svc05_hpc_run_1_23-09-22
v1.59
HTML result view exported from: https://openbenchmarking.org/result/2309266-NE-SVC05HPCR92&grr.
HPC Challenge
Test / Class: G-HPL
HPL Linpack
TensorFlow
Device: CPU - Batch Size: 512 - Model: VGG-16
OpenFOAM
Input: drivaerFastback, Large Mesh Size - Execution Time
OpenFOAM
Input: drivaerFastback, Large Mesh Size - Mesh Time
WRF
Input: conus 2.5km
IOR
Block Size: 1024MB - Disk Target: Default Test Directory
IOR
Block Size: 512MB - Disk Target: Default Test Directory
Whisper.cpp
Model: ggml-medium.en - Input: 2016 State of the Union
TensorFlow
Device: CPU - Batch Size: 256 - Model: VGG-16
High Performance Conjugate Gradient
X Y Z: 160 160 160 - RT: 1800
High Performance Conjugate Gradient
X Y Z: 144 144 144 - RT: 1800
High Performance Conjugate Gradient
X Y Z: 104 104 104 - RT: 1800
IOR
Block Size: 256MB - Disk Target: Default Test Directory
TensorFlow
Device: CPU - Batch Size: 512 - Model: ResNet-50
Whisper.cpp
Model: ggml-small.en - Input: 2016 State of the Union
RELION
Test: Basic - Device: CPU
TensorFlow
Device: CPU - Batch Size: 256 - Model: ResNet-50
LeelaChessZero
Backend: BLAS
IOR
Block Size: 32MB - Disk Target: Default Test Directory
High Performance Conjugate Gradient
X Y Z: 160 160 160 - RT: 60
Whisper.cpp
Model: ggml-base.en - Input: 2016 State of the Union
Caffe
Model: GoogleNet - Acceleration: CPU - Iterations: 1000
TensorFlow
Device: CPU - Batch Size: 64 - Model: VGG-16
TensorFlow
Device: CPU - Batch Size: 512 - Model: GoogLeNet
High Performance Conjugate Gradient
X Y Z: 144 144 144 - RT: 60
Scikit-Learn
Benchmark: Sparse Random Projections / 100 Iterations
Xcompact3d Incompact3d
Input: X3D-benchmarking input.i3d
NWChem
Input: C240 Buckyball
Scikit-Learn
Benchmark: Covertype Dataset Benchmark
IOR
Block Size: 64MB - Disk Target: Default Test Directory
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
TensorFlow
Device: CPU - Batch Size: 32 - Model: VGG-16
TensorFlow
Device: CPU - Batch Size: 256 - Model: GoogLeNet
AI Benchmark Alpha
Device AI Score
AI Benchmark Alpha
Device Training Score
AI Benchmark Alpha
Device Inference Score
OpenRadioss
Model: Chrysler Neon 1M
libxsmm
M N K: 128
Graph500
Scale: 26
Graph500
Scale: 26
Graph500
Scale: 26
Graph500
Scale: 26
FFTW
Build: Float + SSE - Size: 2D FFT Size 4096
IOR
Block Size: 8MB - Disk Target: Default Test Directory
TensorFlow
Device: CPU - Batch Size: 512 - Model: AlexNet
Caffe
Model: AlexNet - Acceleration: CPU - Iterations: 1000
Quantum ESPRESSO
Input: AUSURF112
TensorFlow
Device: CPU - Batch Size: 64 - Model: ResNet-50
Intel MPI Benchmarks
Test: IMB-MPI1 Exchange
Intel MPI Benchmarks
Test: IMB-MPI1 Exchange
Faiss
Test: bench_polysemous_sift1m - Polysemous 30
Faiss
Test: bench_polysemous_sift1m - Polysemous 34
Faiss
Test: bench_polysemous_sift1m - Polysemous 38
Faiss
Test: bench_polysemous_sift1m - Polysemous 42
Faiss
Test: bench_polysemous_sift1m - Polysemous 46
Faiss
Test: bench_polysemous_sift1m - Polysemous 50
Faiss
Test: bench_polysemous_sift1m - Polysemous 54
Faiss
Test: bench_polysemous_sift1m - Polysemous 58
Faiss
Test: bench_polysemous_sift1m - Polysemous 62
Faiss
Test: bench_polysemous_sift1m - Polysemous 64
Faiss
Test: bench_polysemous_sift1m - PQ baseline
OpenFOAM
Input: drivaerFastback, Medium Mesh Size - Execution Time
OpenFOAM
