.76, N = 3179.55180.06180.02179.291. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma

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.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUabcd10K20K30K40K50K47376.5947316.1747255.3647453.431. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenVKL

OpenVKL is the Intel Open Volume Kernel Library that offers high-performance volume computation kernels and part of the Intel oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 2.0.0Benchmark: vklBenchmarkCPU Scalarabcd50100150200250SE +/- 0.67, N = 3242242242241MIN: 17 / MAX: 4418MIN: 16 / MAX: 4422MIN: 17 / MAX: 4423MIN: 16 / MAX: 4419

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Apache Tomcatabcd7001400210028003500SE +/- 3.51, N = 33432342434383437

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.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Vehicle Detection FP16-INT8 - Device: CPUabcd1.1072.2143.3214.4285.5354.924.914.904.90MIN: 2.75 / MAX: 10.58MIN: 2.77 / MAX: 14.1MIN: 2.76 / MAX: 13.8MIN: 2.75 / MAX: 9.121. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

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.orgTotal Execution Time - Seconds, Fewer Is BetterQMCPACK 3.17.1Input: FeCO6_b3lyp_gmsabcd306090120150SE +/- 0.20, N = 3125.46125.49124.98125.131. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPUabcd0.33470.66941.00411.33881.6735SE +/- 0.00176, N = 31.483851.484961.487521.48153MIN: 1.47MIN: 1.47MIN: 1.48MIN: 1.471. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

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.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Face Detection Retail FP16 - Device: CPUabcd0.56251.1251.68752.252.81252.492.502.502.49MIN: 1.35 / MAX: 6.3MIN: 1.34 / MAX: 9.48MIN: 1.34 / MAX: 9.59MIN: 1.35 / MAX: 9.241. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

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.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer - Model: Crownabcd714212835SE +/- 0.06, N = 330.5030.4530.3930.51MIN: 30.29 / MAX: 31MIN: 30.1 / MAX: 31.05MIN: 30.17 / MAX: 30.88MIN: 30.28 / MAX: 31.02

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPUabcd40K80K120K160K200KSE +/- 70.29, N = 3165253165034164625164645

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.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Asian Dragonabcd816243240SE +/- 0.07, N = 333.7833.7333.6533.72MIN: 33.54 / MAX: 34.43MIN: 33.38 / MAX: 34.7MIN: 33.44 / MAX: 34.17MIN: 33.46 / MAX: 34.55

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.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Face Detection FP16 - Device: CPUabcd130260390520650595.55595.36597.20594.98MIN: 576.09 / MAX: 622.5MIN: 575.04 / MAX: 623.57MIN: 574.49 / MAX: 623.82MIN: 577.06 / MAX: 624.471. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Vehicle Detection FP16-INT8 - Device: CPUabcd300600900120015001613.221617.791617.821619.161. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPUabcd0.14570.29140.43710.58280.7285SE +/- 0.000222, N = 30.6475500.6468660.6462230.645231MIN: 0.64MIN: 0.64MIN: 0.64MIN: 0.641. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

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.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer ISPC - Model: Asian Dragon Objabcd714212835SE +/- 0.05, N = 328.6128.7128.6428.70MIN: 28.41 / MAX: 29.24MIN: 28.44 / MAX: 29.67MIN: 28.43 / MAX: 29.3MIN: 28.49 / MAX: 29.4

OpenBenchmarking.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer - Model: Asian Dragonabcd714212835SE +/- 0.06, N = 330.9330.8930.8330.83MIN: 30.8 / MAX: 31.38MIN: 30.64 / MAX: 31.42MIN: 30.69 / MAX: 31.19MIN: 30.69 / MAX: 31.27

