svc05_hpc_run_1_23-09-22

v1.59

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Samsung SSD 980 PRO 1TB
September 22 2023
  4 Days, 7 Hours, 44 Minutes
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Samsung SSD 980 PRO 1TB

Processor: AMD Ryzen Threadripper PRO 5995WX 64-Cores @ 2.70GHz (64 Cores), Motherboard: ASRock WRX80 Creator (5.01 BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 1000GB Samsung SSD 980 PRO 1TB, Graphics: llvmpipe, Audio: AMD Starship/Matisse, Network: 2 x Intel X710 for 10GBASE-T + Intel Wi-Fi 6 AX210/AX211/AX411

OS: Ubuntu 22.04, Kernel: 6.2.0-33-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.3, OpenGL: 4.5 Mesa 23.0.4-0ubuntu1~22.04.1 (LLVM 15.0.7 256 bits), Vulkan: 1.3.238, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1024x768

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --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
Disk Notes: NONE / errors=remount-ro,relatime,rw / Block Size: 4096
Processor Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa008204
Python Notes: Python 3.10.12
Security Notes: 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_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: disabled RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

svc05_hpc_run_1_23-09-22OpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen Threadripper PRO 5995WX 64-Cores @ 2.70GHz (64 Cores)ASRock WRX80 Creator (5.01 BIOS)AMD Starship/Matisse256GB1000GB Samsung SSD 980 PRO 1TBllvmpipeAMD Starship/Matisse2 x Intel X710 for 10GBASE-T + Intel Wi-Fi 6 AX210/AX211/AX411Ubuntu 22.046.2.0-33-generic (x86_64)GNOME Shell 42.9X Server 1.21.1.34.5 Mesa 23.0.4-0ubuntu1~22.04.1 (LLVM 15.0.7 256 bits)1.3.238GCC 11.4.0ext41024x768ProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLVulkanCompilerFile-SystemScreen ResolutionSvc05_hpc_run_1_23-09-22 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 - NONE / errors=remount-ro,relatime,rw / Block Size: 4096- Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa008204- 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_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: disabled RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

svc05_hpc_run_1_23-09-22ior: 2MB - Default Test Directoryior: 4MB - Default Test Directoryior: 8MB - Default Test Directoryior: 16MB - Default Test Directoryior: 32MB - Default Test Directoryior: 64MB - Default Test Directoryior: 256MB - Default Test Directoryior: 512MB - Default Test Directoryior: 1024MB - Default Test Directoryhpcg: 104 104 104 - 60hpcg: 144 144 144 - 60hpcg: 160 160 160 - 60hpcg: 104 104 104 - 1800hpcg: 144 144 144 - 1800hpcg: 160 160 160 - 1800hpl: npb: BT.Cnpb: CG.Cnpb: EP.Cnpb: EP.Dnpb: FT.Cnpb: IS.Dnpb: LU.Cnpb: MG.Cnpb: SP.Bnpb: SP.Chpcc: G-HPLhpcc: G-Fftehpcc: EP-DGEMMhpcc: G-Ptranshpcc: EP-STREAM Triadhpcc: G-Rand Accesshpcc: Rand Ring Latencyhpcc: Rand Ring Bandwidthhpcc: Max Ping Pong Bandwidthlczero: BLASparboil: OpenMP LBMparboil: OpenMP CUTCPparboil: OpenMP Stencilparboil: OpenMP MRI Griddingminife: Smallminibude: OpenMP - BM1minibude: OpenMP - BM1minibude: OpenMP - BM2minibude: OpenMP - BM2cloverleaf: Lagrangian-Eulerian Hydrodynamicsrodinia: OpenMP LavaMDrodinia: OpenMP Leukocyterodinia: OpenMP CFD Solverrodinia: OpenMP Streamclustercp2k: H20-64cp2k: Fayalite-FISTnamd: ATPase Simulation - 327,506 Atomsdolfyn: Computational Fluid Dynamicsneat: amg: libxsmm: 128libxsmm: 256libxsmm: 32libxsmm: 64ffte: N=256, 1D Complex FFT Routinelaghos: Triple Point Problemlaghos: Sedov Blast Wave, ube_922_hex.meshfftw: Stock - 1D FFT Size 32fftw: Stock - 2D FFT Size 32fftw: Stock - 1D FFT Size 4096fftw: Stock - 2D FFT Size 4096fftw: Float + SSE - 1D FFT Size 32fftw: Float + SSE - 2D FFT Size 32fftw: Float + SSE - 1D FFT Size 4096fftw: Float + SSE - 2D FFT Size 4096heffte: c2c - FFTW - float - 128heffte: c2c - FFTW - float - 256heffte: c2c - FFTW - float - 512heffte: r2c - FFTW - float - 128heffte: r2c - FFTW - float - 256heffte: r2c - FFTW - float - 512heffte: c2c - FFTW - double - 128heffte: c2c - FFTW - double - 256heffte: c2c - FFTW - double - 512heffte: c2c - Stock - float - 128heffte: c2c - Stock - float - 256heffte: c2c - Stock - float - 512heffte: r2c - FFTW - double - 128heffte: r2c - FFTW - double - 256heffte: r2c - FFTW - double - 512heffte: r2c - Stock - float - 128heffte: r2c - Stock - float - 256heffte: r2c - Stock - float - 512heffte: c2c - Stock - double - 128heffte: c2c - Stock - double - 256heffte: c2c - Stock - double - 512heffte: r2c - Stock - double - 128heffte: r2c - Stock - double - 256heffte: r2c - Stock - double - 512heffte: c2c - FFTW - float-long - 128heffte: c2c - FFTW - float-long - 256heffte: c2c - FFTW - float-long - 512heffte: r2c - FFTW - float-long - 128heffte: r2c - FFTW - float-long - 256heffte: r2c - FFTW - float-long - 512heffte: c2c - FFTW - double-long - 128heffte: c2c - FFTW - double-long - 256heffte: c2c - FFTW - double-long - 512heffte: c2c - Stock - float-long - 128heffte: c2c - Stock - float-long - 256heffte: c2c - Stock - float-long - 512heffte: r2c - FFTW - double-long - 128heffte: r2c - FFTW - double-long - 256heffte: r2c - FFTW - double-long - 512heffte: r2c - Stock - float-long - 128heffte: r2c - Stock - float-long - 256heffte: r2c - Stock - float-long - 512heffte: c2c - Stock - double-long - 128heffte: c2c - Stock - double-long - 256heffte: c2c - Stock - double-long - 512heffte: r2c - Stock - double-long - 128heffte: r2c - Stock - double-long - 256heffte: r2c - Stock - double-long - 512pennant: sedovbigpennant: leblancbigpalabos: 100palabos: 400palabos: 500palabos: 1000mrbayes: Primate Phylogeny Analysisnwchem: C240 Buckyballqmcpack: Li2_STO_aeqmcpack: simple-H2Oqmcpack: FeCO6_b3lyp_gmsqmcpack: FeCO6_b3lyp_gmshmmer: Pfam Database Searchincompact3d: X3D-benchmarking input.i3dincompact3d: input.i3d 129 Cells Per Directionincompact3d: input.i3d 193 Cells Per Directionmafft: Multiple Sequence Alignment - LSU RNAmocassin: Dust 2D tau100.0openfoam: motorBike - Mesh Timeopenfoam: motorBike - Execution Timeopenfoam: drivaerFastback, Large Mesh Size - Mesh Timeopenfoam: drivaerFastback, Large Mesh Size - Execution Timeopenfoam: drivaerFastback, Small Mesh Size - Mesh Timeopenfoam: drivaerFastback, Small Mesh Size - Execution Timeopenfoam: drivaerFastback, Medium Mesh Size - Mesh Timeopenfoam: drivaerFastback, Medium Mesh Size - Execution Timeopenradioss: Bumper Beamopenradioss: Chrysler Neon 1Mopenradioss: Cell Phone Drop Testopenradioss: Bird Strike on Windshieldopenradioss: Rubber O-Ring Seal Installationqe: AUSURF112relion: Basic - CPUremhos: Sample Remap Examplespecfem3d: Mount St. Helensspecfem3d: Layered Halfspacespecfem3d: Tomographic Modelspecfem3d: Homogeneous Halfspacespecfem3d: Water-layered Halfspacenekrs: Kershawnekrs: TurboPipe Periodiclammps: 20k Atomslammps: Rhodopsin Proteinlulesh: arrayfire: BLAS CPUmt-dgemm: Sustained Floating-Point Ratehimeno: Poisson Pressure Solveronednn: IP Shapes 1D - f32 - CPUonednn: IP Shapes 3D - f32 - CPUonednn: IP Shapes 1D - u8s8f32 - CPUonednn: IP Shapes 3D - u8s8f32 - CPUonednn: Convolution Batch Shapes Auto - f32 - CPUonednn: Deconvolution Batch shapes_1d - f32 - CPUonednn: Deconvolution Batch shapes_3d - f32 - CPUonednn: Convolution Batch Shapes Auto - u8s8f32 - CPUonednn: Deconvolution Batch shapes_1d - u8s8f32 - CPUonednn: Deconvolution Batch shapes_3d - u8s8f32 - CPUonednn: Recurrent Neural Network Training - f32 - CPUonednn: Recurrent Neural Network Inference - f32 - CPUonednn: Recurrent Neural Network Training - u8s8f32 - CPUonednn: Recurrent Neural Network Inference - u8s8f32 - CPUonednn: Recurrent Neural Network Training - bf16bf16bf16 - CPUonednn: Recurrent Neural Network Inference - bf16bf16bf16 - CPUnumpy: deepspeech: CPUrbenchmark: rnnoise: askap: tConvolve MT - Griddingaskap: tConvolve MT - Degriddingaskap: tConvolve MPI - Degriddingaskap: tConvolve MPI - Griddingaskap: tConvolve OpenMP - Griddingaskap: tConvolve OpenMP - Degriddingaskap: Hogbom Clean OpenMPgraph500: 26graph500: 26graph500: 26graph500: 26intel-mpi: IMB-P2P PingPongintel-mpi: IMB-MPI1 Exchangeintel-mpi: IMB-MPI1 Exchangeintel-mpi: IMB-MPI1 PingPongintel-mpi: IMB-MPI1 Sendrecvintel-mpi: IMB-MPI1 Sendrecvgromacs: MPI CPU - water_GMX50_baredaphne: OpenMP - NDT Mappingdaphne: OpenMP - Points2Imagedaphne: OpenMP - Euclidean Clustertensorflow-lite: SqueezeNettensorflow-lite: Inception V4tensorflow-lite: NASNet Mobiletensorflow-lite: Mobilenet Floattensorflow-lite: Mobilenet Quanttensorflow-lite: Inception ResNet V2tensorflow: CPU - 16 - VGG-16tensorflow: CPU - 32 - VGG-16tensorflow: CPU - 64 - VGG-16tensorflow: CPU - 16 - AlexNettensorflow: CPU - 256 - VGG-16tensorflow: CPU - 32 - AlexNettensorflow: CPU - 512 - VGG-16tensorflow: CPU - 64 - AlexNettensorflow: CPU - 256 - AlexNettensorflow: CPU - 512 - AlexNettensorflow: CPU - 16 - GoogLeNettensorflow: CPU - 16 - ResNet-50tensorflow: CPU - 32 - GoogLeNettensorflow: CPU - 32 - ResNet-50tensorflow: CPU - 64 - GoogLeNettensorflow: CPU - 64 - ResNet-50tensorflow: CPU - 256 - GoogLeNettensorflow: CPU - 256 - ResNet-50tensorflow: CPU - 512 - GoogLeNettensorflow: CPU - 512 - ResNet-50octave-benchmark: deepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Streamdeepsparse: NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Streamdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Streamdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Streamdeepsparse: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Streamdeepsparse: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Streamdeepsparse: ResNet-50, Baseline - Asynchronous Multi-Streamdeepsparse: ResNet-50, Baseline - Asynchronous Multi-Streamdeepsparse: ResNet-50, Baseline - Synchronous Single-Streamdeepsparse: ResNet-50, Baseline - Synchronous Single-Streamdeepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: ResNet-50, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Streamdeepsparse: ResNet-50, Sparse INT8 - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering - Synchronous Single-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Streamdeepsparse: CV Classification, ResNet-50 ImageNet - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Streamdeepsparse: CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Streamdeepsparse: NLP Text Classification, DistilBERT mnli - Synchronous Single-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Streamdeepsparse: CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Streamdeepsparse: BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Streamdeepsparse: NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Streamdeepsparse: NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Streamspacy: en_core_web_lgspacy: en_core_web_trfcaffe: AlexNet - CPU - 100caffe: AlexNet - CPU - 200caffe: AlexNet - CPU - 1000caffe: GoogleNet - CPU - 100caffe: GoogleNet - CPU - 200caffe: GoogleNet - CPU - 1000wrf: conus 2.5kmgpaw: Carbon Nanotubemnn: nasnetmnn: mobilenetV3mnn: squeezenetv1.1mnn: resnet-v2-50mnn: SqueezeNetV1.0mnn: MobileNetV2_224mnn: mobilenet-v1-1.0mnn: inception-v3ncnn: CPU - mobilenetncnn: CPU-v2-v2 - mobilenet-v2ncnn: CPU-v3-v3 - mobilenet-v3ncnn: CPU - shufflenet-v2ncnn: CPU - mnasnetncnn: CPU - efficientnet-b0ncnn: CPU - blazefacencnn: CPU - googlenetncnn: CPU - vgg16ncnn: CPU - resnet18ncnn: CPU - alexnetncnn: CPU - resnet50ncnn: CPU - yolov4-tinyncnn: CPU - squeezenet_ssdncnn: CPU - regnety_400mncnn: CPU - vision_transformerncnn: CPU - FastestDetncnn: Vulkan GPU - mobilenetncnn: Vulkan GPU-v2-v2 - mobilenet-v2ncnn: Vulkan GPU-v3-v3 - mobilenet-v3ncnn: Vulkan GPU - shufflenet-v2ncnn: Vulkan GPU - mnasnetncnn: Vulkan GPU - efficientnet-b0ncnn: Vulkan GPU - blazefacencnn: Vulkan GPU - googlenetncnn: Vulkan GPU - vgg16ncnn: Vulkan GPU - resnet18ncnn: Vulkan GPU - alexnetncnn: Vulkan GPU - resnet50ncnn: Vulkan GPU - yolov4-tinyncnn: Vulkan GPU - squeezenet_ssdncnn: Vulkan GPU - regnety_400mncnn: Vulkan GPU - vision_transformerncnn: Vulkan GPU - FastestDettnn: CPU - DenseNettnn: CPU - MobileNet v2tnn: CPU - SqueezeNet v2tnn: CPU - SqueezeNet v1.1openvino: Face Detection FP16 - CPUopenvino: Face Detection FP16 - CPUopenvino: Person Detection FP16 - CPUopenvino: Person Detection FP16 - CPUopenvino: Person Detection FP32 - CPUopenvino: Person Detection FP32 - CPUopenvino: Vehicle Detection FP16 - CPUopenvino: Vehicle Detection FP16 - CPUopenvino: Face Detection FP16-INT8 - CPUopenvino: Face Detection FP16-INT8 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Vehicle Detection FP16-INT8 - CPUopenvino: Weld Porosity Detection FP16 - CPUopenvino: Weld Porosity Detection FP16 - CPUopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Machine Translation EN To DE FP16 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Weld Porosity Detection FP16-INT8 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUopenvino: Person Vehicle Bike Detection FP16 - CPUopenvino: Age Gender Recognition Retail 0013 FP16 - CPUopenvino: Age Gender Recognition Retail 0013 FP16 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUopenvino: Age Gender Recognition Retail 0013 FP16-INT8 - CPUpetsc: Streamsnumenta-nab: KNN CADnumenta-nab: Relative Entropynumenta-nab: Windowed Gaussiannumenta-nab: Earthgecko Skylinenumenta-nab: Bayesian Changepointnumenta-nab: Contextual Anomaly Detector OSEai-benchmark: Device Inference Scoreai-benchmark: Device Training Scoreai-benchmark: Device AI Scorefaiss: bench_polysemous_sift1m - PQ baselinefaiss: bench_polysemous_sift1m - Polysemous 64faiss: bench_polysemous_sift1m - Polysemous 62faiss: bench_polysemous_sift1m - Polysemous 58faiss: bench_polysemous_sift1m - Polysemous 54faiss: bench_polysemous_sift1m - Polysemous 50faiss: bench_polysemous_sift1m - Polysemous 46faiss: bench_polysemous_sift1m - Polysemous 42faiss: bench_polysemous_sift1m - Polysemous 38faiss: bench_polysemous_sift1m - Polysemous 34faiss: bench_polysemous_sift1m - Polysemous 30mlpack: scikit_icamlpack: scikit_qdamlpack: scikit_svmmlpack: scikit_linearridgeregressionpyhpc: CPU - Numpy - 16384 - Equation of Statepyhpc: CPU - Numpy - 16384 - Isoneutral Mixingpyhpc: CPU - Numpy - 65536 - Equation of Statepyhpc: CPU - Numpy - 65536 - Isoneutral Mixingpyhpc: CPU - Numpy - 262144 - Equation of Statepyhpc: CPU - Numpy - 262144 - Isoneutral Mixingpyhpc: CPU - Numpy - 1048576 - Equation of Statepyhpc: CPU - Numpy - 1048576 - Isoneutral Mixingpyhpc: CPU - Numpy - 4194304 - Equation of Statepyhpc: CPU - Numpy - 4194304 - Isoneutral Mixingscikit-learn: Sparsifyscikit-learn: Text Vectorizersscikit-learn: Covertype Dataset Benchmarkscikit-learn: 20 Newsgroups / Logistic Regressionscikit-learn: Sparse Rand Projections / 100 Iterationswhisper-cpp: ggml-base.en - 2016 State of the Unionwhisper-cpp: ggml-small.en - 2016 State of the Unionwhisper-cpp: ggml-medium.en - 2016 State of the Unionkripke: opencv: DNN - Deep Neural NetworkSamsung SSD 980 PRO 1TB665.75645.60813.53933.081009.38974.40907.561059.041489.0619.129318.916418.903318.887718.900918.9001103.69132969.6023866.926255.956568.4462960.512718.02151979.3657204.7493331.7050933.69108.1036729.0544030.0044014.534531.793680.354270.904091.8004827044.453169116.9891120.7219693.347907197.26088521660.42705.093108.2042775.617111.0258.4736.67030.9175.8814.95124.93899.4540.3405515.42227.93010014506671116.11438.6309.6628.0307873.59869846267.50484.3011256114829825.07141.517601475986106728271104.34275.741853.8760191.325173.615100.40459.057527.970527.667392.221682.944854.6569104.47864.754950.3565169.960183.582108.91551.102728.791527.731094.851773.448654.3443104.96075.703053.9035192.589173.835100.73159.072527.914227.704891.061382.969554.7613103.85464.709750.4685170.180180.952108.97151.176628.947327.796593.732573.116654.37447.2545774.009406384.377270.058284.807320.655103.3141753.693.57225.369179.15181.92100.337590.1790365.0188601821.43938648.682178.75636.418959.019752.3381914665.88220.35659941.035709114.81681611.5211261.03278.6022.15105.0856.11265.43413.00612.7809.10976764425.4221842099.02993891911.44510515822.9590261094669710000355817666735.67736.81020397.4831761.2420.6771274580.3817010.9394191.813440.6929440.2998450.6528563.285851.565621.700380.8576560.5200111237.80424.1621255.04416.8061253.69419.345538.4770.415400.121718.2355441.328148.1025499.327028.99740.418186.03603.656733923000748649000286299000367742000365383437897.23135.456060.625133.4975.267.0441401.1925162.1997104721381.662165.5921802.619693.51485.941519.5927862.99.349.9010.22129.3310.55157.3610.60175.72194.81201.6876.3225.2485.5927.3182.0127.3483.5028.6684.0829.536.00050.5980623.589024.735240.42231405.235722.6529217.92804.5868533.086359.708699.98119.9946157.9105201.260845.003922.2117620.278351.1578222.04584.50114954.46336.4127893.91961.1157291.7761108.7560159.40546.269961.9463511.291029.890933.4481617.888951.3821223.33894.4754299.7655105.8412161.23296.2004438.929072.3802113.37528.814565.0980485.267834.349629.0985746.714642.6437114.61138.7222223.1894142.339263.029815.859750.7158622.407824.765340.373913673398128045558852782027665415297276573715397.72759.10210.5271.6762.86114.6134.7523.0242.02716.78910.244.294.045.413.886.332.2810.2319.356.163.6611.3715.699.5117.5937.145.9510.224.254.044.983.886.302.1310.2319.606.243.7211.4515.699.6015.7637.145.902499.477246.03560.433250.49015.391033.1810.161564.6510.111572.801432.3511.1636.96432.402524.356.331542.3910.36159.15100.453764.5916.991806.678.8550822.471.2554311.931.17118164.411774.30410.5623.84259.35428.31434.9372967200049673.7495.9564.8973.0291.9071.2280.8730.7280.6810.6650.65932.9625.1519.510.880.0020.0090.0120.0320.0440.1160.1620.4730.9521.97595.42458.804347.99230.830478.562208.24901475.98140959.4728531588126724131OpenBenchmarking.org