Input: drivaerFastback, Medium Mesh Size - Mesh Time
IOR
Block Size: 4MB - Disk Target: Default Test Directory
High Performance Conjugate Gradient
X Y Z: 104 104 104 - RT: 60
LAMMPS Molecular Dynamics Simulator
Model: 20k Atoms
Intel MPI Benchmarks
Test: IMB-MPI1 Sendrecv
Intel MPI Benchmarks
Test: IMB-MPI1 Sendrecv
Parboil
Test: OpenMP MRI Gridding
Mlpack Benchmark
Benchmark: scikit_linearridgeregression
TensorFlow
Device: CPU - Batch Size: 16 - Model: VGG-16
QMCPACK
Input: FeCO6_b3lyp_gms
QMCPACK
Input: FeCO6_b3lyp_gms
Monte Carlo Simulations of Ionised Nebulae
Input: Dust 2D tau100.0
TNN
Target: CPU - Model: DenseNet
FFTW
Build: Stock - Size: 2D FFT Size 4096
PETSc
Test: Streams
Caffe
Model: GoogleNet - Acceleration: CPU - Iterations: 200
TensorFlow
Device: CPU - Batch Size: 256 - Model: AlexNet
Numpy Benchmark
Palabos
Grid Size: 400
libxsmm
M N K: 256
Palabos
Grid Size: 500
Palabos
Grid Size: 1000
TensorFlow
Device: CPU - Batch Size: 32 - Model: ResNet-50
Scikit-Learn
Benchmark: Sparsify
IOR
Block Size: 16MB - Disk Target: Default Test Directory
ASKAP
Test: tConvolve MT - Degridding
ASKAP
Test: tConvolve MT - Gridding
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
OpenCV
Test: DNN - Deep Neural Network
Mobile Neural Network
Model: inception-v3
Mobile Neural Network
Model: mobilenet-v1-1.0
Mobile Neural Network
Model: MobileNetV2_224
Mobile Neural Network
Model: SqueezeNetV1.0
Mobile Neural Network
Model: resnet-v2-50
Mobile Neural Network
Model: squeezenetv1.1
Mobile Neural Network
Model: mobilenetV3
Mobile Neural Network
Model: nasnet
nekRS
Input: TurboPipe Periodic
IOR
Block Size: 2MB - Disk Target: Default Test Directory
NAS Parallel Benchmarks
Test / Class: EP.D
OpenRadioss
Model: Bird Strike on Windshield
Timed MrBayes Analysis
Primate Phylogeny Analysis
Palabos
Grid Size: 100
Timed HMMer Search
Pfam Database Search
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixing
QMCPACK
Input: Li2_STO_ae
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream
TensorFlow
Device: CPU - Batch Size: 64 - Model: GoogLeNet
oneDNN
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Scikit-Learn
Benchmark: Text Vectorizers
nekRS
Input: Kershaw
DeepSpeech
Acceleration: CPU
Caffe
Model: GoogleNet - Acceleration: CPU - Iterations: 100
oneDNN
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
Numenta Anomaly Benchmark
Detector: KNN CAD
TensorFlow
Device: CPU - Batch Size: 16 - Model: ResNet-50
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: double - X Y Z: 512
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 512
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: double - X Y Z: 512
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 512
Darmstadt Automotive Parallel Heterogeneous Suite
Backend: OpenMP - Kernel: Points2Image
OpenVINO
Model: Person Detection FP16 - Device: CPU
OpenVINO
Model: Person Detection FP16 - Device: CPU
NCNN
Target: CPU - Model: FastestDet
NCNN
Target: CPU - Model: vision_transformer
NCNN
Target: CPU - Model: regnety_400m
NCNN
Target: CPU - Model: squeezenet_ssd
NCNN
Target: CPU - Model: yolov4-tiny
NCNN
Target: CPU - Model: resnet50
NCNN
Target: CPU - Model: alexnet
NCNN
Target: CPU - Model: resnet18
NCNN
Target: CPU - Model: vgg16
NCNN
Target: CPU - Model: googlenet
NCNN