WebP2 Image Encode

This is a test of Google's libwebp2 library with the WebP2 image encode utility and using a sample 6000x4000 pixel JPEG image as the input, similar to the WebP/libwebp test profile. WebP2 is currently experimental and under heavy development as ultimately the successor to WebP. WebP2 supports 10-bit HDR, more efficienct lossy compression, improved lossless compression, animation support, and full multi-threading support compared to WebP. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 100, Compression Effort 5abcd246810SE +/- 0.01, N = 38.738.738.728.751. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPUabcd11002200330044005500SE +/- 4.26, N = 35025502750355042

FFmpeg

This is a benchmark of the FFmpeg multimedia framework. The FFmpeg test profile is making use of a modified version of vbench from Columbia University's Architecture and Design Lab (ARCADE) [http://arcade.cs.columbia.edu/vbench/] that is a benchmark for video-as-a-service workloads. The test profile offers the options of a range of vbench scenarios based on freely distributable video content and offers the options of using the x264 or x265 video encoders for transcoding. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFPS, More Is BetterFFmpeg 6.1Encoder: libx265 - Scenario: Uploadabcd816243240SE +/- 0.08, N = 333.5433.5333.4633.571. (CXX) g++ options: -O3 -rdynamic -lpthread -lrt -ldl -lnuma

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.orgms, Fewer Is BetteroneDNN 3.3Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPUabcd0.15650.3130.46950.6260.7825SE +/- 0.002235, N = 30.6942960.6931230.6953390.695171MIN: 0.65MIN: 0.64MIN: 0.65MIN: 0.651. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

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.orgkH/s, More Is BetterCpuminer-Opt 23.5Algorithm: scryptabcd70140210280350SE +/- 0.25, N = 3304.46304.62305.22305.361. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 2 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPUabcd30K60K90K120K150KSE +/- 201.33, N = 3141550141773141698141360

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. The system/openssl test profiles relies on benchmarking the system/OS-supplied openssl binary rather than the pts/openssl test profile that uses the locally-built OpenSSL for benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSLAlgorithm: ChaCha20abcd30000M60000M90000M120000M150000M1254886520101251274309601253385800401252770638501. OpenSSL 3.0.2 15 Mar 2022 (Library: OpenSSL 3.0.2 15 Mar 2022)

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.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Vehicle Detection FP16 - Device: CPUabcd2468107.717.737.737.72MIN: 4.99 / MAX: 14.68MIN: 4.78 / MAX: 16.94MIN: 4.84 / MAX: 13.08MIN: 4.51 / MAX: 13.751. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Face Detection FP16-INT8 - Device: CPUabcd70140210280350313.05312.53312.48313.25MIN: 299.21 / MAX: 324.17MIN: 300.51 / MAX: 321.6MIN: 296.91 / MAX: 323.74MIN: 299.83 / MAX: 323.761. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

QuantLib

QuantLib is an open-source library/framework around quantitative finance for modeling, trading and risk management scenarios. QuantLib is written in C++ with Boost and its built-in benchmark used reports the QuantLib Benchmark Index benchmark score. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMFLOPS, More Is BetterQuantLib 1.32Configuration: Multi-Threadedabcd20K40K60K80K100KSE +/- 83.63, N = 382133.582020.081958.082159.41. (CXX) g++ options: -O3 -march=native -fPIE -pie

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 1 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPUabcd4K8K12K16K20KSE +/- 12.41, N = 317116171161709717075

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.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Weld Porosity Detection FP16-INT8 - Device: CPUabcd60012001800240030002608.392604.122609.572603.351. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 3 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPUabcd30060090012001500SE +/- 2.65, N = 31267126612651264

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.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Face Detection Retail FP16 - Device: CPUabcd70014002100280035003070.773063.553067.523069.351. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Face Detection FP16-INT8 - Device: CPUabcd61218243025.5225.5625.5625.501. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

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.orgFrames Per Second, More Is BetterEmbree 4.3Binary: Pathtracer - Model: Asian Dragon Objabcd612182430SE +/- 0.04, N = 327.5827.5227.5427.53MIN: 27.4 / MAX: 28.05MIN: 27.3 / MAX: 28.13MIN: 27.35 / MAX: 27.99MIN: 27.38 / MAX: 27.96