IOR

IOR is a parallel I/O storage benchmark making use of MPI with a particular focus on HPC (High Performance Computing) systems. IOR is developed at the Lawrence Livermore National Laboratory (LLNL). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterIOR 3.3.0Block Size: 2MB - Disk Target: Default Test DirectorySamsung SSD 980 PRO 1TB140280420560700SE +/- 7.30, N = 15665.75MIN: 304.4 / MAX: 1664.381. (CC) gcc options: -O2 -lm -lmpi

OpenBenchmarking.orgMB/s, More Is BetterIOR 3.3.0Block Size: 4MB - Disk Target: Default Test DirectorySamsung SSD 980 PRO 1TB140280420560700SE +/- 13.18, N = 15645.60MIN: 225.55 / MAX: 1839.221. (CC) gcc options: -O2 -lm -lmpi

OpenBenchmarking.orgMB/s, More Is BetterIOR 3.3.0Block Size: 8MB - Disk Target: Default Test DirectorySamsung SSD 980 PRO 1TB2004006008001000SE +/- 5.77, N = 12813.53MIN: 364.4 / MAX: 2104.521. (CC) gcc options: -O2 -lm -lmpi

OpenBenchmarking.orgMB/s, More Is BetterIOR 3.3.0Block Size: 16MB - Disk Target: Default Test DirectorySamsung SSD 980 PRO 1TB2004006008001000SE +/- 6.79, N = 3933.08MIN: 504.16 / MAX: 2148.311. (CC) gcc options: -O2 -lm -lmpi

OpenBenchmarking.orgMB/s, More Is BetterIOR 3.3.0Block Size: 32MB - Disk Target: Default Test DirectorySamsung SSD 980 PRO 1TB2004006008001000SE +/- 13.63, N = 121009.38MIN: 480 / MAX: 2073.61. (CC) gcc options: -O2 -lm -lmpi

OpenBenchmarking.orgMB/s, More Is BetterIOR 3.3.0Block Size: 64MB - Disk Target: Default Test DirectorySamsung SSD 980 PRO 1TB2004006008001000SE +/- 0.79, N = 3974.40MIN: 769.69 / MAX: 1622.261. (CC) gcc options: -O2 -lm -lmpi

OpenBenchmarking.orgMB/s, More Is BetterIOR 3.3.0Block Size: 256MB - Disk Target: Default Test DirectorySamsung SSD 980 PRO 1TB2004006008001000SE +/- 0.79, N = 3907.56MIN: 854.73 / MAX: 1182.561. (CC) gcc options: -O2 -lm -lmpi

OpenBenchmarking.orgMB/s, More Is BetterIOR 3.3.0Block Size: 512MB - Disk Target: Default Test DirectorySamsung SSD 980 PRO 1TB2004006008001000SE +/- 1.91, N = 31059.04MIN: 985.02 / MAX: 1145.21. (CC) gcc options: -O2 -lm -lmpi

OpenBenchmarking.orgMB/s, More Is BetterIOR 3.3.0Block Size: 1024MB - Disk Target: Default Test DirectorySamsung SSD 980 PRO 1TB30060090012001500SE +/- 10.44, N = 31489.06MIN: 1347.42 / MAX: 1565.791. (CC) gcc options: -O2 -lm -lmpi

High Performance Conjugate Gradient

HPCG is the High Performance Conjugate Gradient and is a new scientific benchmark from Sandia National Lans focused for super-computer testing with modern real-world workloads compared to HPCC. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOP/s, More Is BetterHigh Performance Conjugate Gradient 3.1X Y Z: 104 104 104 - RT: 60Samsung SSD 980 PRO 1TB510152025SE +/- 0.01, N = 319.131. (CXX) g++ options: -O3 -ffast-math -ftree-vectorize -lmpi_cxx -lmpi

OpenBenchmarking.orgGFLOP/s, More Is BetterHigh Performance Conjugate Gradient 3.1X Y Z: 144 144 144 - RT: 60Samsung SSD 980 PRO 1TB510152025SE +/- 0.01, N = 318.921. (CXX) g++ options: -O3 -ffast-math -ftree-vectorize -lmpi_cxx -lmpi

OpenBenchmarking.orgGFLOP/s, More Is BetterHigh Performance Conjugate Gradient 3.1X Y Z: 160 160 160 - RT: 60Samsung SSD 980 PRO 1TB510152025SE +/- 0.00, N = 318.901. (CXX) g++ options: -O3 -ffast-math -ftree-vectorize -lmpi_cxx -lmpi

X Y Z: 192 192 192 - RT: 60

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: cat: 'HPCG-Benchmark*.txt': No such file or directory

OpenBenchmarking.orgGFLOP/s, More Is BetterHigh Performance Conjugate Gradient 3.1X Y Z: 104 104 104 - RT: 1800Samsung SSD 980 PRO 1TB510152025SE +/- 0.22, N = 318.891. (CXX) g++ options: -O3 -ffast-math -ftree-vectorize -lmpi_cxx -lmpi

OpenBenchmarking.orgGFLOP/s, More Is BetterHigh Performance Conjugate Gradient 3.1X Y Z: 144 144 144 - RT: 1800Samsung SSD 980 PRO 1TB510152025SE +/- 0.01, N = 318.901. (CXX) g++ options: -O3 -ffast-math -ftree-vectorize -lmpi_cxx -lmpi

OpenBenchmarking.orgGFLOP/s, More Is BetterHigh Performance Conjugate Gradient 3.1X Y Z: 160 160 160 - RT: 1800Samsung SSD 980 PRO 1TB510152025SE +/- 0.00, N = 318.901. (CXX) g++ options: -O3 -ffast-math -ftree-vectorize -lmpi_cxx -lmpi

X Y Z: 192 192 192 - RT: 1800

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: cat: 'HPCG-Benchmark*.txt': No such file or directory

HPL Linpack

HPL is a well known portable Linpack implementation for distributed memory systems. This test profile is testing HPL upstream directly, outside the scope of the HPC Challenge test profile also available through the Phoronix Test Suite (hpcc). The test profile attempts to generate an optimized HPL.dat input file based on the CPU/memory under test. The automated HPL.dat input generation is still being tuned and thus for now this test profile remains "experimental". Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOPS, More Is BetterHPL Linpack 2.3Samsung SSD 980 PRO 1TB20406080100SE +/- 0.04, N = 3103.691. (CC) gcc options: -O2 -lopenblas -lm -lmpi

NAS Parallel Benchmarks

NPB, NAS Parallel Benchmarks, is a benchmark developed by NASA for high-end computer systems. This test profile currently uses the MPI version of NPB. This test profile offers selecting the different NPB tests/problems and varying problem sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: BT.CSamsung SSD 980 PRO 1TB30K60K90K120K150KSE +/- 357.94, N = 3132969.601. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: CG.CSamsung SSD 980 PRO 1TB5K10K15K20K25KSE +/- 261.54, N = 323866.921. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: EP.CSamsung SSD 980 PRO 1TB13002600390052006500SE +/- 154.00, N = 156255.951. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: EP.DSamsung SSD 980 PRO 1TB14002800420056007000SE +/- 94.18, N = 156568.441. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: FT.CSamsung SSD 980 PRO 1TB13K26K39K52K65KSE +/- 108.01, N = 362960.511. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: IS.DSamsung SSD 980 PRO 1TB6001200180024003000SE +/- 20.08, N = 32718.021. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: LU.CSamsung SSD 980 PRO 1TB30K60K90K120K150KSE +/- 379.20, N = 3151979.361. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: MG.CSamsung SSD 980 PRO 1TB12K24K36K48K60KSE +/- 66.98, N = 357204.741. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.BSamsung SSD 980 PRO 1TB20K40K60K80K100KSE +/- 236.71, N = 393331.701. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2

OpenBenchmarking.orgTotal Mop/s, More Is BetterNAS Parallel Benchmarks 3.4Test / Class: SP.CSamsung SSD 980 PRO 1TB11K22K33K44K55KSE +/- 258.86, N = 350933.691. (F9X) gfortran options: -O3 -march=native -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz 2. Open MPI 4.1.2

HPC Challenge

HPC Challenge (HPCC) is a cluster-focused benchmark consisting of the HPL Linpack TPP benchmark, DGEMM, STREAM, PTRANS, RandomAccess, FFT, and communication bandwidth and latency. This HPC Challenge test profile attempts to ship with standard yet versatile configuration/input files though they can be modified. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOPS, More Is BetterHPC Challenge 1.5.0Test / Class: G-HPLSamsung SSD 980 PRO 1TB20406080100SE +/- 2.45, N = 3108.101. (CC) gcc options: -lblas -lm -lmpi -fomit-frame-pointer -funroll-loops2. ATLAS + Open MPI 4.1.2

OpenBenchmarking.orgGFLOPS, More Is BetterHPC Challenge 1.5.0Test / Class: G-FfteSamsung SSD 980 PRO 1TB714212835SE +/- 0.21, N = 329.051. (CC) gcc options: -lblas -lm -lmpi -fomit-frame-pointer -funroll-loops2. ATLAS + Open MPI 4.1.2

OpenBenchmarking.orgGFLOPS, More Is BetterHPC Challenge 1.5.0Test / Class: EP-DGEMMSamsung SSD 980 PRO 1TB714212835SE +/- 1.20, N = 330.001. (CC) gcc options: -lblas -lm -lmpi -fomit-frame-pointer -funroll-loops2. ATLAS + Open MPI 4.1.2

OpenBenchmarking.orgGB/s, More Is BetterHPC Challenge 1.5.0Test / Class: G-PtransSamsung SSD 980 PRO 1TB48121620SE +/- 0.05, N = 314.531. (CC) gcc options: -lblas -lm -lmpi -fomit-frame-pointer -funroll-loops2. ATLAS + Open MPI 4.1.2

OpenBenchmarking.orgGB/s, More Is BetterHPC Challenge 1.5.0Test / Class: EP-STREAM TriadSamsung SSD 980 PRO 1TB0.40360.80721.21081.61442.018SE +/- 0.00220, N = 31.793681. (CC) gcc options: -lblas -lm -lmpi -fomit-frame-pointer -funroll-loops2. ATLAS + Open MPI 4.1.2

OpenBenchmarking.orgGUP/s, More Is BetterHPC Challenge 1.5.0Test / Class: G-Random AccessSamsung SSD 980 PRO 1TB0.07970.15940.23910.31880.3985SE +/- 0.01763, N = 30.354271. (CC) gcc options: -lblas -lm -lmpi -fomit-frame-pointer -funroll-loops2. ATLAS + Open MPI 4.1.2

OpenBenchmarking.orgusecs, Fewer Is BetterHPC Challenge 1.5.0Test / Class: Random Ring LatencySamsung SSD 980 PRO 1TB0.20340.40680.61020.81361.017SE +/- 0.00270, N = 30.904091. (CC) gcc options: -lblas -lm -lmpi -fomit-frame-pointer -funroll-loops2. ATLAS + Open MPI 4.1.2

OpenBenchmarking.orgGB/s, More Is BetterHPC Challenge 1.5.0Test / Class: Random Ring BandwidthSamsung SSD 980 PRO 1TB0.40510.81021.21531.62042.0255SE +/- 0.00539, N = 31.800481. (CC) gcc options: -lblas -lm -lmpi -fomit-frame-pointer -funroll-loops2. ATLAS + Open MPI 4.1.2

OpenBenchmarking.orgMB/s, More Is BetterHPC Challenge 1.5.0Test / Class: Max Ping Pong BandwidthSamsung SSD 980 PRO 1TB6K12K18K24K30KSE +/- 309.71, N = 327044.451. (CC) gcc options: -lblas -lm -lmpi -fomit-frame-pointer -funroll-loops2. ATLAS + Open MPI 4.1.2

LeelaChessZero

LeelaChessZero (lc0 / lczero) is a chess engine automated vian neural networks. This test profile can be used for OpenCL, CUDA + cuDNN, and BLAS (CPU-based) benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNodes Per Second, More Is BetterLeelaChessZero 0.28Backend: BLASSamsung SSD 980 PRO 1TB400800120016002000SE +/- 14.81, N = 816911. (CXX) g++ options: -flto -pthread

Parboil

The Parboil Benchmarks from the IMPACT Research Group at University of Illinois are a set of throughput computing applications for looking at computing architecture and compilers. Parboil test-cases support OpenMP, OpenCL, and CUDA multi-processing environments. However, at this time the test profile is just making use of the OpenMP and OpenCL test workloads. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterParboil 2.5Test: OpenMP LBMSamsung SSD 980 PRO 1TB48121620SE +/- 0.16, N = 316.991. (CXX) g++ options: -lm -lpthread -lgomp -O3 -ffast-math -fopenmp

OpenBenchmarking.orgSeconds, Fewer Is BetterParboil 2.5Test: OpenMP CUTCPSamsung SSD 980 PRO 1TB0.16240.32480.48720.64960.812SE +/- 0.008966, N = 40.7219691. (CXX) g++ options: -lm -lpthread -lgomp -O3 -ffast-math -fopenmp

Test: OpenMP MRI-Q

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: main.c:(.text.startup+0x20c): undefined reference to `ComputeQCPU'

OpenBenchmarking.orgSeconds, Fewer Is BetterParboil 2.5Test: OpenMP StencilSamsung SSD 980 PRO 1TB0.75331.50662.25993.01323.7665SE +/- 0.022641, N = 133.3479071. (CXX) g++ options: -lm -lpthread -lgomp -O3 -ffast-math -fopenmp

OpenBenchmarking.orgSeconds, Fewer Is BetterParboil 2.5Test: OpenMP MRI GriddingSamsung SSD 980 PRO 1TB4080120160200SE +/- 0.85, N = 3197.261. (CXX) g++ options: -lm -lpthread -lgomp -O3 -ffast-math -fopenmp

miniFE

MiniFE Finite Element is an application for unstructured implicit finite element codes. Learn more via the OpenBenchmarking.org test page.