Target: CPU - Model: blazeface
NCNN
Target: CPU - Model: efficientnet-b0
NCNN
Target: CPU - Model: mnasnet
NCNN
Target: CPU - Model: shufflenet-v2
NCNN
Target: CPU-v3-v3 - Model: mobilenet-v3
NCNN
Target: CPU-v2-v2 - Model: mobilenet-v2
NCNN
Target: CPU - Model: mobilenet
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of State
OpenVINO
Model: Person Detection FP32 - Device: CPU
OpenVINO
Model: Person Detection FP32 - Device: CPU
Laghos
Test: Sedov Blast Wave, ube_922_hex.mesh
NCNN
Target: Vulkan GPU - Model: FastestDet
NCNN
Target: Vulkan GPU - Model: vision_transformer
NCNN
Target: Vulkan GPU - Model: regnety_400m
NCNN
Target: Vulkan GPU - Model: squeezenet_ssd
NCNN
Target: Vulkan GPU - Model: yolov4-tiny
NCNN
Target: Vulkan GPU - Model: resnet50
NCNN
Target: Vulkan GPU - Model: alexnet
NCNN
Target: Vulkan GPU - Model: resnet18
NCNN
Target: Vulkan GPU - Model: vgg16
NCNN
Target: Vulkan GPU - Model: googlenet
NCNN
Target: Vulkan GPU - Model: blazeface
NCNN
Target: Vulkan GPU - Model: efficientnet-b0
NCNN
Target: Vulkan GPU - Model: mnasnet
NCNN
Target: Vulkan GPU - Model: shufflenet-v2
NCNN
Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3
NCNN
Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2
NCNN
Target: Vulkan GPU - Model: mobilenet
OpenVINO
Model: Face Detection FP16 - Device: CPU
OpenVINO
Model: Face Detection FP16 - Device: CPU
Mlpack Benchmark
Benchmark: scikit_qda
OpenVINO
Model: Face Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Face Detection FP16-INT8 - Device: CPU
OpenRadioss
Model: Bumper Beam
OpenVINO
Model: Machine Translation EN To DE FP16 - Device: CPU
OpenVINO
Model: Machine Translation EN To DE FP16 - Device: CPU
OpenVINO
Model: Person Vehicle Bike Detection FP16 - Device: CPU
OpenVINO
Model: Person Vehicle Bike Detection FP16 - Device: CPU
TensorFlow Lite
Model: Inception V4
TensorFlow Lite
Model: Inception ResNet V2
TensorFlow Lite
Model: NASNet Mobile
TensorFlow Lite
Model: Mobilenet Float
TensorFlow Lite
Model: SqueezeNet
TensorFlow Lite
Model: Mobilenet Quant
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
OpenVINO
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16-INT8 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16 - Device: CPU
OpenVINO
Model: Weld Porosity Detection FP16 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16 - Device: CPU
OpenVINO
Model: Vehicle Detection FP16 - Device: CPU
GPAW
Input: Carbon Nanotube
Himeno Benchmark
Poisson Pressure Solver
OpenRadioss
Model: Rubber O-Ring Seal Installation
Numenta Anomaly Benchmark
Detector: Earthgecko Skyline
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
Caffe
Model: AlexNet - Acceleration: CPU - Iterations: 200
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
Scikit-Learn
Benchmark: 20 Newsgroups / Logistic Regression
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream
Intel MPI Benchmarks
Test: IMB-MPI1 PingPong
miniBUDE
Implementation: OpenMP - Input Deck: BM2
miniBUDE
Implementation: OpenMP - Input Deck: BM2
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream
ASKAP
Test: tConvolve MPI - Gridding
ASKAP
Test: tConvolve MPI - Degridding
TensorFlow
Device: CPU - Batch Size: 32 - Model: GoogLeNet
Laghos
Test: Triple Point Problem
TensorFlow
Device: CPU - Batch Size: 64 - Model: AlexNet