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.orgkH/s, More Is BetterCpuminer-Opt 23.5Algorithm: Deepcoinabcd2K4K6K8K10KSE +/- 21.85, N = 37978.247965.217982.947973.871. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp

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.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Vehicle Detection FP16 - Device: CPUabcd20040060080010001034.251032.481032.001033.331. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Weld Porosity Detection FP16 - Device: CPUabcd300600900120015001353.911352.891353.621351.171. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 2 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPUabcd2004006008001000SE +/- 0.58, N = 31083108310831085

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPUabcd0.10270.20540.30810.41080.5135SE +/- 0.000061, N = 30.4555310.4563470.4562500.455553MIN: 0.44MIN: 0.44MIN: 0.44MIN: 0.441. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.3Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPUabcd0.52721.05441.58162.10882.636SE +/- 0.00064, N = 32.343292.341252.339222.34110MIN: 2.31MIN: 2.31MIN: 2.31MIN: 2.31. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

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.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Weld Porosity Detection FP16 - Device: CPUabcd369121511.7911.8011.7911.81MIN: 6.37 / MAX: 23.3MIN: 7.57 / MAX: 15.99MIN: 6.2 / MAX: 21.55MIN: 6.78 / MAX: 18.071. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. The system/openssl test profiles relies on benchmarking the system/OS-supplied openssl binary rather than the pts/openssl test profile that uses the locally-built OpenSSL for benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgverify/s, More Is BetterOpenSSLAlgorithm: RSA4096abcd80K160K240K320K400K358677.5359044.2358701.3358447.11. OpenSSL 3.0.2 15 Mar 2022 (Library: OpenSSL 3.0.2 15 Mar 2022)

OpenVKL

OpenVKL is the Intel Open Volume Kernel Library that offers high-performance volume computation kernels and part of the Intel oneAPI rendering toolkit. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgItems / Sec, More Is BetterOpenVKL 2.0.0Benchmark: vklBenchmarkCPU ISPCabcd130260390520650SE +/- 0.00, N = 3603603604603MIN: 46 / MAX: 8290MIN: 46 / MAX: 8279MIN: 46 / MAX: 8291MIN: 46 / MAX: 8277

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.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Weld Porosity Detection FP16-INT8 - Device: CPUabcd2468106.106.116.106.11MIN: 3.19 / MAX: 11.01MIN: 3.19 / MAX: 13.98MIN: 3.18 / MAX: 11.91MIN: 3.18 / MAX: 11.791. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

CloverLeaf

CloverLeaf is a Lagrangian-Eulerian hydrodynamics benchmark. This test profile currently makes use of CloverLeaf's OpenMP version. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterCloverLeaf 1.3Input: clover_bm16abcd30060090012001500SE +/- 0.59, N = 31496.781495.651494.531496.301. (F9X) gfortran options: -O3 -march=native -funroll-loops -fopenmp

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: Apache Kafkaabcd11002200330044005500SE +/- 0.88, N = 35095509150985094

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. The system/openssl test profiles relies on benchmarking the system/OS-supplied openssl binary rather than the pts/openssl test profile that uses the locally-built OpenSSL for benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSLAlgorithm: ChaCha20-Poly1305abcd20000M40000M60000M80000M100000M894615022308958268839089478928250894884039301. OpenSSL 3.0.2 15 Mar 2022 (Library: OpenSSL 3.0.2 15 Mar 2022)

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSLAlgorithm: SHA512abcd2000M4000M6000M8000M10000M106384699601063583553010649075500106392313701. OpenSSL 3.0.2 15 Mar 2022 (Library: OpenSSL 3.0.2 15 Mar 2022)

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.orgkH/s, More Is BetterCpuminer-Opt 23.5Algorithm: Quad SHA-256, Pyriteabcd13K26K39K52K65KSE +/- 16.67, N = 3620806212762080620501. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp

OpenBenchmarking.orgkH/s, More Is BetterCpuminer-Opt 23.5Algorithm: Skeincoinabcd7K14K21K28K35KSE +/- 5.77, N = 3344403444034430344701. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp

OSPRay Studio

Intel OSPRay Studio is an open-source, interactive visualization and ray-tracing software package. OSPRay Studio makes use of Intel OSPRay, a portable ray-tracing engine for high-performance, high-fidelity 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.orgms, Fewer Is BetterOSPRay Studio 0.13Camera: 1 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPUabcd2004006008001000SE +/- 0.58, N = 31069106910691068

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.orgFPS, More Is BetterOpenVINO 2023.2.devModel: Age Gender Recognition Retail 0013 FP16 - Device: CPUabcd7K14K21K28K35K33511.7933483.5333482.5433491.831. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: jMonkeyEngineabcd15003000450060007500SE +/- 0.88, N = 36812681568116815

OpenSSL

OpenSSL is an open-source toolkit that implements SSL (Secure Sockets Layer) and TLS (Transport Layer Security) protocols. The system/openssl test profiles relies on benchmarking the system/OS-supplied openssl binary rather than the pts/openssl test profile that uses the locally-built OpenSSL for benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSLAlgorithm: AES-128-GCMabcd20000M40000M60000M80000M100000M989600814409895833518098987719340990110304901. OpenSSL 3.0.2 15 Mar 2022 (Library: OpenSSL 3.0.2 15 Mar 2022)

OpenBenchmarking.orgbyte/s, More Is BetterOpenSSLAlgorithm: AES-256-GCMabcd20000M40000M60000M80000M100000M924807869809251269263092479315690924791346501. OpenSSL 3.0.2 15 Mar 2022 (Library: OpenSSL 3.0.2 15 Mar 2022)

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.orgkH/s, More Is BetterCpuminer-Opt 23.5Algorithm: Triple SHA-256, Onecoinabcd20K40K60K80K100KSE +/- 3.33, N = 31058801058971058901058601. (CXX) g++ options: -O2 -lcurl -lz -lpthread -lssl -lcrypto -lgmp

CloverLeaf

CloverLeaf is a Lagrangian-Eulerian hydrodynamics benchmark. This test profile currently makes use of CloverLeaf's OpenMP version. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterCloverLeaf 1.3Input: clover_bm64_shortabcd4080120160200SE +/- 0.02, N = 3175.91175.92175.92175.861. (F9X) gfortran options: -O3 -march=native -funroll-loops -fopenmp

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.orgms, Fewer Is BetteroneDNN 3.3Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPUabcd0.58481.16961.75442.33922.924SE +/- 0.00014, N = 32.598962.598382.598352.59886MIN: 2.59MIN: 2.59MIN: 2.59MIN: 2.591. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

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.orgms, Fewer Is BetterOpenVINO 2023.2.devModel: Age Gender Recognition Retail 0013 FP16 - Device: CPUabcd0.09680.19360.29040.38720.4840.430.430.430.43MIN: 0.22 / MAX: 4.2MIN: 0.22 / MAX: 4.19MIN: 0.22 / MAX: 7.73MIN: 0.22 / MAX: 5.021. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared -ldl

Intel Open Image Denoise

OpenBenchmarking.orgImages / Sec, More Is BetterIntel Open Image Denoise 2.1Run: RTLightmap.hdr.4096x4096 - Device: CPU-Onlyabcd0.08780.17560.26340.35120.439SE +/- 0.00, N = 30.390.390.390.39

OpenBenchmarking.orgImages / Sec, More Is BetterIntel Open Image Denoise 2.1Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Onlyabcd0.18230.36460.54690.72920.9115SE +/- 0.00, N = 30.810.810.810.81

OpenBenchmarking.orgImages / Sec, More Is BetterIntel Open Image Denoise 2.1Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Onlyabcd0.18230.36460.54690.72920.9115SE +/- 0.00, N = 30.810.810.810.81