Problem Size: Large

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: cat: '*.yaml': No such file or directory

OpenBenchmarking.orgCG Mflops, More Is BetterminiFE 2.2Problem Size: SmallSamsung SSD 980 PRO 1TB5K10K15K20K25KSE +/- 38.34, N = 321660.41. (CXX) g++ options: -O3 -fopenmp -lmpi_cxx -lmpi

Problem Size: Medium

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: on the y==0 face (ix=2112, iz=1984), ERROR: found negative row (-99) for nodeID=-131072

miniBUDE

MiniBUDE is a mini application for the the core computation of the Bristol University Docking Engine (BUDE). This test profile currently makes use of the OpenMP implementation of miniBUDE for CPU benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFInst/s, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM1Samsung SSD 980 PRO 1TB6001200180024003000SE +/- 1.53, N = 32705.091. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm

OpenBenchmarking.orgBillion Interactions/s, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM1Samsung SSD 980 PRO 1TB20406080100SE +/- 0.06, N = 3108.201. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm

OpenBenchmarking.orgGFInst/s, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM2Samsung SSD 980 PRO 1TB6001200180024003000SE +/- 8.88, N = 32775.621. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm

OpenBenchmarking.orgBillion Interactions/s, More Is BetterminiBUDE 20210901Implementation: OpenMP - Input Deck: BM2Samsung SSD 980 PRO 1TB20406080100SE +/- 0.36, N = 3111.031. (CC) gcc options: -std=c99 -Ofast -ffast-math -fopenmp -march=native -lm

CloverLeaf

CloverLeaf is a Lagrangian-Eulerian hydrodynamics benchmark. This test profile currently makes use of CloverLeaf's OpenMP version and benchmarked with the clover_bm.in input file (Problem 5). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterCloverLeafLagrangian-Eulerian HydrodynamicsSamsung SSD 980 PRO 1TB246810SE +/- 0.02, N = 38.471. (F9X) gfortran options: -O3 -march=native -funroll-loops -fopenmp

Rodinia

Rodinia is a suite focused upon accelerating compute-intensive applications with accelerators. CUDA, OpenMP, and OpenCL parallel models are supported by the included applications. This profile utilizes select OpenCL, NVIDIA CUDA and OpenMP test binaries at the moment. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRodinia 3.1Test: OpenMP LavaMDSamsung SSD 980 PRO 1TB816243240SE +/- 0.30, N = 336.671. (CXX) g++ options: -O2 -lOpenCL

OpenBenchmarking.orgSeconds, Fewer Is BetterRodinia 3.1Test: OpenMP LeukocyteSamsung SSD 980 PRO 1TB714212835SE +/- 0.16, N = 330.921. (CXX) g++ options: -O2 -lOpenCL

OpenBenchmarking.orgSeconds, Fewer Is BetterRodinia 3.1Test: OpenMP CFD SolverSamsung SSD 980 PRO 1TB1.32322.64643.96965.29286.616SE +/- 0.026, N = 35.8811. (CXX) g++ options: -O2 -lOpenCL

OpenBenchmarking.orgSeconds, Fewer Is BetterRodinia 3.1Test: OpenMP StreamclusterSamsung SSD 980 PRO 1TB1.1142.2283.3424.4565.57SE +/- 0.003, N = 34.9511. (CXX) g++ options: -O2 -lOpenCL

CP2K Molecular Dynamics

CP2K is an open-source molecular dynamics software package focused on quantum chemistry and solid-state physics. More details on the CP2K benchmark test cases and details can be found @ https://www.cp2k.org/performance Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterCP2K Molecular Dynamics 2023.1Input: H20-64Samsung SSD 980 PRO 1TB61218243024.941. (F9X) gfortran options: -fopenmp -mtune=native -O3 -funroll-loops -fbacktrace -ffree-form -fimplicit-none -std=f2008 -lcp2kstart -lcp2kmc -lcp2kswarm -lcp2kmotion -lcp2kthermostat -lcp2kemd -lcp2ktmc -lcp2kmain -lcp2kdbt -lcp2ktas -lcp2kdbm -lcp2kgrid -lcp2kgridcpu -lcp2kgridref -lcp2kgridcommon -ldbcsrarnoldi -ldbcsrx -lcp2kshg_int -lcp2keri_mme -lcp2kminimax -lcp2khfxbase -lcp2ksubsys -lcp2kxc -lcp2kao -lcp2kpw_env -lcp2kinput -lcp2kpw -lcp2kgpu -lcp2kfft -lcp2kfpga -lcp2kfm -lcp2kcommon -lcp2koffload -lcp2kmpiwrap -lcp2kbase -ldbcsr -lsirius -lspla -lspfft -lsymspg -lvdwxc -lhdf5 -lhdf5_hl -lz -lgsl -lelpa_openmp -lcosma -lcosta -lscalapack -lxsmmf -lxsmm -ldl -lpthread -lxcf03 -lxc -lint2 -lfftw3_mpi -lfftw3 -lfftw3_omp -lmpi_cxx -lmpi -lopenblas -lvori -lstdc++ -lmpi_usempif08 -lmpi_mpifh -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm

Input: H2O-DFT-LS

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. E: mpirun noticed that process rank 25 with PID 0 on node incooling-desktop exited on signal 9 (Killed).

OpenBenchmarking.orgSeconds, Fewer Is BetterCP2K Molecular Dynamics 2023.1Input: Fayalite-FISTSamsung SSD 980 PRO 1TB2040608010099.451. (F9X) gfortran options: -fopenmp -mtune=native -O3 -funroll-loops -fbacktrace -ffree-form -fimplicit-none -std=f2008 -lcp2kstart -lcp2kmc -lcp2kswarm -lcp2kmotion -lcp2kthermostat -lcp2kemd -lcp2ktmc -lcp2kmain -lcp2kdbt -lcp2ktas -lcp2kdbm -lcp2kgrid -lcp2kgridcpu -lcp2kgridref -lcp2kgridcommon -ldbcsrarnoldi -ldbcsrx -lcp2kshg_int -lcp2keri_mme -lcp2kminimax -lcp2khfxbase -lcp2ksubsys -lcp2kxc -lcp2kao -lcp2kpw_env -lcp2kinput -lcp2kpw -lcp2kgpu -lcp2kfft -lcp2kfpga -lcp2kfm -lcp2kcommon -lcp2koffload -lcp2kmpiwrap -lcp2kbase -ldbcsr -lsirius -lspla -lspfft -lsymspg -lvdwxc -lhdf5 -lhdf5_hl -lz -lgsl -lelpa_openmp -lcosma -lcosta -lscalapack -lxsmmf -lxsmm -ldl -lpthread -lxcf03 -lxc -lint2 -lfftw3_mpi -lfftw3 -lfftw3_omp -lmpi_cxx -lmpi -lopenblas -lvori -lstdc++ -lmpi_usempif08 -lmpi_mpifh -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm

NAMD

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD was developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgdays/ns, Fewer Is BetterNAMD 2.14ATPase Simulation - 327,506 AtomsSamsung SSD 980 PRO 1TB0.07660.15320.22980.30640.383SE +/- 0.00245, N = 30.34055

Dolfyn

Dolfyn is a Computational Fluid Dynamics (CFD) code of modern numerical simulation techniques. The Dolfyn test profile measures the execution time of the bundled computational fluid dynamics demos that are bundled with Dolfyn. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterDolfyn 0.527Computational Fluid DynamicsSamsung SSD 980 PRO 1TB48121620SE +/- 0.03, N = 315.42

Nebular Empirical Analysis Tool

NEAT is the Nebular Empirical Analysis Tool for empirical analysis of ionised nebulae, with uncertainty propagation. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNebular Empirical Analysis Tool 2.3Samsung SSD 980 PRO 1TB714212835SE +/- 0.04, N = 327.931. (F9X) gfortran options: -O3 -cpp -ffree-line-length-0 -Jsource/ -fopenmp -fno-backtrace -lcfitsio

Algebraic Multi-Grid Benchmark

AMG is a parallel algebraic multigrid solver for linear systems arising from problems on unstructured grids. The driver provided with AMG builds linear systems for various 3-dimensional problems. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgFigure Of Merit, More Is BetterAlgebraic Multi-Grid Benchmark 1.2Samsung SSD 980 PRO 1TB200M400M600M800M1000MSE +/- 401333.33, N = 310014506671. (CC) gcc options: -lparcsr_ls -lparcsr_mv -lseq_mv -lIJ_mv -lkrylov -lHYPRE_utilities -lm -fopenmp -lmpi

libxsmm

Libxsmm is an open-source library for specialized dense and sparse matrix operations and deep learning primitives. Libxsmm supports making use of Intel AMX, AVX-512, and other modern CPU instruction set capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOPS/s, More Is Betterlibxsmm 2-1.17-3645M N K: 128Samsung SSD 980 PRO 1TB2004006008001000SE +/- 8.10, N = 31116.11. (CXX) g++ options: -dynamic -Bstatic -static-libgcc -lgomp -lm -lrt -ldl -lquadmath -lstdc++ -pthread -fPIC -std=c++14 -O2 -fopenmp-simd -funroll-loops -ftree-vectorize -fdata-sections -ffunction-sections -fvisibility=hidden -msse4.2

OpenBenchmarking.orgGFLOPS/s, More Is Betterlibxsmm 2-1.17-3645M N K: 256Samsung SSD 980 PRO 1TB30060090012001500SE +/- 1.34, N = 31438.61. (CXX) g++ options: -dynamic -Bstatic -static-libgcc -lgomp -lm -lrt -ldl -lquadmath -lstdc++ -pthread -fPIC -std=c++14 -O2 -fopenmp-simd -funroll-loops -ftree-vectorize -fdata-sections -ffunction-sections -fvisibility=hidden -msse4.2

OpenBenchmarking.orgGFLOPS/s, More Is Betterlibxsmm 2-1.17-3645M N K: 32Samsung SSD 980 PRO 1TB70140210280350SE +/- 0.07, N = 3309.61. (CXX) g++ options: -dynamic -Bstatic -static-libgcc -lgomp -lm -lrt -ldl -lquadmath -lstdc++ -pthread -fPIC -std=c++14 -O2 -fopenmp-simd -funroll-loops -ftree-vectorize -fdata-sections -ffunction-sections -fvisibility=hidden -msse4.2

OpenBenchmarking.orgGFLOPS/s, More Is Betterlibxsmm 2-1.17-3645M N K: 64Samsung SSD 980 PRO 1TB140280420560700SE +/- 0.54, N = 3628.01. (CXX) g++ options: -dynamic -Bstatic -static-libgcc -lgomp -lm -lrt -ldl -lquadmath -lstdc++ -pthread -fPIC -std=c++14 -O2 -fopenmp-simd -funroll-loops -ftree-vectorize -fdata-sections -ffunction-sections -fvisibility=hidden -msse4.2

FFTE

FFTE is a package by Daisuke Takahashi to compute Discrete Fourier Transforms of 1-, 2- and 3- dimensional sequences of length (2^p)*(3^q)*(5^r). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMFLOPS, More Is BetterFFTE 7.0Test: N=256, 1D Complex FFT RoutineSamsung SSD 980 PRO 1TB70K140K210K280K350KSE +/- 2521.62, N = 3307873.601. (F9X) gfortran options: -O3 -fomit-frame-pointer -fopenmp

Laghos

Laghos (LAGrangian High-Order Solver) is a miniapp that solves the time-dependent Euler equations of compressible gas dynamics in a moving Lagrangian frame using unstructured high-order finite element spatial discretization and explicit high-order time-stepping. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMajor Kernels Total Rate, More Is BetterLaghos 3.1Test: Triple Point ProblemSamsung SSD 980 PRO 1TB60120180240300SE +/- 2.84, N = 3267.501. (CXX) g++ options: -O3 -std=c++11 -lmfem -lHYPRE -lmetis -lrt -lmpi_cxx -lmpi

OpenBenchmarking.orgMajor Kernels Total Rate, More Is BetterLaghos 3.1Test: Sedov Blast Wave, ube_922_hex.meshSamsung SSD 980 PRO 1TB100200300400500SE +/- 2.78, N = 3484.301. (CXX) g++ options: -O3 -std=c++11 -lmfem -lHYPRE -lmetis -lrt -lmpi_cxx -lmpi

FFTW

FFTW is a C subroutine library for computing the discrete Fourier transform (DFT) in one or more dimensions. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMflops, More Is BetterFFTW 3.3.6Build: Stock - Size: 1D FFT Size 32Samsung SSD 980 PRO 1TB2K4K6K8K10KSE +/- 50.60, N = 3112561. (CC) gcc options: -O3 -fomit-frame-pointer -mtune=native -malign-double -fstrict-aliasing -fno-schedule-insns -ffast-math -lm

OpenBenchmarking.orgMflops, More Is BetterFFTW 3.3.6Build: Stock - Size: 2D FFT Size 32Samsung SSD 980 PRO 1TB2K4K6K8K10KSE +/- 115.39, N = 3114821. (CC) gcc options: -O3 -fomit-frame-pointer -mtune=native -malign-double -fstrict-aliasing -fno-schedule-insns -ffast-math -lm

OpenBenchmarking.orgMflops, More Is BetterFFTW 3.3.6Build: Stock - Size: 1D FFT Size 4096Samsung SSD 980 PRO 1TB2K4K6K8K10KSE +/- 11.11, N = 39825.01. (CC) gcc options: -O3 -fomit-frame-pointer -mtune=native -malign-double -fstrict-aliasing -fno-schedule-insns -ffast-math -lm

OpenBenchmarking.orgMflops, More Is BetterFFTW 3.3.6Build: Stock - Size: 2D FFT Size 4096Samsung SSD 980 PRO 1TB15003000450060007500SE +/- 31.92, N = 37141.51. (CC) gcc options: -O3 -fomit-frame-pointer -mtune=native -malign-double -fstrict-aliasing -fno-schedule-insns -ffast-math -lm

OpenBenchmarking.orgMflops, More Is BetterFFTW 3.3.6Build: Float + SSE - Size: 1D FFT Size 32Samsung SSD 980 PRO 1TB4K8K12K16K20KSE +/- 85.83, N = 3176011. (CC) gcc options: -O3 -fomit-frame-pointer -mtune=native -malign-double -fstrict-aliasing -fno-schedule-insns -ffast-math -lm

OpenBenchmarking.orgMflops, More Is BetterFFTW 3.3.6Build: Float + SSE - Size: 2D FFT Size 32Samsung SSD 980 PRO 1TB10K20K30K40K50KSE +/- 525.31, N = 4475981. (CC) gcc options: -O3 -fomit-frame-pointer -mtune=native -malign-double -fstrict-aliasing -fno-schedule-insns -ffast-math -lm

OpenBenchmarking.orgMflops, More Is BetterFFTW 3.3.6Build: Float + SSE - Size: 1D FFT Size 4096Samsung SSD 980 PRO 1TB13K26K39K52K65KSE +/- 484.43, N = 9610671. (CC) gcc options: -O3 -fomit-frame-pointer -mtune=native -malign-double -fstrict-aliasing -fno-schedule-insns -ffast-math -lm

OpenBenchmarking.orgMflops, More Is BetterFFTW 3.3.6Build: Float + SSE - Size: 2D FFT Size 4096Samsung SSD 980 PRO 1TB6K12K18K24K30KSE +/- 139.81, N = 3282711. (CC) gcc options: -O3 -fomit-frame-pointer -mtune=native -malign-double -fstrict-aliasing -fno-schedule-insns -ffast-math -lm