WebP2 Image Encode

This is a test of Google's libwebp2 library with the WebP2 image encode utility and using a sample 6000x4000 pixel JPEG image as the input, similar to the WebP/libwebp test profile. WebP2 is currently experimental and under heavy development as ultimately the successor to WebP. WebP2 supports 10-bit HDR, more efficienct lossy compression, improved lossless compression, animation support, and full multi-threading support compared to WebP. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 100, Lossless Compressionabcd0.00680.01360.02040.02720.034SE +/- 0.00, N = 30.030.030.030.031. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

OpenBenchmarking.orgMP/s, More Is BetterWebP2 Image Encode 20220823Encode Settings: Quality 95, Compression Effort 7abcd0.03380.06760.10140.13520.169SE +/- 0.00, N = 30.150.150.150.151. (CXX) g++ options: -msse4.2 -fno-rtti -O3 -ldl

RabbitMQ

RabbitMQ is an open-source message broker. This test profile makes use of the RabbitMQ PerfTest with the RabbitMQ server and PerfTest client running on the same host namely as a system/CPU performance benchmark. Learn more via the OpenBenchmarking.org test page.

Scenario: 200 Queues, 400 Producers, 400 Consumers

a: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

b: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

c: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

d: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

Scenario: 120 Queues, 400 Producers, 400 Consumers

a: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

b: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

c: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

d: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

Scenario: 60 Queues, 100 Producers, 100 Consumers

a: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

b: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

c: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

d: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

Scenario: 10 Queues, 100 Producers, 100 Consumers

a: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

b: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

c: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

d: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

Scenario: Simple 2 Publishers + 4 Consumers

a: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

b: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

c: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

d: The test quit with a non-zero exit status. E: java.net.ConnectException: Connection refused (Connection refused)

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.orgms, Fewer Is BetteroneDNN 3.3Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPUabcd0.12030.24060.36090.48120.6015SE +/- 0.010372, N = 150.5236590.4987410.5345760.520575MIN: 0.43MIN: 0.39MIN: 0.43MIN: 0.431. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl

DaCapo Benchmark

This test runs the DaCapo Benchmarks written in Java and intended to test system/CPU performance of various popular real-world Java workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgmsec, Fewer Is BetterDaCapo Benchmark 23.11Java Test: FOP Print Formatterabcd100200300400500SE +/- 7.98, N = 15414417455430