HeFFTe - Highly Efficient FFT for Exascale

HeFFTe is the Highly Efficient FFT for Exascale software developed as part of the Exascale Computing Project. This test profile uses HeFFTe's built-in speed benchmarks under a variety of configuration options and currently catering to CPU/processor testing. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128Samsung SSD 980 PRO 1TB20406080100SE +/- 0.82, N = 3104.341. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256Samsung SSD 980 PRO 1TB20406080100SE +/- 0.15, N = 375.741. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: float - X Y Z: 512Samsung SSD 980 PRO 1TB1224364860SE +/- 0.01, N = 353.881. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128Samsung SSD 980 PRO 1TB4080120160200SE +/- 0.13, N = 3191.331. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256Samsung SSD 980 PRO 1TB4080120160200SE +/- 2.24, N = 3173.621. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: float - X Y Z: 512Samsung SSD 980 PRO 1TB20406080100SE +/- 0.20, N = 3100.401. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128Samsung SSD 980 PRO 1TB1326395265SE +/- 0.57, N = 359.061. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256Samsung SSD 980 PRO 1TB714212835SE +/- 0.03, N = 327.971. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: double - X Y Z: 512Samsung SSD 980 PRO 1TB714212835SE +/- 0.02, N = 327.671. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: float - X Y Z: 128Samsung SSD 980 PRO 1TB20406080100SE +/- 0.82, N = 392.221. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: float - X Y Z: 256Samsung SSD 980 PRO 1TB20406080100SE +/- 0.16, N = 382.941. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: float - X Y Z: 512Samsung SSD 980 PRO 1TB1224364860SE +/- 0.02, N = 354.661. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128Samsung SSD 980 PRO 1TB20406080100SE +/- 0.59, N = 3104.481. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256Samsung SSD 980 PRO 1TB1428425670SE +/- 0.08, N = 364.751. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: double - X Y Z: 512Samsung SSD 980 PRO 1TB1122334455SE +/- 0.01, N = 350.361. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: float - X Y Z: 128Samsung SSD 980 PRO 1TB4080120160200SE +/- 0.74, N = 3169.961. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: float - X Y Z: 256Samsung SSD 980 PRO 1TB4080120160200SE +/- 1.53, N = 3183.581. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: float - X Y Z: 512Samsung SSD 980 PRO 1TB20406080100SE +/- 0.02, N = 3108.921. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: double - X Y Z: 128Samsung SSD 980 PRO 1TB1224364860SE +/- 0.55, N = 451.101. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: double - X Y Z: 256Samsung SSD 980 PRO 1TB714212835SE +/- 0.05, N = 328.791. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: double - X Y Z: 512Samsung SSD 980 PRO 1TB714212835SE +/- 0.01, N = 327.731. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: double - X Y Z: 128Samsung SSD 980 PRO 1TB20406080100SE +/- 0.68, N = 394.851. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: double - X Y Z: 256Samsung SSD 980 PRO 1TB1632486480SE +/- 0.08, N = 373.451. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: double - X Y Z: 512Samsung SSD 980 PRO 1TB1224364860SE +/- 0.01, N = 354.341. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 128Samsung SSD 980 PRO 1TB20406080100SE +/- 0.20, N = 3104.961. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 256Samsung SSD 980 PRO 1TB20406080100SE +/- 0.30, N = 375.701. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 512Samsung SSD 980 PRO 1TB1224364860SE +/- 0.04, N = 353.901. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 128Samsung SSD 980 PRO 1TB4080120160200SE +/- 1.46, N = 3192.591. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 256Samsung SSD 980 PRO 1TB4080120160200SE +/- 0.68, N = 3173.841. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 512Samsung SSD 980 PRO 1TB20406080100SE +/- 0.06, N = 3100.731. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 128Samsung SSD 980 PRO 1TB1326395265SE +/- 0.41, N = 359.071. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 256Samsung SSD 980 PRO 1TB714212835SE +/- 0.02, N = 327.911. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 512Samsung SSD 980 PRO 1TB714212835SE +/- 0.02, N = 327.701. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 128Samsung SSD 980 PRO 1TB20406080100SE +/- 0.51, N = 391.061. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 256Samsung SSD 980 PRO 1TB20406080100SE +/- 0.11, N = 382.971. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 512Samsung SSD 980 PRO 1TB1224364860SE +/- 0.03, N = 354.761. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 128Samsung SSD 980 PRO 1TB20406080100SE +/- 1.24, N = 3103.851. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 256Samsung SSD 980 PRO 1TB1428425670SE +/- 0.19, N = 364.711. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 512Samsung SSD 980 PRO 1TB1122334455SE +/- 0.04, N = 350.471. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 128Samsung SSD 980 PRO 1TB4080120160200SE +/- 0.20, N = 3170.181. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 256Samsung SSD 980 PRO 1TB4080120160200SE +/- 0.37, N = 3180.951. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 512Samsung SSD 980 PRO 1TB20406080100SE +/- 0.05, N = 3108.971. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 128Samsung SSD 980 PRO 1TB1224364860SE +/- 0.34, N = 351.181. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 256Samsung SSD 980 PRO 1TB714212835SE +/- 0.03, N = 328.951. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 512Samsung SSD 980 PRO 1TB714212835SE +/- 0.02, N = 327.801. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 128Samsung SSD 980 PRO 1TB20406080100SE +/- 0.69, N = 393.731. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 256Samsung SSD 980 PRO 1TB1632486480SE +/- 0.19, N = 373.121. (CXX) g++ options: -O3

OpenBenchmarking.orgGFLOP/s, More Is BetterHeFFTe - Highly Efficient FFT for Exascale 2.3Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 512Samsung SSD 980 PRO 1TB1224364860SE +/- 0.02, N = 354.371. (CXX) g++ options: -O3

Pennant

Pennant is an application focused on hydrodynamics on general unstructured meshes in 2D. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgHydro Cycle Time - Seconds, Fewer Is BetterPennant 1.0.1Test: sedovbigSamsung SSD 980 PRO 1TB246810SE +/- 0.045366, N = 37.2545771. (CXX) g++ options: -fopenmp -lmpi_cxx -lmpi

OpenBenchmarking.orgHydro Cycle Time - Seconds, Fewer Is BetterPennant 1.0.1Test: leblancbigSamsung SSD 980 PRO 1TB0.90211.80422.70633.60844.5105SE +/- 0.003859, N = 34.0094061. (CXX) g++ options: -fopenmp -lmpi_cxx -lmpi

Palabos

The Palabos library is a framework for general purpose Computational Fluid Dynamics (CFD). Palabos uses a kernel based on the Lattice Boltzmann method. This test profile uses the Palabos MPI-based Cavity3D benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMega Site Updates Per Second, More Is BetterPalabos 2.3Grid Size: 100Samsung SSD 980 PRO 1TB80160240320400SE +/- 0.82, N = 3384.381. (CXX) g++ options: -std=c++17 -pedantic -O3 -rdynamic -lcrypto -lcurl -lsz -lz -ldl -lm

OpenBenchmarking.orgMega Site Updates Per Second, More Is BetterPalabos 2.3Grid Size: 400Samsung SSD 980 PRO 1TB60120180240300SE +/- 0.10, N = 3270.061. (CXX) g++ options: -std=c++17 -pedantic -O3 -rdynamic -lcrypto -lcurl -lsz -lz -ldl -lm

OpenBenchmarking.orgMega Site Updates Per Second, More Is BetterPalabos 2.3Grid Size: 500Samsung SSD 980 PRO 1TB60120180240300SE +/- 0.07, N = 3284.811. (CXX) g++ options: -std=c++17 -pedantic -O3 -rdynamic -lcrypto -lcurl -lsz -lz -ldl -lm

OpenBenchmarking.orgMega Site Updates Per Second, More Is BetterPalabos 2.3Grid Size: 1000Samsung SSD 980 PRO 1TB70140210280350SE +/- 0.29, N = 3320.661. (CXX) g++ options: -std=c++17 -pedantic -O3 -rdynamic -lcrypto -lcurl -lsz -lz -ldl -lm

Grid Size: 4000

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status.

Timed MrBayes Analysis

This test performs a bayesian analysis of a set of primate genome sequences in order to estimate their phylogeny. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed MrBayes Analysis 3.2.7Primate Phylogeny AnalysisSamsung SSD 980 PRO 1TB20406080100SE +/- 0.67, N = 3103.311. (CC) gcc options: -mmmx -msse -msse2 -msse3 -mssse3 -msse4.1 -msse4.2 -msse4a -msha -maes -mavx -mfma -mavx2 -mrdrnd -mbmi -mbmi2 -madx -mabm -O3 -std=c99 -pedantic -lm -lreadline

NWChem

NWChem is an open-source high performance computational chemistry package. Per NWChem's documentation, "NWChem aims to provide its users with computational chemistry tools that are scalable both in their ability to treat large scientific computational chemistry problems efficiently, and in their use of available parallel computing resources from high-performance parallel supercomputers to conventional workstation clusters." Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNWChem 7.0.2Input: C240 BuckyballSamsung SSD 980 PRO 1TB4008001200160020001753.61. (F9X) gfortran options: -lnwctask -lccsd -lmcscf -lselci -lmp2 -lmoints -lstepper -ldriver -loptim -lnwdft -lgradients -lcphf -lesp -lddscf -ldangchang -lguess -lhessian -lvib -lnwcutil -lrimp2 -lproperty -lsolvation -lnwints -lprepar -lnwmd -lnwpw -lofpw -lpaw -lpspw -lband -lnwpwlib -lcafe -lspace -lanalyze -lqhop -lpfft -ldplot -ldrdy -lvscf -lqmmm -lqmd -letrans -ltce -lbq -lmm -lcons -lperfm -ldntmc -lccca -ldimqm -lga -larmci -lpeigs -l64to32 -lopenblas -lpthread -lrt -llapack -lnwcblas -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz -lcomex -m64 -ffast-math -std=legacy -fdefault-integer-8 -finline-functions -O2

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 3.16Input: Li2_STO_aeSamsung SSD 980 PRO 1TB20406080100SE +/- 0.55, N = 393.571. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl

OpenBenchmarking.orgTotal Execution Time - Seconds, Fewer Is BetterQMCPACK 3.16Input: simple-H2OSamsung SSD 980 PRO 1TB612182430SE +/- 0.05, N = 325.371. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl

OpenBenchmarking.orgTotal Execution Time - Seconds, Fewer Is BetterQMCPACK 3.16Input: FeCO6_b3lyp_gmsSamsung SSD 980 PRO 1TB4080120160200SE +/- 0.41, N = 3179.151. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl

OpenBenchmarking.orgTotal Execution Time - Seconds, Fewer Is BetterQMCPACK 3.16Input: FeCO6_b3lyp_gmsSamsung SSD 980 PRO 1TB4080120160200SE +/- 0.67, N = 3181.921. (CXX) g++ options: -fopenmp -foffload=disable -finline-limit=1000 -fstrict-aliasing -funroll-all-loops -ffast-math -march=native -O3 -lm -ldl

Timed HMMer Search

This test searches through the Pfam database of profile hidden markov models. The search finds the domain structure of Drosophila Sevenless protein. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed HMMer Search 3.3.2Pfam Database SearchSamsung SSD 980 PRO 1TB20406080100SE +/- 0.06, N = 3100.341. (CC) gcc options: -O3 -pthread -lhmmer -leasel -lm -lmpi

Xcompact3d Incompact3d

Xcompact3d Incompact3d is a Fortran-MPI based, finite difference high-performance code for solving the incompressible Navier-Stokes equation and as many as you need scalar transport equations. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterXcompact3d Incompact3d 2021-03-11Input: X3D-benchmarking input.i3dSamsung SSD 980 PRO 1TB130260390520650SE +/- 0.04, N = 3590.181. (F9X) gfortran options: -cpp -O2 -funroll-loops -floop-optimize -fcray-pointer -fbacktrace -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz

OpenBenchmarking.orgSeconds, Fewer Is BetterXcompact3d Incompact3d 2021-03-11Input: input.i3d 129 Cells Per DirectionSamsung SSD 980 PRO 1TB1.12922.25843.38764.51685.646SE +/- 0.03302499, N = 35.018860181. (F9X) gfortran options: -cpp -O2 -funroll-loops -floop-optimize -fcray-pointer -fbacktrace -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz

OpenBenchmarking.orgSeconds, Fewer Is BetterXcompact3d Incompact3d 2021-03-11Input: input.i3d 193 Cells Per DirectionSamsung SSD 980 PRO 1TB510152025SE +/- 0.03, N = 321.441. (F9X) gfortran options: -cpp -O2 -funroll-loops -floop-optimize -fcray-pointer -fbacktrace -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz

Timed MAFFT Alignment

This test performs an alignment of 100 pyruvate decarboxylase sequences. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed MAFFT Alignment 7.471Multiple Sequence Alignment - LSU RNASamsung SSD 980 PRO 1TB246810SE +/- 0.058, N = 38.6821. (CC) gcc options: -std=c99 -O3 -lm -lpthread

Monte Carlo Simulations of Ionised Nebulae

Mocassin is the Monte Carlo Simulations of Ionised Nebulae. MOCASSIN is a fully 3D or 2D photoionisation and dust radiative transfer code which employs a Monte Carlo approach to the transfer of radiation through media of arbitrary geometry and density distribution. Learn more via the OpenBenchmarking.org test page.

Input: Gas HII40

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: Backtrace for this error:

OpenBenchmarking.orgSeconds, Fewer Is BetterMonte Carlo Simulations of Ionised Nebulae 2.02.73.3Input: Dust 2D tau100.0Samsung SSD 980 PRO 1TB4080120160200SE +/- 1.31, N = 3178.761. (F9X) gfortran options: -cpp -Jsource/ -ffree-line-length-0 -lm -std=legacy -O2 -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lz

OpenFOAM

OpenFOAM is the leading free, open-source software for computational fluid dynamics (CFD). This test profile currently uses the drivaerFastback test case for analyzing automotive aerodynamics or alternatively the older motorBike input. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: motorBike - Mesh TimeSamsung SSD 980 PRO 1TB81624324036.421. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: motorBike - Execution TimeSamsung SSD 980 PRO 1TB132639526559.021. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Large Mesh Size - Mesh TimeSamsung SSD 980 PRO 1TB160320480640800752.341. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Large Mesh Size - Execution TimeSamsung SSD 980 PRO 1TB3K6K9K12K15K14665.881. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Small Mesh Size - Mesh TimeSamsung SSD 980 PRO 1TB51015202520.361. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Small Mesh Size - Execution TimeSamsung SSD 980 PRO 1TB91827364541.041. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Medium Mesh Size - Mesh TimeSamsung SSD 980 PRO 1TB306090120150114.821. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenFOAM 10Input: drivaerFastback, Medium Mesh Size - Execution TimeSamsung SSD 980 PRO 1TB130260390520650611.521. (CXX) g++ options: -std=c++14 -m64 -O3 -ftemplate-depth-100 -fPIC -fuse-ld=bfd -Xlinker --add-needed --no-as-needed -lfoamToVTK -ldynamicMesh -llagrangian -lgenericPatchFields -lfileFormats -lOpenFOAM -ldl -lm

OpenRadioss

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenRadioss 2023.09.15Model: Bumper BeamSamsung SSD 980 PRO 1TB1428425670SE +/- 0.23, N = 361.03

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenRadioss 2023.09.15Model: Chrysler Neon 1MSamsung SSD 980 PRO 1TB60120180240300SE +/- 0.84, N = 3278.60

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenRadioss 2023.09.15Model: Cell Phone Drop TestSamsung SSD 980 PRO 1TB510152025SE +/- 0.08, N = 322.15

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenRadioss 2023.09.15Model: Bird Strike on WindshieldSamsung SSD 980 PRO 1TB20406080100SE +/- 0.65, N = 3105.08

OpenBenchmarking.orgSeconds, Fewer Is BetterOpenRadioss 2023.09.15Model: Rubber O-Ring Seal InstallationSamsung SSD 980 PRO 1TB1326395265SE +/- 0.25, N = 356.11

Model: INIVOL and Fluid Structure Interaction Drop Container

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result. E: ** ERROR: INPUT FILE /fsi_drop_container_0001. NOT FOUND

Quantum ESPRESSO

Quantum ESPRESSO is an integrated suite of Open-Source computer codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterQuantum ESPRESSO 7.0Input: AUSURF112Samsung SSD 980 PRO 1TB60120180240300SE +/- 0.42, N = 3265.431. (F9X) gfortran options: -pthread -fopenmp -ldevXlib -lopenblas -lFoX_dom -lFoX_sax -lFoX_wxml -lFoX_common -lFoX_utils -lFoX_fsys -lfftw3_omp -lfftw3 -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz

RELION

RELION - REgularised LIkelihood OptimisatioN - is a stand-alone computer program for Maximum A Posteriori refinement of (multiple) 3D reconstructions or 2D class averages in cryo-electron microscopy (cryo-EM). It is developed in the research group of Sjors Scheres at the MRC Laboratory of Molecular Biology. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRELION 4.0.1Test: Basic - Device: CPUSamsung SSD 980 PRO 1TB90180270360450SE +/- 9.25, N = 9413.011. (CXX) g++ options: -fopenmp -std=c++11 -O3 -rdynamic -ldl -ltiff -lfftw3f -lfftw3 -lpng -ljpeg -lmpi_cxx -lmpi

Remhos

Remhos (REMap High-Order Solver) is a miniapp that solves the pure advection equations that are used to perform monotonic and conservative discontinuous field interpolation (remap) as part of the Eulerian phase in Arbitrary Lagrangian Eulerian (ALE) simulations. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRemhos 1.0Test: Sample Remap ExampleSamsung SSD 980 PRO 1TB3691215SE +/- 0.07, N = 312.781. (CXX) g++ options: -O3 -std=c++11 -lmfem -lHYPRE -lmetis -lrt -lmpi_cxx -lmpi

SPECFEM3D

simulates acoustic (fluid), elastic (solid), coupled acoustic/elastic, poroelastic or seismic wave propagation in any type of conforming mesh of hexahedra. This test profile currently relies on CPU-based execution for SPECFEM3D and using a variety of their built-in examples/models for benchmarking. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterSPECFEM3D 4.0Model: Mount St. HelensSamsung SSD 980 PRO 1TB3691215SE +/- 0.115311179, N = 39.1097676441. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz

OpenBenchmarking.orgSeconds, Fewer Is BetterSPECFEM3D 4.0Model: Layered HalfspaceSamsung SSD 980 PRO 1TB612182430SE +/- 0.19, N = 325.421. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz

OpenBenchmarking.orgSeconds, Fewer Is BetterSPECFEM3D 4.0Model: Tomographic ModelSamsung SSD 980 PRO 1TB3691215SE +/- 0.068910369, N = 39.0299389191. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz

OpenBenchmarking.orgSeconds, Fewer Is BetterSPECFEM3D 4.0Model: Homogeneous HalfspaceSamsung SSD 980 PRO 1TB3691215SE +/- 0.07, N = 311.451. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz

OpenBenchmarking.orgSeconds, Fewer Is BetterSPECFEM3D 4.0Model: Water-layered HalfspaceSamsung SSD 980 PRO 1TB612182430SE +/- 0.11, N = 322.961. (F9X) gfortran options: -O2 -fopenmp -std=f2003 -fimplicit-none -fmax-errors=10 -pedantic -pedantic-errors -O3 -finline-functions -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz

nekRS

nekRS is an open-source Navier Stokes solver based on the spectral element method. NekRS supports both CPU and GPU/accelerator support though this test profile is currently configured for CPU execution. NekRS is part of Nek5000 of the Mathematics and Computer Science MCS at Argonne National Laboratory. This nekRS benchmark is primarily relevant to large core count HPC servers and otherwise may be very time consuming on smaller systems. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgflops/rank, More Is BetternekRS 23.0Input: KershawSamsung SSD 980 PRO 1TB1000M2000M3000M4000M5000MSE +/- 12527076.01, N = 346697100001. (CXX) g++ options: -fopenmp -O2 -march=native -mtune=native -ftree-vectorize -rdynamic -lmpi_cxx -lmpi

OpenBenchmarking.orgflops/rank, More Is BetternekRS 23.0Input: TurboPipe PeriodicSamsung SSD 980 PRO 1TB800M1600M2400M3200M4000MSE +/- 624615.44, N = 335581766671. (CXX) g++ options: -fopenmp -O2 -march=native -mtune=native -ftree-vectorize -rdynamic -lmpi_cxx -lmpi

LAMMPS Molecular Dynamics Simulator

LAMMPS is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgns/day, More Is BetterLAMMPS Molecular Dynamics Simulator 23Jun2022Model: 20k AtomsSamsung SSD 980 PRO 1TB816243240SE +/- 0.25, N = 335.681. (CXX) g++ options: -O3 -lm -ldl

OpenBenchmarking.orgns/day, More Is BetterLAMMPS Molecular Dynamics Simulator 23Jun2022Model: Rhodopsin ProteinSamsung SSD 980 PRO 1TB816243240SE +/- 0.24, N = 336.811. (CXX) g++ options: -O3 -lm -ldl

LULESH

LULESH is the Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgz/s, More Is BetterLULESH 2.0.3Samsung SSD 980 PRO 1TB4K8K12K16K20KSE +/- 70.84, N = 320397.481. (CXX) g++ options: -O3 -fopenmp -lm -lmpi_cxx -lmpi

ArrayFire

ArrayFire is an GPU and CPU numeric processing library, this test uses the built-in CPU and OpenCL ArrayFire benchmarks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOPS, More Is BetterArrayFire 3.7Test: BLAS CPUSamsung SSD 980 PRO 1TB400800120016002000SE +/- 6.85, N = 31761.241. (CXX) g++ options: -rdynamic

ACES DGEMM

This is a multi-threaded DGEMM benchmark. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgGFLOP/s, More Is BetterACES DGEMM 1.0Sustained Floating-Point RateSamsung SSD 980 PRO 1TB510152025SE +/- 0.23, N = 320.681. (CC) gcc options: -O3 -march=native -fopenmp

Himeno Benchmark

The Himeno benchmark is a linear solver of pressure Poisson using a point-Jacobi method. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMFLOPS, More Is BetterHimeno Benchmark 3.0Poisson Pressure SolverSamsung SSD 980 PRO 1TB10002000300040005000SE +/- 17.66, N = 34580.381. (CC) gcc options: -O3 -mavx2

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 3.1Harness: IP Shapes 1D - Data Type: f32 - Engine: CPUSamsung SSD 980 PRO 1TB0.21140.42280.63420.84561.057SE +/- 0.014147, N = 150.939419MIN: 0.781. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: IP Shapes 3D - Data Type: f32 - Engine: CPUSamsung SSD 980 PRO 1TB0.4080.8161.2241.6322.04SE +/- 0.00446, N = 31.81344MIN: 1.681. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPUSamsung SSD 980 PRO 1TB0.15590.31180.46770.62360.7795SE +/- 0.027689, N = 150.692944MIN: 0.451. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPUSamsung SSD 980 PRO 1TB0.06750.1350.20250.270.3375SE +/- 0.001871, N = 30.299845MIN: 0.271. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPUSamsung SSD 980 PRO 1TB0.14690.29380.44070.58760.7345SE +/- 0.005226, N = 90.652856MIN: 0.621. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPUSamsung SSD 980 PRO 1TB0.73931.47862.21792.95723.6965SE +/- 0.00823, N = 33.28585MIN: 2.851. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPUSamsung SSD 980 PRO 1TB0.35230.70461.05691.40921.7615SE +/- 0.00946, N = 31.56562MIN: 1.41. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPUSamsung SSD 980 PRO 1TB0.38260.76521.14781.53041.913SE +/- 0.02013, N = 31.70038MIN: 1.531. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPUSamsung SSD 980 PRO 1TB0.1930.3860.5790.7720.965SE +/- 0.009588, N = 40.857656MIN: 0.771. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPUSamsung SSD 980 PRO 1TB0.1170.2340.3510.4680.585SE +/- 0.005655, N = 40.520011MIN: 0.491. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPUSamsung SSD 980 PRO 1TB30060090012001500SE +/- 12.15, N = 31237.80MIN: 1206.51. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPUSamsung SSD 980 PRO 1TB90180270360450SE +/- 5.95, N = 15424.16MIN: 392.441. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPUSamsung SSD 980 PRO 1TB30060090012001500SE +/- 9.42, N = 31255.04MIN: 1235.241. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPUSamsung SSD 980 PRO 1TB90180270360450SE +/- 5.05, N = 15416.81MIN: 370.031. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPUSamsung SSD 980 PRO 1TB30060090012001500SE +/- 9.95, N = 31253.69MIN: 1228.291. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

OpenBenchmarking.orgms, Fewer Is BetteroneDNN 3.1Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPUSamsung SSD 980 PRO 1TB90180270360450SE +/- 4.15, N = 15419.35MIN: 380.651. (CXX) g++ options: -O3 -march=native -fopenmp -msse4.1 -fPIC -pie -ldl -lpthread

Numpy Benchmark

This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterNumpy BenchmarkSamsung SSD 980 PRO 1TB120240360480600SE +/- 1.09, N = 3538.47

DeepSpeech

Mozilla DeepSpeech is a speech-to-text engine powered by TensorFlow for machine learning and derived from Baidu's Deep Speech research paper. This test profile times the speech-to-text process for a roughly three minute audio recording. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterDeepSpeech 0.6Acceleration: CPUSamsung SSD 980 PRO 1TB1632486480SE +/- 0.79, N = 470.42

R Benchmark

This test is a quick-running survey of general R performance Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterR BenchmarkSamsung SSD 980 PRO 1TB0.02740.05480.08220.10960.137SE +/- 0.0010, N = 30.12171. R scripting front-end version 4.1.2 (2021-11-01)

RNNoise

RNNoise is a recurrent neural network for audio noise reduction developed by Mozilla and Xiph.Org. This test profile is a single-threaded test measuring the time to denoise a sample 26 minute long 16-bit RAW audio file using this recurrent neural network noise suppression library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRNNoise 2020-06-28Samsung SSD 980 PRO 1TB48121620SE +/- 0.02, N = 318.241. (CC) gcc options: -O2 -pedantic -fvisibility=hidden

ASKAP

ASKAP is a set of benchmarks from the Australian SKA Pathfinder. The principal ASKAP benchmarks are the Hogbom Clean Benchmark (tHogbomClean) and Convolutional Resamping Benchmark (tConvolve) as well as some previous ASKAP benchmarks being included as well for OpenCL and CUDA execution of tConvolve. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMillion Grid Points Per Second, More Is BetterASKAP 1.0Test: tConvolve MT - GriddingSamsung SSD 980 PRO 1TB12002400360048006000SE +/- 0.58, N = 35441.321. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp

OpenBenchmarking.orgMillion Grid Points Per Second, More Is BetterASKAP 1.0Test: tConvolve MT - DegriddingSamsung SSD 980 PRO 1TB2K4K6K8K10KSE +/- 4.68, N = 38148.101. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp

OpenBenchmarking.orgMpix/sec, More Is BetterASKAP 1.0Test: tConvolve MPI - DegriddingSamsung SSD 980 PRO 1TB5K10K15K20K25KSE +/- 208.17, N = 325499.31. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp

OpenBenchmarking.orgMpix/sec, More Is BetterASKAP 1.0Test: tConvolve MPI - GriddingSamsung SSD 980 PRO 1TB6K12K18K24K30KSE +/- 116.50, N = 327028.91. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp

OpenBenchmarking.orgMillion Grid Points Per Second, More Is BetterASKAP 1.0Test: tConvolve OpenMP - GriddingSamsung SSD 980 PRO 1TB2K4K6K8K10KSE +/- 124.76, N = 159740.411. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp

OpenBenchmarking.orgMillion Grid Points Per Second, More Is BetterASKAP 1.0Test: tConvolve OpenMP - DegriddingSamsung SSD 980 PRO 1TB2K4K6K8K10KSE +/- 33.62, N = 158186.031. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp

OpenBenchmarking.orgIterations Per Second, More Is BetterASKAP 1.0Test: Hogbom Clean OpenMPSamsung SSD 980 PRO 1TB130260390520650SE +/- 3.22, N = 3603.661. (CXX) g++ options: -O3 -fstrict-aliasing -fopenmp

Graph500

This is a benchmark of the reference implementation of Graph500, an HPC benchmark focused on data intensive loads and commonly tested on supercomputers for complex data problems. Graph500 primarily stresses the communication subsystem of the hardware under test. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgbfs median_TEPS, More Is BetterGraph500 3.0Scale: 26Samsung SSD 980 PRO 1TB160M320M480M640M800M7339230001. (CC) gcc options: -fcommon -O3 -lpthread -lm -lmpi

OpenBenchmarking.orgbfs max_TEPS, More Is BetterGraph500 3.0Scale: 26Samsung SSD 980 PRO 1TB160M320M480M640M800M7486490001. (CC) gcc options: -fcommon -O3 -lpthread -lm -lmpi

OpenBenchmarking.orgsssp median_TEPS, More Is BetterGraph500 3.0Scale: 26Samsung SSD 980 PRO 1TB60M120M180M240M300M2862990001. (CC) gcc options: -fcommon -O3 -lpthread -lm -lmpi

OpenBenchmarking.orgsssp max_TEPS, More Is BetterGraph500 3.0Scale: 26Samsung SSD 980 PRO 1TB80M160M240M320M400M3677420001. (CC) gcc options: -fcommon -O3 -lpthread -lm -lmpi

Scale: 29

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. E: mpirun noticed that process rank 28 with PID 0 on node incooling-desktop exited on signal 9 (Killed).

Intel MPI Benchmarks

Intel MPI Benchmarks for stressing MPI implementations. At this point the test profile aggregates results for some common MPI functionality. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgAverage Msg/sec, More Is BetterIntel MPI Benchmarks 2019.3Test: IMB-P2P PingPongSamsung SSD 980 PRO 1TB8M16M24M32M40MSE +/- 45475.10, N = 336538343MIN: 9387 / MAX: 905983591. (CXX) g++ options: -O0 -pedantic -fopenmp -lmpi_cxx -lmpi

OpenBenchmarking.orgAverage Mbytes/sec, More Is BetterIntel MPI Benchmarks 2019.3Test: IMB-MPI1 ExchangeSamsung SSD 980 PRO 1TB2K4K6K8K10KSE +/- 247.12, N = 127897.23MAX: 40197.481. (CXX) g++ options: -O0 -pedantic -fopenmp -lmpi_cxx -lmpi

OpenBenchmarking.orgAverage usec, Fewer Is BetterIntel MPI Benchmarks 2019.3Test: IMB-MPI1 ExchangeSamsung SSD 980 PRO 1TB306090120150SE +/- 3.98, N = 12135.45MIN: 0.72 / MAX: 5787.961. (CXX) g++ options: -O0 -pedantic -fopenmp -lmpi_cxx -lmpi

OpenBenchmarking.orgAverage Mbytes/sec, More Is BetterIntel MPI Benchmarks 2019.3Test: IMB-MPI1 PingPongSamsung SSD 980 PRO 1TB13002600390052006500SE +/- 90.55, N = 156060.62MIN: 5.41 / MAX: 21642.931. (CXX) g++ options: -O0 -pedantic -fopenmp -lmpi_cxx -lmpi

OpenBenchmarking.orgAverage Mbytes/sec, More Is BetterIntel MPI Benchmarks 2019.3Test: IMB-MPI1 SendrecvSamsung SSD 980 PRO 1TB11002200330044005500SE +/- 93.14, N = 155133.49MAX: 24149.021. (CXX) g++ options: -O0 -pedantic -fopenmp -lmpi_cxx -lmpi

OpenBenchmarking.orgAverage usec, Fewer Is BetterIntel MPI Benchmarks 2019.3Test: IMB-MPI1 SendrecvSamsung SSD 980 PRO 1TB20406080100SE +/- 1.38, N = 1575.26MIN: 0.36 / MAX: 2923.861. (CXX) g++ options: -O0 -pedantic -fopenmp -lmpi_cxx -lmpi

GROMACS

The GROMACS (GROningen MAchine for Chemical Simulations) molecular dynamics package testing with the water_GMX50 data. This test profile allows selecting between CPU and GPU-based GROMACS builds. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgNs Per Day, More Is BetterGROMACS 2023Implementation: MPI CPU - Input: water_GMX50_bareSamsung SSD 980 PRO 1TB246810SE +/- 0.001, N = 37.0441. (CXX) g++ options: -O3

Darmstadt Automotive Parallel Heterogeneous Suite

DAPHNE is the Darmstadt Automotive Parallel HeterogeNEous Benchmark Suite with OpenCL / CUDA / OpenMP test cases for these automotive benchmarks for evaluating programming models in context to vehicle autonomous driving capabilities. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgTest Cases Per Minute, More Is BetterDarmstadt Automotive Parallel Heterogeneous Suite 2021.11.02Backend: OpenMP - Kernel: NDT MappingSamsung SSD 980 PRO 1TB30060090012001500SE +/- 6.20, N = 31401.191. (CXX) g++ options: -O3 -std=c++11 -fopenmp

OpenBenchmarking.orgTest Cases Per Minute, More Is BetterDarmstadt Automotive Parallel Heterogeneous Suite 2021.11.02Backend: OpenMP - Kernel: Points2ImageSamsung SSD 980 PRO 1TB5K10K15K20K25KSE +/- 160.89, N = 325162.201. (CXX) g++ options: -O3 -std=c++11 -fopenmp

OpenBenchmarking.orgTest Cases Per Minute, More Is BetterDarmstadt Automotive Parallel Heterogeneous Suite 2021.11.02Backend: OpenMP - Kernel: Euclidean ClusterSamsung SSD 980 PRO 1TB30060090012001500SE +/- 1.30, N = 31381.661. (CXX) g++ options: -O3 -std=c++11 -fopenmp

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation focused on TensorFlow machine learning for mobile, IoT, edge, and other cases. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: SqueezeNetSamsung SSD 980 PRO 1TB5001000150020002500SE +/- 2.26, N = 32165.59

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Inception V4Samsung SSD 980 PRO 1TB5K10K15K20K25KSE +/- 39.22, N = 321802.6

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: NASNet MobileSamsung SSD 980 PRO 1TB4K8K12K16K20KSE +/- 36.01, N = 319693.5

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Mobilenet FloatSamsung SSD 980 PRO 1TB30060090012001500SE +/- 3.31, N = 31485.94

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Mobilenet QuantSamsung SSD 980 PRO 1TB30060090012001500SE +/- 2.79, N = 31519.59

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2022-05-18Model: Inception ResNet V2Samsung SSD 980 PRO 1TB6K12K18K24K30KSE +/- 57.85, N = 327862.9

TensorFlow

This is a benchmark of the TensorFlow deep learning framework using the TensorFlow reference benchmarks (tensorflow/benchmarks with tf_cnn_benchmarks.py). Note with the Phoronix Test Suite there is also pts/tensorflow-lite for benchmarking the TensorFlow Lite binaries if desired for complementary metrics. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: VGG-16Samsung SSD 980 PRO 1TB3691215SE +/- 0.01, N = 39.34

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 32 - Model: VGG-16Samsung SSD 980 PRO 1TB3691215SE +/- 0.01, N = 39.90

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 64 - Model: VGG-16Samsung SSD 980 PRO 1TB3691215SE +/- 0.01, N = 310.22

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: AlexNetSamsung SSD 980 PRO 1TB306090120150SE +/- 1.42, N = 3129.33

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 256 - Model: VGG-16Samsung SSD 980 PRO 1TB3691215SE +/- 0.01, N = 310.55

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 32 - Model: AlexNetSamsung SSD 980 PRO 1TB306090120150SE +/- 0.03, N = 3157.36

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 512 - Model: VGG-16Samsung SSD 980 PRO 1TB3691215SE +/- 0.01, N = 310.60

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 64 - Model: AlexNetSamsung SSD 980 PRO 1TB4080120160200SE +/- 0.73, N = 3175.72

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 256 - Model: AlexNetSamsung SSD 980 PRO 1TB4080120160200SE +/- 0.88, N = 3194.81

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 512 - Model: AlexNetSamsung SSD 980 PRO 1TB4080120160200SE +/- 0.09, N = 3201.68

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: GoogLeNetSamsung SSD 980 PRO 1TB20406080100SE +/- 0.16, N = 376.32

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 16 - Model: ResNet-50Samsung SSD 980 PRO 1TB612182430SE +/- 0.28, N = 325.24

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 32 - Model: GoogLeNetSamsung SSD 980 PRO 1TB20406080100SE +/- 0.38, N = 385.59

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 32 - Model: ResNet-50Samsung SSD 980 PRO 1TB612182430SE +/- 0.12, N = 327.31

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 64 - Model: GoogLeNetSamsung SSD 980 PRO 1TB20406080100SE +/- 0.34, N = 382.01

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 64 - Model: ResNet-50Samsung SSD 980 PRO 1TB612182430SE +/- 0.00, N = 327.34