172 Results Shown

oneDNN
DaCapo Benchmark:
  Avrora AVR Simulation Framework
  Spring Boot
PyTorch
DaCapo Benchmark:
  Apache Lucene Search Index
  PMD Source Code Analyzer
oneDNN
WebP2 Image Encode
DaCapo Benchmark
PyTorch
DaCapo Benchmark:
  Jython
  Tradesoap
QMCPACK
DaCapo Benchmark
PyTorch:
  CPU - 64 - ResNet-50
  CPU - 16 - ResNet-50
OpenVINO
Cpuminer-Opt
PyTorch:
  CPU - 1 - ResNet-152
  CPU - 16 - Efficientnet_v2_l
WebP2 Image Encode
DaCapo Benchmark
Cpuminer-Opt
easyWave
FFmpeg
DaCapo Benchmark
PyTorch
oneDNN
DaCapo Benchmark
oneDNN
PyTorch
QMCPACK
DaCapo Benchmark
OpenVINO:
  Road Segmentation ADAS FP16-INT8 - CPU:
    FPS
    ms
PyTorch
easyWave
oneDNN
PyTorch
Timed Gem5 Compilation
PyTorch
DaCapo Benchmark:
  Batik SVG Toolkit
  Zxing 1D/2D Barcode Image Processing
  Apache Cassandra
QuantLib
FFmpeg
oneDNN
OSPRay Studio
OpenVINO
PyTorch
OpenVINO
PyTorch:
  CPU - 256 - Efficientnet_v2_l
  CPU - 256 - ResNet-152
OpenVINO:
  Person Vehicle Bike Detection FP16 - CPU:
    FPS
    ms
DaCapo Benchmark
OSPRay Studio
oneDNN
OpenVINO:
  Handwritten English Recognition FP16 - CPU:
    FPS
    ms
  Person Detection FP16 - CPU:
    FPS
    ms
  Road Segmentation ADAS FP16 - CPU:
    ms
    FPS
QMCPACK
DaCapo Benchmark
oneDNN
PyTorch:
  CPU - 32 - Efficientnet_v2_l
  CPU - 64 - Efficientnet_v2_l
OpenVINO:
  Person Detection FP32 - CPU:
    ms
    FPS
OpenSSL
Cpuminer-Opt
QMCPACK
oneDNN
CloverLeaf
FFmpeg
OSPRay Studio
oneDNN
Embree
OpenVINO
oneDNN
Cpuminer-Opt
FFmpeg
OpenSSL
Cpuminer-Opt
OpenVINO:
  Machine Translation EN To DE FP16 - CPU
  Face Detection Retail FP16-INT8 - CPU
QMCPACK
OSPRay Studio
oneDNN
PyTorch
OSPRay Studio:
  1 - 1080p - 32 - Path Tracer - CPU
  2 - 4K - 1 - Path Tracer - CPU
oneDNN
OpenVINO:
  Face Detection FP16 - CPU
  Face Detection Retail FP16-INT8 - CPU
PyTorch
Timed FFmpeg Compilation
OSPRay Studio
FFmpeg
OSPRay Studio
oneDNN
OSPRay Studio:
  2 - 1080p - 16 - Path Tracer - CPU
  3 - 4K - 16 - Path Tracer - CPU
FFmpeg
Cpuminer-Opt
OSPRay Studio
FFmpeg
OpenVINO
OpenVKL
DaCapo Benchmark
OpenVINO
QMCPACK
oneDNN
OpenVINO
Embree
OSPRay Studio
Embree
OpenVINO:
  Face Detection FP16 - CPU
  Vehicle Detection FP16-INT8 - CPU
oneDNN
Embree:
  Pathtracer ISPC - Asian Dragon Obj
  Pathtracer - Asian Dragon
WebP2 Image Encode
OSPRay Studio
FFmpeg
oneDNN
Cpuminer-Opt
OSPRay Studio
OpenSSL
OpenVINO:
  Vehicle Detection FP16 - CPU
  Face Detection FP16-INT8 - CPU
QuantLib
OSPRay Studio
OpenVINO
OSPRay Studio
OpenVINO:
  Face Detection Retail FP16 - CPU
  Face Detection FP16-INT8 - CPU
Embree
Cpuminer-Opt
OpenVINO:
  Vehicle Detection FP16 - CPU
  Weld Porosity Detection FP16 - CPU
OSPRay Studio
oneDNN:
  Deconvolution Batch shapes_1d - u8s8f32 - CPU
  Deconvolution Batch shapes_1d - bf16bf16bf16 - CPU
OpenVINO
OpenSSL
OpenVKL
OpenVINO
CloverLeaf
DaCapo Benchmark
OpenSSL:
  ChaCha20-Poly1305
  SHA512
Cpuminer-Opt:
  Quad SHA-256, Pyrite
  Skeincoin
OSPRay Studio
OpenVINO
DaCapo Benchmark
OpenSSL:
  AES-128-GCM
  AES-256-GCM
Cpuminer-Opt
CloverLeaf
oneDNN
OpenVINO
Intel Open Image Denoise:
  RTLightmap.hdr.4096x4096 - CPU-Only
  RT.ldr_alb_nrm.3840x2160 - CPU-Only
  RT.hdr_alb_nrm.3840x2160 - CPU-Only
WebP2 Image Encode:
  Quality 100, Lossless Compression
  Quality 95, Compression Effort 7
oneDNN
DaCapo Benchmark