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 256 - Model: GoogLeNetSamsung SSD 980 PRO 1TB20406080100SE +/- 0.02, N = 383.50

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 256 - Model: ResNet-50Samsung SSD 980 PRO 1TB714212835SE +/- 0.03, N = 328.66

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 512 - Model: GoogLeNetSamsung SSD 980 PRO 1TB20406080100SE +/- 0.07, N = 384.08

OpenBenchmarking.orgimages/sec, More Is BetterTensorFlow 2.12Device: CPU - Batch Size: 512 - Model: ResNet-50Samsung SSD 980 PRO 1TB714212835SE +/- 0.02, N = 329.53

GNU Octave Benchmark

This test profile measures how long it takes to complete several reference GNU Octave files via octave-benchmark. GNU Octave is used for numerical computations and is an open-source alternative to MATLAB. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterGNU Octave Benchmark 6.4.0Samsung SSD 980 PRO 1TB246810SE +/- 0.025, N = 56.000

Neural Magic DeepSparse

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB1122334455SE +/- 0.27, N = 350.60

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB130260390520650SE +/- 3.00, N = 3623.59

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB612182430SE +/- 0.05, N = 324.74

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB918273645SE +/- 0.07, N = 340.42

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB30060090012001500SE +/- 8.56, N = 31405.24

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB510152025SE +/- 0.14, N = 322.65

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB50100150200250SE +/- 0.61, N = 3217.93

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB1.0322.0643.0964.1285.16SE +/- 0.0128, N = 34.5868

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB120240360480600SE +/- 4.89, N = 3533.09

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB1326395265SE +/- 0.54, N = 359.71

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB20406080100SE +/- 0.21, N = 399.98

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB3691215SE +/- 0.0208, N = 39.9946

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB306090120150SE +/- 1.43, N = 3157.91

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB4080120160200SE +/- 1.81, N = 3201.26

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB1020304050SE +/- 0.25, N = 345.00

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB510152025SE +/- 0.13, N = 322.21

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB130260390520650SE +/- 0.42, N = 3620.28

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB1224364860SE +/- 0.04, N = 351.16

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB50100150200250SE +/- 0.26, N = 3222.05

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Baseline - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB1.01272.02543.03814.05085.0635SE +/- 0.0054, N = 34.5011

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB11002200330044005500SE +/- 7.70, N = 34954.46

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB246810SE +/- 0.0106, N = 36.4127

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB2004006008001000SE +/- 6.11, N = 3893.92

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB0.2510.5020.7531.0041.255SE +/- 0.0076, N = 31.1157

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB60120180240300SE +/- 3.82, N = 3291.78

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB20406080100SE +/- 1.44, N = 3108.76

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB4080120160200SE +/- 0.51, N = 3159.41

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB246810SE +/- 0.0201, N = 36.2699

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB1428425670SE +/- 0.09, N = 361.95

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB110220330440550SE +/- 0.79, N = 3511.29

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB714212835SE +/- 0.08, N = 329.89

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB816243240SE +/- 0.09, N = 333.45

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB130260390520650SE +/- 1.98, N = 3617.89

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB1224364860SE +/- 0.18, N = 351.38

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB50100150200250SE +/- 1.43, N = 3223.34

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB1.0072.0143.0214.0285.035SE +/- 0.0289, N = 34.4754

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB70140210280350SE +/- 1.99, N = 3299.77

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB20406080100SE +/- 0.71, N = 3105.84

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB4080120160200SE +/- 0.74, N = 3161.23

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB246810SE +/- 0.0287, N = 36.2004

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB100200300400500SE +/- 1.34, N = 3438.93

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB1632486480SE +/- 0.25, N = 372.38

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB306090120150SE +/- 0.46, N = 3113.38

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB246810SE +/- 0.0357, N = 38.8145

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB1530456075SE +/- 0.47, N = 365.10

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB110220330440550SE +/- 3.29, N = 3485.27

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB816243240SE +/- 0.14, N = 334.35

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB714212835SE +/- 0.12, N = 329.10

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB160320480640800SE +/- 1.16, N = 3746.71

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB1020304050SE +/- 0.05, N = 342.64

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB306090120150SE +/- 0.39, N = 3114.61

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB246810SE +/- 0.0297, N = 38.7222

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB50100150200250SE +/- 0.80, N = 3223.19

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB306090120150SE +/- 0.52, N = 3142.34

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB1428425670SE +/- 0.19, N = 363.03

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB48121620SE +/- 0.05, N = 315.86

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB1122334455SE +/- 0.24, N = 350.72

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-StreamSamsung SSD 980 PRO 1TB130260390520650SE +/- 2.89, N = 3622.41

OpenBenchmarking.orgitems/sec, More Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB612182430SE +/- 0.08, N = 324.77

OpenBenchmarking.orgms/batch, Fewer Is BetterNeural Magic DeepSparse 1.5Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-StreamSamsung SSD 980 PRO 1TB918273645SE +/- 0.13, N = 340.37

spaCy

The spaCy library is an open-source solution for advanced neural language processing (NLP). The spaCy library leverages Python and is a leading neural language processing solution. This test profile times the spaCy CPU performance with various models. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgtokens/sec, More Is BetterspaCy 3.4.1Model: en_core_web_lgSamsung SSD 980 PRO 1TB3K6K9K12K15KSE +/- 35.10, N = 313673

OpenBenchmarking.orgtokens/sec, More Is BetterspaCy 3.4.1Model: en_core_web_trfSamsung SSD 980 PRO 1TB9001800270036004500SE +/- 9.84, N = 33981

Caffe

This is a benchmark of the Caffe deep learning framework and currently supports the AlexNet and Googlenet model and execution on both CPUs and NVIDIA GPUs. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMilli-Seconds, Fewer Is BetterCaffe 2020-02-13Model: AlexNet - Acceleration: CPU - Iterations: 100Samsung SSD 980 PRO 1TB6K12K18K24K30KSE +/- 66.71, N = 3280451. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lcrypto -lcurl -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas

OpenBenchmarking.orgMilli-Seconds, Fewer Is BetterCaffe 2020-02-13Model: AlexNet - Acceleration: CPU - Iterations: 200Samsung SSD 980 PRO 1TB12K24K36K48K60KSE +/- 105.71, N = 3558851. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lcrypto -lcurl -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas

OpenBenchmarking.orgMilli-Seconds, Fewer Is BetterCaffe 2020-02-13Model: AlexNet - Acceleration: CPU - Iterations: 1000Samsung SSD 980 PRO 1TB60K120K180K240K300KSE +/- 95.71, N = 32782021. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lcrypto -lcurl -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas

OpenBenchmarking.orgMilli-Seconds, Fewer Is BetterCaffe 2020-02-13Model: GoogleNet - Acceleration: CPU - Iterations: 100Samsung SSD 980 PRO 1TB16K32K48K64K80KSE +/- 62.63, N = 3766541. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lcrypto -lcurl -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas

OpenBenchmarking.orgMilli-Seconds, Fewer Is BetterCaffe 2020-02-13Model: GoogleNet - Acceleration: CPU - Iterations: 200Samsung SSD 980 PRO 1TB30K60K90K120K150KSE +/- 103.51, N = 31529721. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lcrypto -lcurl -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas

OpenBenchmarking.orgMilli-Seconds, Fewer Is BetterCaffe 2020-02-13Model: GoogleNet - Acceleration: CPU - Iterations: 1000Samsung SSD 980 PRO 1TB160K320K480K640K800KSE +/- 849.11, N = 37657371. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lcrypto -lcurl -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas

WRF

WRF, the Weather Research and Forecasting Model, is a "next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational forecasting applications. It features two dynamical cores, a data assimilation system, and a software architecture supporting parallel computation and system extensibility." Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterWRF 4.2.2Input: conus 2.5kmSamsung SSD 980 PRO 1TB3K6K9K12K15K15397.731. (F9X) gfortran options: -O2 -ftree-vectorize -funroll-loops -ffree-form -fconvert=big-endian -frecord-marker=4 -fallow-invalid-boz -lesmf_time -lwrfio_nf -lnetcdff -lnetcdf -lmpi_usempif08 -lmpi_mpifh -lmpi -lopen-rte -lopen-pal -lhwloc -levent_core -levent_pthreads -lm -lz

GPAW

GPAW is a density-functional theory (DFT) Python code based on the projector-augmented wave (PAW) method and the atomic simulation environment (ASE). Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterGPAW 23.6Input: Carbon NanotubeSamsung SSD 980 PRO 1TB1326395265SE +/- 0.16, N = 359.101. (CC) gcc options: -shared -fwrapv -O2 -lxc -lblas -lmpi

Mobile Neural Network

MNN is the Mobile Neural Network as a highly efficient, lightweight deep learning framework developed by Alibaba. This MNN test profile is building the OpenMP / CPU threaded version for processor benchmarking and not any GPU-accelerated test. MNN does allow making use of AVX-512 extensions. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: nasnetSamsung SSD 980 PRO 1TB3691215SE +/- 0.07, N = 310.53MIN: 10.29 / MAX: 11.771. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: mobilenetV3Samsung SSD 980 PRO 1TB0.37710.75421.13131.50841.8855SE +/- 0.023, N = 31.676MIN: 1.61 / MAX: 2.841. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: squeezenetv1.1Samsung SSD 980 PRO 1TB0.64371.28741.93112.57483.2185SE +/- 0.047, N = 32.861MIN: 2.74 / MAX: 35.681. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: resnet-v2-50Samsung SSD 980 PRO 1TB48121620SE +/- 0.06, N = 314.61MIN: 14.39 / MAX: 15.811. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: SqueezeNetV1.0Samsung SSD 980 PRO 1TB1.06922.13843.20764.27685.346SE +/- 0.021, N = 34.752MIN: 4.66 / MAX: 5.671. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: MobileNetV2_224Samsung SSD 980 PRO 1TB0.68041.36082.04122.72163.402SE +/- 0.004, N = 33.024MIN: 2.98 / MAX: 4.171. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: mobilenet-v1-1.0Samsung SSD 980 PRO 1TB0.45610.91221.36831.82442.2805SE +/- 0.010, N = 32.027MIN: 1.98 / MAX: 3.141. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

OpenBenchmarking.orgms, Fewer Is BetterMobile Neural Network 2.1Model: inception-v3Samsung SSD 980 PRO 1TB48121620SE +/- 0.09, N = 316.79MIN: 16.52 / MAX: 24.511. (CXX) g++ options: -std=c++11 -O3 -fvisibility=hidden -fomit-frame-pointer -fstrict-aliasing -ffunction-sections -fdata-sections -ffast-math -fno-rtti -fno-exceptions -rdynamic -pthread -ldl

NCNN

NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: mobilenetSamsung SSD 980 PRO 1TB3691215SE +/- 0.13, N = 310.24MIN: 9.92 / MAX: 84.51. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU-v2-v2 - Model: mobilenet-v2Samsung SSD 980 PRO 1TB0.96531.93062.89593.86124.8265SE +/- 0.13, N = 34.29MIN: 4.06 / MAX: 17.291. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU-v3-v3 - Model: mobilenet-v3Samsung SSD 980 PRO 1TB0.9091.8182.7273.6364.545SE +/- 0.08, N = 34.04MIN: 3.8 / MAX: 13.961. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: shufflenet-v2Samsung SSD 980 PRO 1TB1.21732.43463.65194.86926.0865SE +/- 0.55, N = 35.41MIN: 4.73 / MAX: 15.171. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: mnasnetSamsung SSD 980 PRO 1TB0.8731.7462.6193.4924.365SE +/- 0.08, N = 33.88MIN: 3.7 / MAX: 6.351. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: efficientnet-b0Samsung SSD 980 PRO 1TB246810SE +/- 0.10, N = 36.33MIN: 6.07 / MAX: 15.851. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: blazefaceSamsung SSD 980 PRO 1TB0.5131.0261.5392.0522.565SE +/- 0.25, N = 32.28MIN: 1.93 / MAX: 3.541. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: googlenetSamsung SSD 980 PRO 1TB3691215SE +/- 0.07, N = 310.23MIN: 9.99 / MAX: 21.131. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: vgg16Samsung SSD 980 PRO 1TB510152025SE +/- 0.04, N = 319.35MIN: 19.07 / MAX: 29.981. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: resnet18Samsung SSD 980 PRO 1TB246810SE +/- 0.02, N = 36.16MIN: 5.96 / MAX: 17.411. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: alexnetSamsung SSD 980 PRO 1TB0.82351.6472.47053.2944.1175SE +/- 0.02, N = 33.66MIN: 3.54 / MAX: 9.111. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: resnet50Samsung SSD 980 PRO 1TB3691215SE +/- 0.05, N = 311.37MIN: 11.1 / MAX: 22.561. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: yolov4-tinySamsung SSD 980 PRO 1TB48121620SE +/- 0.06, N = 315.69MIN: 14.82 / MAX: 63.831. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: squeezenet_ssdSamsung SSD 980 PRO 1TB3691215SE +/- 0.06, N = 39.51MIN: 9.12 / MAX: 20.191. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: regnety_400mSamsung SSD 980 PRO 1TB48121620SE +/- 1.97, N = 317.59MIN: 14.46 / MAX: 161.611. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: vision_transformerSamsung SSD 980 PRO 1TB918273645SE +/- 0.30, N = 337.14MIN: 36.07 / MAX: 98.771. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: FastestDetSamsung SSD 980 PRO 1TB1.33882.67764.01645.35526.694SE +/- 0.25, N = 35.95MIN: 5.44 / MAX: 17.141. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: mobilenetSamsung SSD 980 PRO 1TB3691215SE +/- 0.03, N = 310.22MIN: 10 / MAX: 21.021. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2Samsung SSD 980 PRO 1TB0.95631.91262.86893.82524.7815SE +/- 0.06, N = 34.25MIN: 4.11 / MAX: 19.631. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3Samsung SSD 980 PRO 1TB0.9091.8182.7273.6364.545SE +/- 0.05, N = 34.04MIN: 3.87 / MAX: 14.951. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: shufflenet-v2Samsung SSD 980 PRO 1TB1.12052.2413.36154.4825.6025SE +/- 0.01, N = 34.98MIN: 4.83 / MAX: 14.331. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: mnasnetSamsung SSD 980 PRO 1TB0.8731.7462.6193.4924.365SE +/- 0.02, N = 33.88MIN: 3.78 / MAX: 11.951. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: efficientnet-b0Samsung SSD 980 PRO 1TB246810SE +/- 0.05, N = 36.30MIN: 6.15 / MAX: 15.91. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: blazefaceSamsung SSD 980 PRO 1TB0.47930.95861.43791.91722.3965SE +/- 0.06, N = 32.13MIN: 2 / MAX: 3.351. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: googlenetSamsung SSD 980 PRO 1TB3691215SE +/- 0.06, N = 310.23MIN: 9.96 / MAX: 21.681. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: vgg16Samsung SSD 980 PRO 1TB510152025SE +/- 0.15, N = 319.60MIN: 19.07 / MAX: 106.651. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: resnet18Samsung SSD 980 PRO 1TB246810SE +/- 0.08, N = 36.24MIN: 6.04 / MAX: 20.461. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: alexnetSamsung SSD 980 PRO 1TB0.8371.6742.5113.3484.185SE +/- 0.06, N = 33.72MIN: 3.52 / MAX: 7.391. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: resnet50Samsung SSD 980 PRO 1TB3691215SE +/- 0.06, N = 311.45MIN: 11.22 / MAX: 23.571. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: yolov4-tinySamsung SSD 980 PRO 1TB48121620SE +/- 0.04, N = 315.69MIN: 14.78 / MAX: 27.111. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: squeezenet_ssdSamsung SSD 980 PRO 1TB3691215SE +/- 0.05, N = 39.60MIN: 9.24 / MAX: 20.721. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: regnety_400mSamsung SSD 980 PRO 1TB48121620SE +/- 0.48, N = 315.76MIN: 14.71 / MAX: 59.831. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: vision_transformerSamsung SSD 980 PRO 1TB918273645SE +/- 0.05, N = 337.14MIN: 36.16 / MAX: 57.211. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: Vulkan GPU - Model: FastestDetSamsung SSD 980 PRO 1TB1.32752.6553.98255.316.6375SE +/- 0.06, N = 35.90MIN: 5.66 / MAX: 16.771. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

TNN

TNN is an open-source deep learning reasoning framework developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: DenseNetSamsung SSD 980 PRO 1TB5001000150020002500SE +/- 1.35, N = 32499.48MIN: 2428.85 / MAX: 2554.761. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: MobileNet v2Samsung SSD 980 PRO 1TB50100150200250SE +/- 0.06, N = 3246.04MIN: 244.84 / MAX: 260.761. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: SqueezeNet v2Samsung SSD 980 PRO 1TB1428425670SE +/- 0.29, N = 360.43MIN: 59.69 / MAX: 60.991. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

OpenBenchmarking.orgms, Fewer Is BetterTNN 0.3Target: CPU - Model: SqueezeNet v1.1Samsung SSD 980 PRO 1TB50100150200250SE +/- 0.11, N = 3250.49MIN: 250.17 / MAX: 250.981. (CXX) g++ options: -fopenmp -pthread -fvisibility=hidden -fvisibility=default -O3 -rdynamic -ldl

PlaidML

This test profile uses PlaidML deep learning framework developed by Intel for offering up various benchmarks. Learn more via the OpenBenchmarking.org test page.

FP16: No - Mode: Inference - Network: VGG16 - Device: CPU

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'Iterable' from 'collections' (/usr/lib/python3.10/collections/__init__.py)

FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'Iterable' from 'collections' (/usr/lib/python3.10/collections/__init__.py)

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 2022.3Model: Face Detection FP16 - Device: CPUSamsung SSD 980 PRO 1TB48121620SE +/- 0.00, N = 315.391. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Face Detection FP16 - Device: CPUSamsung SSD 980 PRO 1TB2004006008001000SE +/- 0.77, N = 31033.18MIN: 900.57 / MAX: 1094.111. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.3Model: Person Detection FP16 - Device: CPUSamsung SSD 980 PRO 1TB3691215SE +/- 0.01, N = 310.161. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Person Detection FP16 - Device: CPUSamsung SSD 980 PRO 1TB30060090012001500SE +/- 2.08, N = 31564.65MIN: 1396.52 / MAX: 1795.31. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.3Model: Person Detection FP32 - Device: CPUSamsung SSD 980 PRO 1TB3691215SE +/- 0.03, N = 310.111. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Person Detection FP32 - Device: CPUSamsung SSD 980 PRO 1TB30060090012001500SE +/- 5.09, N = 31572.80MIN: 1371.92 / MAX: 1836.381. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.3Model: Vehicle Detection FP16 - Device: CPUSamsung SSD 980 PRO 1TB30060090012001500SE +/- 4.82, N = 31432.351. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Vehicle Detection FP16 - Device: CPUSamsung SSD 980 PRO 1TB3691215SE +/- 0.04, N = 311.16MIN: 10.05 / MAX: 33.111. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.3Model: Face Detection FP16-INT8 - Device: CPUSamsung SSD 980 PRO 1TB816243240SE +/- 0.21, N = 336.961. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Face Detection FP16-INT8 - Device: CPUSamsung SSD 980 PRO 1TB90180270360450SE +/- 2.59, N = 3432.40MIN: 402.42 / MAX: 466.311. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.3Model: Vehicle Detection FP16-INT8 - Device: CPUSamsung SSD 980 PRO 1TB5001000150020002500SE +/- 4.19, N = 32524.351. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Vehicle Detection FP16-INT8 - Device: CPUSamsung SSD 980 PRO 1TB246810SE +/- 0.01, N = 36.33MIN: 5.87 / MAX: 25.461. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.3Model: Weld Porosity Detection FP16 - Device: CPUSamsung SSD 980 PRO 1TB30060090012001500SE +/- 0.74, N = 31542.391. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Weld Porosity Detection FP16 - Device: CPUSamsung SSD 980 PRO 1TB3691215SE +/- 0.01, N = 310.36MIN: 8.69 / MAX: 38.361. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.3Model: Machine Translation EN To DE FP16 - Device: CPUSamsung SSD 980 PRO 1TB4080120160200SE +/- 0.71, N = 3159.151. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Machine Translation EN To DE FP16 - Device: CPUSamsung SSD 980 PRO 1TB20406080100SE +/- 0.45, N = 3100.45MIN: 86.37 / MAX: 135.941. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.3Model: Weld Porosity Detection FP16-INT8 - Device: CPUSamsung SSD 980 PRO 1TB8001600240032004000SE +/- 6.50, N = 33764.591. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Weld Porosity Detection FP16-INT8 - Device: CPUSamsung SSD 980 PRO 1TB48121620SE +/- 0.03, N = 316.99MIN: 14.92 / MAX: 31.841. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.3Model: Person Vehicle Bike Detection FP16 - Device: CPUSamsung SSD 980 PRO 1TB400800120016002000SE +/- 4.71, N = 31806.671. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Person Vehicle Bike Detection FP16 - Device: CPUSamsung SSD 980 PRO 1TB246810SE +/- 0.02, N = 38.85MIN: 7.32 / MAX: 26.61. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.3Model: Age Gender Recognition Retail 0013 FP16 - Device: CPUSamsung SSD 980 PRO 1TB11K22K33K44K55KSE +/- 332.81, N = 350822.471. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Age Gender Recognition Retail 0013 FP16 - Device: CPUSamsung SSD 980 PRO 1TB0.28130.56260.84391.12521.4065SE +/- 0.01, N = 31.25MIN: 0.99 / MAX: 27.851. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgFPS, More Is BetterOpenVINO 2022.3Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUSamsung SSD 980 PRO 1TB12K24K36K48K60KSE +/- 223.97, N = 354311.931. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

OpenBenchmarking.orgms, Fewer Is BetterOpenVINO 2022.3Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPUSamsung SSD 980 PRO 1TB0.26330.52660.78991.05321.3165SE +/- 0.00, N = 31.17MIN: 0.92 / MAX: 38.441. (CXX) g++ options: -fPIC -fsigned-char -ffunction-sections -fdata-sections -O3 -fno-strict-overflow -fwrapv -shared

PETSc

PETSc, the Portable, Extensible Toolkit for Scientific Computation, is for the scalable (parallel) solution of scientific applications modeled by partial differential equations. This test profile runs the PETSc "make streams" benchmark and records the throughput rate when all available cores are utilized for the MPI Streams build. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMB/s, More Is BetterPETSc 3.19Test: StreamsSamsung SSD 980 PRO 1TB30K60K90K120K150KSE +/- 21.10, N = 3118164.411. (CC) gcc options: -fPIC -O3 -O2 -lpthread -lm

ECP-CANDLE

The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales. Learn more via the OpenBenchmarking.org test page.

Benchmark: P1B2

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. E: ValueError: decay is deprecated in the new Keras optimizer, please check the docstring for valid arguments, or use the legacy optimizer, e.g., tf.keras.optimizers.legacy.RMSprop.

Benchmark: P3B1

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. E: ValueError: decay is deprecated in the new Keras optimizer, please check the docstring for valid arguments, or use the legacy optimizer, e.g., tf.keras.optimizers.legacy.SGD.

Benchmark: P3B2

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. E: AttributeError: module 'numpy' has no attribute 'bool'.

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: KNN CADSamsung SSD 980 PRO 1TB1632486480SE +/- 0.23, N = 374.30

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative EntropySamsung SSD 980 PRO 1TB3691215SE +/- 0.06, N = 310.56

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed GaussianSamsung SSD 980 PRO 1TB0.86451.7292.59353.4584.3225SE +/- 0.004, N = 33.842

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko SkylineSamsung SSD 980 PRO 1TB1326395265SE +/- 0.13, N = 359.35

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian ChangepointSamsung SSD 980 PRO 1TB714212835SE +/- 0.17, N = 328.31

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Contextual Anomaly Detector OSESamsung SSD 980 PRO 1TB816243240SE +/- 0.06, N = 334.94

ONNX Runtime

ONNX Runtime is developed by Microsoft and partners as a open-source, cross-platform, high performance machine learning inferencing and training accelerator. This test profile runs the ONNX Runtime with various models available from the ONNX Model Zoo. Learn more via the OpenBenchmarking.org test page.

Model: GPT-2 - Device: CPU - Executor: Parallel

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: GPT-2 - Device: CPU - Executor: Standard

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: yolov4 - Device: CPU - Executor: Parallel

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: yolov4 - Device: CPU - Executor: Standard

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: bertsquad-12 - Device: CPU - Executor: Parallel

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: bertsquad-12 - Device: CPU - Executor: Standard

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: fcn-resnet101-11 - Device: CPU - Executor: Standard

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: super-resolution-10 - Device: CPU - Executor: Parallel

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: super-resolution-10 - Device: CPU - Executor: Standard

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./onnx: line 2: ./onnxruntime/build/Linux/Release/onnxruntime_perf_test: No such file or directory

AI Benchmark Alpha

AI Benchmark Alpha is a Python library for evaluating artificial intelligence (AI) performance on diverse hardware platforms and relies upon the TensorFlow machine learning library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterAI Benchmark Alpha 0.1.2Device Inference ScoreSamsung SSD 980 PRO 1TB60012001800240030002967

OpenBenchmarking.orgScore, More Is BetterAI Benchmark Alpha 0.1.2Device Training ScoreSamsung SSD 980 PRO 1TB4008001200160020002000

OpenBenchmarking.orgScore, More Is BetterAI Benchmark Alpha 0.1.2Device AI ScoreSamsung SSD 980 PRO 1TB110022003300440055004967

Faiss

Faiss is developed by Meta/Facebook. Faiss is a library for efficient similarity search and clustering of dense vectors. Learn more via the OpenBenchmarking.org test page.

Test: demo_sift1M

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ./faiss: line 3: ./demos/demo_sift1M: No such file or directory

OpenBenchmarking.orgms per query, Fewer Is BetterFaiss 1.7.4Test: bench_polysemous_sift1m - PQ baselineSamsung SSD 980 PRO 1TB0.84351.6872.53053.3744.2175SE +/- 0.008, N = 33.7491. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
OpenBenchmarking.orgms per query, Fewer Is BetterFaiss 1.7.4Test: bench_polysemous_sift1m - Polysemous 64Samsung SSD 980 PRO 1TB1.34012.68024.02035.36046.7005SE +/- 0.002, N = 35.9561. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
OpenBenchmarking.orgms per query, Fewer Is BetterFaiss 1.7.4Test: bench_polysemous_sift1m - Polysemous 62Samsung SSD 980 PRO 1TB1.10182.20363.30544.40725.509SE +/- 0.003, N = 34.8971. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
OpenBenchmarking.orgms per query, Fewer Is BetterFaiss 1.7.4Test: bench_polysemous_sift1m - Polysemous 58Samsung SSD 980 PRO 1TB0.68151.3632.04452.7263.4075SE +/- 0.003, N = 33.0291. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
OpenBenchmarking.orgms per query, Fewer Is BetterFaiss 1.7.4Test: bench_polysemous_sift1m - Polysemous 54Samsung SSD 980 PRO 1TB0.42910.85821.28731.71642.1455SE +/- 0.002, N = 31.9071. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
OpenBenchmarking.orgms per query, Fewer Is BetterFaiss 1.7.4Test: bench_polysemous_sift1m - Polysemous 50Samsung SSD 980 PRO 1TB0.27630.55260.82891.10521.3815SE +/- 0.000, N = 31.2281. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
OpenBenchmarking.orgms per query, Fewer Is BetterFaiss 1.7.4Test: bench_polysemous_sift1m - Polysemous 46Samsung SSD 980 PRO 1TB0.19640.39280.58920.78560.982SE +/- 0.000, N = 30.8731. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
OpenBenchmarking.orgms per query, Fewer Is BetterFaiss 1.7.4Test: bench_polysemous_sift1m - Polysemous 42Samsung SSD 980 PRO 1TB0.16380.32760.49140.65520.819SE +/- 0.000, N = 30.7281. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
OpenBenchmarking.orgms per query, Fewer Is BetterFaiss 1.7.4Test: bench_polysemous_sift1m - Polysemous 38Samsung SSD 980 PRO 1TB0.15320.30640.45960.61280.766SE +/- 0.001, N = 30.6811. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
OpenBenchmarking.orgms per query, Fewer Is BetterFaiss 1.7.4Test: bench_polysemous_sift1m - Polysemous 34Samsung SSD 980 PRO 1TB0.14960.29920.44880.59840.748SE +/- 0.001, N = 30.6651. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc
OpenBenchmarking.orgms per query, Fewer Is BetterFaiss 1.7.4Test: bench_polysemous_sift1m - Polysemous 30Samsung SSD 980 PRO 1TB0.14830.29660.44490.59320.7415SE +/- 0.001, N = 30.6591. (F9X) gfortran options: -O2 -frecursive -m64 -fopenmp -msse3 -mssse3 -msse4.1 -mavx -mavx2 -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_icaSamsung SSD 980 PRO 1TB816243240SE +/- 0.07, N = 332.96

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_qdaSamsung SSD 980 PRO 1TB612182430SE +/- 0.01, N = 325.15

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_svmSamsung SSD 980 PRO 1TB510152025SE +/- 0.03, N = 319.51

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_linearridgeregressionSamsung SSD 980 PRO 1TB0.1980.3960.5940.7920.99SE +/- 0.01, N = 150.88

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

Device: CPU - Backend: JAX - Project Size: 16384 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 16384 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 65536 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 65536 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 262144 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 262144 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 1048576 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 1048576 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 16384 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 16384 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 65536 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 65536 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of StateSamsung SSD 980 PRO 1TB0.00050.0010.00150.0020.0025SE +/- 0.000, N = 30.002

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral MixingSamsung SSD 980 PRO 1TB0.0020.0040.0060.0080.01SE +/- 0.000, N = 30.009

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of StateSamsung SSD 980 PRO 1TB0.00270.00540.00810.01080.0135SE +/- 0.000, N = 150.012

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral MixingSamsung SSD 980 PRO 1TB0.00720.01440.02160.02880.036SE +/- 0.000, N = 30.032

Device: CPU - Backend: Aesara - Project Size: 16384 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 16384 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 65536 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 65536 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 262144 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 262144 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of StateSamsung SSD 980 PRO 1TB0.00990.01980.02970.03960.0495SE +/- 0.000, N = 30.044

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral MixingSamsung SSD 980 PRO 1TB0.02610.05220.07830.10440.1305SE +/- 0.000, N = 30.116

Device: CPU - Backend: Aesara - Project Size: 262144 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 262144 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 1048576 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 1048576 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of StateSamsung SSD 980 PRO 1TB0.03650.0730.10950.1460.1825SE +/- 0.001, N = 30.162

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral MixingSamsung SSD 980 PRO 1TB0.10640.21280.31920.42560.532SE +/- 0.002, N = 30.473

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of StateSamsung SSD 980 PRO 1TB0.21420.42840.64260.85681.071SE +/- 0.001, N = 30.952

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral MixingSamsung SSD 980 PRO 1TB0.44440.88881.33321.77762.222SE +/- 0.007, N = 31.975

Device: CPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Equation of State

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Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Equation of State

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Isoneutral Mixing

Samsung SSD 980 PRO 1TB: The test run did not produce a result. The test run did not produce a result. The test run did not produce a result.

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

Benchmark: GLM

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: SAGA

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'sklearn.utils.parallel'

Benchmark: Tree

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Lasso

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Glmnet

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'glmnet'

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SparsifySamsung SSD 980 PRO 1TB20406080100SE +/- 0.20, N = 395.421. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Benchmark: Plot Ward

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: MNIST Dataset

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: TypeError: fetch_openml() got an unexpected keyword argument 'parser'

Benchmark: Plot Neighbors

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: SGD Regression

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: SGDOneClassSVM

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'SGDOneClassSVM' from 'sklearn.linear_model' (/usr/lib/python3/dist-packages/sklearn/linear_model/__init__.py)

Benchmark: Plot Lasso Path

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Isolation Forest

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Plot Fast KMeans

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Text VectorizersSamsung SSD 980 PRO 1TB1326395265SE +/- 0.08, N = 358.801. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Benchmark: Plot Hierarchical

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Plot OMP vs. LARS

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: TypeError: make_sparse_coded_signal() got an unexpected keyword argument 'data_transposed'

Benchmark: Feature Expansions

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: LocalOutlierFactor

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: TSNE MNIST Dataset

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: TypeError: fetch_openml() got an unexpected keyword argument 'parser'

Benchmark: Isotonic / Logistic

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Plot Incremental PCA

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Hist Gradient Boosting

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Plot Parallel Pairwise

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Isotonic / Pathological

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: RCV1 Logreg Convergencet

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Sample Without Replacement

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Covertype Dataset BenchmarkSamsung SSD 980 PRO 1TB80160240320400SE +/- 0.45, N = 3347.991. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Benchmark: Hist Gradient Boosting Adult

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'HistGradientBoostingClassifier' from 'sklearn.ensemble' (/usr/lib/python3/dist-packages/sklearn/ensemble/__init__.py)

Benchmark: Isotonic / Perturbed Logarithm

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Hist Gradient Boosting Threading

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'HistGradientBoostingRegressor' from 'sklearn.ensemble' (/usr/lib/python3/dist-packages/sklearn/ensemble/__init__.py)

Benchmark: Plot Singular Value Decomposition

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Hist Gradient Boosting Higgs Boson

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'HistGradientBoostingClassifier' from 'sklearn.ensemble' (/usr/lib/python3/dist-packages/sklearn/ensemble/__init__.py)

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: 20 Newsgroups / Logistic RegressionSamsung SSD 980 PRO 1TB714212835SE +/- 0.38, N = 430.831. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Benchmark: Plot Polynomial Kernel Approximation

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Plot Non-Negative Matrix Factorization

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Hist Gradient Boosting Categorical Only

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'HistGradientBoostingClassifier' from 'sklearn.ensemble' (/usr/lib/python3/dist-packages/sklearn/ensemble/__init__.py)

Benchmark: Kernel PCA Solvers / Time vs. N Samples

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

Benchmark: Kernel PCA Solvers / Time vs. N Components

Samsung SSD 980 PRO 1TB: The test quit with a non-zero exit status. The test quit with a non-zero exit status. The test quit with a non-zero exit status. E: ImportError: cannot import name 'docstring' from 'matplotlib' (/home/incooling/.local/lib/python3.10/site-packages/matplotlib/__init__.py)

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Sparse Random Projections / 100 IterationsSamsung SSD 980 PRO 1TB100200300400500SE +/- 0.25, N = 3478.561. (F9X) gfortran options: -O3 -fopenmp -fno-tree-vectorize -lm -lpthread -lgfortran -lc

Whisper.cpp

Whisper.cpp is a port of OpenAI's Whisper model in C/C++. Whisper.cpp is developed by Georgi Gerganov for transcribing WAV audio files to text / speech recognition. Whisper.cpp supports ARM NEON, x86 AVX, and other advanced CPU features. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterWhisper.cpp 1.4Model: ggml-base.en - Input: 2016 State of the UnionSamsung SSD 980 PRO 1TB50100150200250SE +/- 6.03, N = 12208.251. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread

OpenBenchmarking.orgSeconds, Fewer Is BetterWhisper.cpp 1.4Model: ggml-small.en - Input: 2016 State of the UnionSamsung SSD 980 PRO 1TB100200300400500SE +/- 15.93, N = 9475.981. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread

OpenBenchmarking.orgSeconds, Fewer Is BetterWhisper.cpp 1.4Model: ggml-medium.en - Input: 2016 State of the UnionSamsung SSD 980 PRO 1TB2004006008001000SE +/- 27.09, N = 9959.471. (CXX) g++ options: -O3 -std=c++11 -fPIC -pthread

Kripke

Kripke is a simple, scalable, 3D Sn deterministic particle transport code. Its primary purpose is to research how data layout, programming paradigms and architectures effect the implementation and performance of Sn transport. Kripke is developed by LLNL. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgThroughput FoM, More Is BetterKripke 1.2.6Samsung SSD 980 PRO 1TB70M140M210M280M350MSE +/- 387235.49, N = 33158812671. (CXX) g++ options: -O3 -fopenmp -ldl

OpenCV

This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenCV 4.7Test: DNN - Deep Neural NetworkSamsung SSD 980 PRO 1TB5K10K15K20K25KSE +/- 279.29, N = 15241311. (CXX) g++ options: -fPIC -fsigned-char -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections -msse -msse2 -msse3 -fvisibility=hidden -O3 -shared

409 Results Shown

IOR:
  2MB - Default Test Directory
  4MB - Default Test Directory
  8MB - Default Test Directory
  16MB - Default Test Directory
  32MB - Default Test Directory
  64MB - Default Test Directory
  256MB - Default Test Directory
  512MB - Default Test Directory
  1024MB - Default Test Directory
High Performance Conjugate Gradient:
  104 104 104 - 60
  144 144 144 - 60
  160 160 160 - 60
  104 104 104 - 1800
  144 144 144 - 1800
  160 160 160 - 1800
HPL Linpack
NAS Parallel Benchmarks:
  BT.C
  CG.C
  EP.C
  EP.D
  FT.C
  IS.D
  LU.C
  MG.C
  SP.B
  SP.C
HPC Challenge:
  G-HPL
  G-Ffte
  EP-DGEMM
  G-Ptrans
  EP-STREAM Triad
  G-Rand Access
  Rand Ring Latency
  Rand Ring Bandwidth
  Max Ping Pong Bandwidth
LeelaChessZero
Parboil:
  OpenMP LBM
  OpenMP CUTCP
  OpenMP Stencil
  OpenMP MRI Gridding
miniFE
miniBUDE:
  OpenMP - BM1:
    GFInst/s
    Billion Interactions/s
  OpenMP - BM2:
    GFInst/s
    Billion Interactions/s
CloverLeaf
Rodinia:
  OpenMP LavaMD
  OpenMP Leukocyte
  OpenMP CFD Solver
  OpenMP Streamcluster
CP2K Molecular Dynamics:
  H20-64
  Fayalite-FIST
NAMD
Dolfyn
Nebular Empirical Analysis Tool
Algebraic Multi-Grid Benchmark
libxsmm:
  128
  256
  32
  64
FFTE
Laghos:
  Triple Point Problem
  Sedov Blast Wave, ube_922_hex.mesh
FFTW:
  Stock - 1D FFT Size 32
  Stock - 2D FFT Size 32
  Stock - 1D FFT Size 4096
  Stock - 2D FFT Size 4096
  Float + SSE - 1D FFT Size 32
  Float + SSE - 2D FFT Size 32
  Float + SSE - 1D FFT Size 4096
  Float + SSE - 2D FFT Size 4096
HeFFTe - Highly Efficient FFT for Exascale:
  c2c - FFTW - float - 128
  c2c - FFTW - float - 256
  c2c - FFTW - float - 512
  r2c - FFTW - float - 128
  r2c - FFTW - float - 256
  r2c - FFTW - float - 512
  c2c - FFTW - double - 128
  c2c - FFTW - double - 256
  c2c - FFTW - double - 512
  c2c - Stock - float - 128
  c2c - Stock - float - 256
  c2c - Stock - float - 512
  r2c - FFTW - double - 128
  r2c - FFTW - double - 256
  r2c - FFTW - double - 512
  r2c - Stock - float - 128
  r2c - Stock - float - 256
  r2c - Stock - float - 512
  c2c - Stock - double - 128
  c2c - Stock - double - 256
  c2c - Stock - double - 512
  r2c - Stock - double - 128
  r2c - Stock - double - 256
  r2c - Stock - double - 512
  c2c - FFTW - float-long - 128
  c2c - FFTW - float-long - 256
  c2c - FFTW - float-long - 512
  r2c - FFTW - float-long - 128
  r2c - FFTW - float-long - 256
  r2c - FFTW - float-long - 512
  c2c - FFTW - double-long - 128
  c2c - FFTW - double-long - 256
  c2c - FFTW - double-long - 512
  c2c - Stock - float-long - 128
  c2c - Stock - float-long - 256
  c2c - Stock - float-long - 512
  r2c - FFTW - double-long - 128
  r2c - FFTW - double-long - 256
  r2c - FFTW - double-long - 512
  r2c - Stock - float-long - 128
  r2c - Stock - float-long - 256
  r2c - Stock - float-long - 512
  c2c - Stock - double-long - 128
  c2c - Stock - double-long - 256
  c2c - Stock - double-long - 512
  r2c - Stock - double-long - 128
  r2c - Stock - double-long - 256
  r2c - Stock - double-long - 512
Pennant:
  sedovbig
  leblancbig
Palabos:
  100
  400
  500
  1000
Timed MrBayes Analysis
NWChem
QMCPACK:
  Li2_STO_ae
  simple-H2O
  FeCO6_b3lyp_gms
  FeCO6_b3lyp_gms
Timed HMMer Search
Xcompact3d Incompact3d:
  X3D-benchmarking input.i3d
  input.i3d 129 Cells Per Direction
  input.i3d 193 Cells Per Direction
Timed MAFFT Alignment
Monte Carlo Simulations of Ionised Nebulae
OpenFOAM:
  motorBike - Mesh Time
  motorBike - Execution Time
  drivaerFastback, Large Mesh Size - Mesh Time
  drivaerFastback, Large Mesh Size - Execution Time
  drivaerFastback, Small Mesh Size - Mesh Time
  drivaerFastback, Small Mesh Size - Execution Time
  drivaerFastback, Medium Mesh Size - Mesh Time
  drivaerFastback, Medium Mesh Size - Execution Time
OpenRadioss:
  Bumper Beam
  Chrysler Neon 1M
  Cell Phone Drop Test
  Bird Strike on Windshield
  Rubber O-Ring Seal Installation
Quantum ESPRESSO
RELION
Remhos
SPECFEM3D:
  Mount St. Helens
  Layered Halfspace
  Tomographic Model
  Homogeneous Halfspace
  Water-layered Halfspace
nekRS:
  Kershaw
  TurboPipe Periodic
LAMMPS Molecular Dynamics Simulator:
  20k Atoms
  Rhodopsin Protein
LULESH
ArrayFire
ACES DGEMM
Himeno Benchmark
oneDNN:
  IP Shapes 1D - f32 - CPU
  IP Shapes 3D - f32 - CPU
  IP Shapes 1D - u8s8f32 - CPU
  IP Shapes 3D - u8s8f32 - CPU
  Convolution Batch Shapes Auto - f32 - CPU
  Deconvolution Batch shapes_1d - f32 - CPU
  Deconvolution Batch shapes_3d - f32 - CPU
  Convolution Batch Shapes Auto - u8s8f32 - CPU
  Deconvolution Batch shapes_1d - u8s8f32 - CPU
  Deconvolution Batch shapes_3d - u8s8f32 - CPU
  Recurrent Neural Network Training - f32 - CPU
  Recurrent Neural Network Inference - f32 - CPU
  Recurrent Neural Network Training - u8s8f32 - CPU
  Recurrent Neural Network Inference - u8s8f32 - CPU
  Recurrent Neural Network Training - bf16bf16bf16 - CPU
  Recurrent Neural Network Inference - bf16bf16bf16 - CPU
Numpy Benchmark
DeepSpeech
R Benchmark
RNNoise
ASKAP:
  tConvolve MT - Gridding
  tConvolve MT - Degridding
  tConvolve MPI - Degridding
  tConvolve MPI - Gridding
  tConvolve OpenMP - Gridding
  tConvolve OpenMP - Degridding
  Hogbom Clean OpenMP
Graph500:
  26:
    bfs median_TEPS
    bfs max_TEPS
    sssp median_TEPS
    sssp max_TEPS
Intel MPI Benchmarks:
  IMB-P2P PingPong
  IMB-MPI1 Exchange
  IMB-MPI1 Exchange
  IMB-MPI1 PingPong
  IMB-MPI1 Sendrecv
  IMB-MPI1 Sendrecv
GROMACS
Darmstadt Automotive Parallel Heterogeneous Suite:
  OpenMP - NDT Mapping
  OpenMP - Points2Image
  OpenMP - Euclidean Cluster
TensorFlow Lite:
  SqueezeNet
  Inception V4
  NASNet Mobile
  Mobilenet Float
  Mobilenet Quant
  Inception ResNet V2
TensorFlow:
  CPU - 16 - VGG-16
  CPU - 32 - VGG-16
  CPU - 64 - VGG-16
  CPU - 16 - AlexNet
  CPU - 256 - VGG-16
  CPU - 32 - AlexNet
  CPU - 512 - VGG-16
  CPU - 64 - AlexNet
  CPU - 256 - AlexNet
  CPU - 512 - AlexNet
  CPU - 16 - GoogLeNet
  CPU - 16 - ResNet-50
  CPU - 32 - GoogLeNet
  CPU - 32 - ResNet-50
  CPU - 64 - GoogLeNet
  CPU - 64 - ResNet-50
  CPU - 256 - GoogLeNet
  CPU - 256 - ResNet-50
  CPU - 512 - GoogLeNet
  CPU - 512 - ResNet-50
GNU Octave Benchmark
Neural Magic DeepSparse:
  NLP Document Classification, oBERT base uncased on IMDB - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Document Classification, oBERT base uncased on IMDB - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Synchronous Single-Stream:
    items/sec
    ms/batch
  ResNet-50, Baseline - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  ResNet-50, Baseline - Synchronous Single-Stream:
    items/sec
    ms/batch
  ResNet-50, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  ResNet-50, Sparse INT8 - Synchronous Single-Stream:
    items/sec
    ms/batch
  CV Detection, YOLOv5s COCO - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Detection, YOLOv5s COCO - Synchronous Single-Stream:
    items/sec
    ms/batch
  BERT-Large, NLP Question Answering - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  BERT-Large, NLP Question Answering - Synchronous Single-Stream:
    items/sec
    ms/batch
  CV Classification, ResNet-50 ImageNet - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Classification, ResNet-50 ImageNet - Synchronous Single-Stream:
    items/sec
    ms/batch
  CV Detection, YOLOv5s COCO, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Detection, YOLOv5s COCO, Sparse INT8 - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Text Classification, DistilBERT mnli - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Text Classification, DistilBERT mnli - Synchronous Single-Stream:
    items/sec
    ms/batch
  CV Segmentation, 90% Pruned YOLACT Pruned - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  CV Segmentation, 90% Pruned YOLACT Pruned - Synchronous Single-Stream:
    items/sec
    ms/batch
  BERT-Large, NLP Question Answering, Sparse INT8 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  BERT-Large, NLP Question Answering, Sparse INT8 - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Text Classification, BERT base uncased SST2 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Text Classification, BERT base uncased SST2 - Synchronous Single-Stream:
    items/sec
    ms/batch
  NLP Token Classification, BERT base uncased conll2003 - Asynchronous Multi-Stream:
    items/sec
    ms/batch
  NLP Token Classification, BERT base uncased conll2003 - Synchronous Single-Stream:
    items/sec
    ms/batch
spaCy:
  en_core_web_lg
  en_core_web_trf
Caffe:
  AlexNet - CPU - 100
  AlexNet - CPU - 200
  AlexNet - CPU - 1000
  GoogleNet - CPU - 100
  GoogleNet - CPU - 200
  GoogleNet - CPU - 1000
WRF
GPAW
Mobile Neural Network:
  nasnet
  mobilenetV3
  squeezenetv1.1
  resnet-v2-50
  SqueezeNetV1.0
  MobileNetV2_224
  mobilenet-v1-1.0
  inception-v3
NCNN:
  CPU - mobilenet
  CPU-v2-v2 - mobilenet-v2
  CPU-v3-v3 - mobilenet-v3
  CPU - shufflenet-v2
  CPU - mnasnet
  CPU - efficientnet-b0
  CPU - blazeface
  CPU - googlenet
  CPU - vgg16
  CPU - resnet18
  CPU - alexnet
  CPU - resnet50
  CPU - yolov4-tiny
  CPU - squeezenet_ssd
  CPU - regnety_400m
  CPU - vision_transformer
  CPU - FastestDet
  Vulkan GPU - mobilenet
  Vulkan GPU-v2-v2 - mobilenet-v2
  Vulkan GPU-v3-v3 - mobilenet-v3
  Vulkan GPU - shufflenet-v2
  Vulkan GPU - mnasnet
  Vulkan GPU - efficientnet-b0
  Vulkan GPU - blazeface
  Vulkan GPU - googlenet
  Vulkan GPU - vgg16
  Vulkan GPU - resnet18
  Vulkan GPU - alexnet
  Vulkan GPU - resnet50
  Vulkan GPU - yolov4-tiny
  Vulkan GPU - squeezenet_ssd
  Vulkan GPU - regnety_400m
  Vulkan GPU - vision_transformer
  Vulkan GPU - FastestDet
TNN:
  CPU - DenseNet
  CPU - MobileNet v2
  CPU - SqueezeNet v2
  CPU - SqueezeNet v1.1
OpenVINO:
  Face Detection FP16 - CPU:
    FPS
    ms
  Person Detection FP16 - CPU:
    FPS
    ms
  Person Detection FP32 - CPU:
    FPS
    ms
  Vehicle Detection FP16 - CPU:
    FPS
    ms
  Face Detection FP16-INT8 - CPU:
    FPS
    ms
  Vehicle Detection FP16-INT8 - CPU:
    FPS
    ms
  Weld Porosity Detection FP16 - CPU:
    FPS
    ms
  Machine Translation EN To DE FP16 - CPU:
    FPS
    ms
  Weld Porosity Detection FP16-INT8 - CPU:
    FPS
    ms
  Person Vehicle Bike Detection FP16 - CPU:
    FPS
    ms
  Age Gender Recognition Retail 0013 FP16 - CPU:
    FPS
    ms
  Age Gender Recognition Retail 0013 FP16-INT8 - CPU:
    FPS
    ms
PETSc
Numenta Anomaly Benchmark:
  KNN CAD
  Relative Entropy
  Windowed Gaussian
  Earthgecko Skyline
  Bayesian Changepoint
  Contextual Anomaly Detector OSE
AI Benchmark Alpha:
  Device Inference Score
  Device Training Score
  Device AI Score
Faiss
Mlpack Benchmark:
  scikit_ica
  scikit_qda
  scikit_svm
  scikit_linearridgeregression
PyHPC Benchmarks:
  CPU - Numpy - 16384 - Equation of State
  CPU - Numpy - 16384 - Isoneutral Mixing
  CPU - Numpy - 65536 - Equation of State
  CPU - Numpy - 65536 - Isoneutral Mixing
  CPU - Numpy - 262144 - Equation of State
  CPU - Numpy - 262144 - Isoneutral Mixing
  CPU - Numpy - 1048576 - Equation of State
  CPU - Numpy - 1048576 - Isoneutral Mixing
  CPU - Numpy - 4194304 - Equation of State
  CPU - Numpy - 4194304 - Isoneutral Mixing
Scikit-Learn:
  Sparsify
  Text Vectorizers
  Covertype Dataset Benchmark
  20 Newsgroups / Logistic Regression
  Sparse Rand Projections / 100 Iterations
Whisper.cpp:
  ggml-base.en - 2016 State of the Union
  ggml-small.en - 2016 State of the Union
  ggml-medium.en - 2016 State of the Union
Kripke
OpenCV

Samsung SSD 980 PRO 1TB

Processor: AMD Ryzen Threadripper PRO 5995WX 64-Cores @ 2.70GHz (64 Cores), Motherboard: ASRock WRX80 Creator (5.01 BIOS), Chipset: AMD Starship/Matisse, Memory: 256GB, Disk: 1000GB Samsung SSD 980 PRO 1TB, Graphics: llvmpipe, Audio: AMD Starship/Matisse, Network: 2 x Intel X710 for 10GBASE-T + Intel Wi-Fi 6 AX210/AX211/AX411

OS: Ubuntu 22.04, Kernel: 6.2.0-33-generic (x86_64), Desktop: GNOME Shell 42.9, Display Server: X Server 1.21.1.3, OpenGL: 4.5 Mesa 23.0.4-0ubuntu1~22.04.1 (LLVM 15.0.7 256 bits), Vulkan: 1.3.238, Compiler: GCC 11.4.0, File-System: ext4, Screen Resolution: 1024x768

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --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
Disk Notes: NONE / errors=remount-ro,relatime,rw / Block Size: 4096
Processor Notes: Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa008204
Python Notes: Python 3.10.12
Security Notes: 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_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: disabled RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 22 September 2023 15:31 by user ooling.