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
ASKAP
Test: tConvolve OpenMP - Degridding
ASKAP
Test: tConvolve OpenMP - Gridding
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: double - X Y Z: 512
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 512
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
spaCy
Model: en_core_web_trf
spaCy
Model: en_core_web_lg
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
Rodinia
Test: OpenMP LavaMD
Algebraic Multi-Grid Benchmark
Intel MPI Benchmarks
Test: IMB-P2P PingPong
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: float - X Y Z: 512
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 512
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: double - X Y Z: 512
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 512
Mlpack Benchmark
Benchmark: scikit_ica
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: float - X Y Z: 512
Numenta Anomaly Benchmark
Detector: Contextual Anomaly Detector OSE
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 512
GROMACS
Implementation: MPI CPU - Input: water_GMX50_bare
OpenFOAM
Input: motorBike - Execution Time
OpenFOAM
Input: motorBike - Mesh Time
CP2K Molecular Dynamics
Input: Fayalite-FIST
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of State
Rodinia
Test: OpenMP Leukocyte
NAMD
ATPase Simulation - 327,506 Atoms
NAS Parallel Benchmarks
Test / Class: SP.C
OpenRadioss
Model: Cell Phone Drop Test
Caffe
Model: AlexNet - Acceleration: CPU - Iterations: 100
Numenta Anomaly Benchmark
Detector: Bayesian Changepoint
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
Nebular Empirical Analysis Tool
SPECFEM3D
Model: Layered Halfspace
Kripke
TensorFlow
Device: CPU - Batch Size: 16 - Model: GoogLeNet
QMCPACK
Input: simple-H2O
SPECFEM3D
Model: Water-layered Halfspace
TensorFlow
Device: CPU - Batch Size: 32 - Model: AlexNet
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral Mixing
NAS Parallel Benchmarks
Test / Class: BT.C
FFTW
Build: Float + SSE - Size: 1D FFT Size 4096
Xcompact3d Incompact3d
Input: input.i3d 193 Cells Per Direction
Mlpack Benchmark
Benchmark: scikit_svm
OpenFOAM
Input: drivaerFastback, Small Mesh Size - Execution Time
OpenFOAM
Input: drivaerFastback, Small Mesh Size - Mesh Time
LULESH
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of State
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixing
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
Darmstadt Automotive Parallel Heterogeneous Suite
Backend: OpenMP - Kernel: NDT Mapping
Parboil
Test: OpenMP Stencil
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: float - X Y Z: 512
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 512
R Benchmark
libxsmm
M N K: 32
Parboil
Test: OpenMP LBM
libxsmm
M N K: 64
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
RNNoise
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: float - X Y Z: 512
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 512
TNN
Target: CPU - Model: SqueezeNet v1.1
TNN
Target: CPU - Model: MobileNet v2
miniFE
Problem Size: Small
Darmstadt Automotive Parallel Heterogeneous Suite
Backend: OpenMP - Kernel: Euclidean Cluster
Dolfyn
Computational Fluid Dynamics
TensorFlow
Device: CPU - Batch Size: 16 - Model: AlexNet
NAS Parallel Benchmarks
Test / Class: LU.C
ArrayFire
Test: BLAS CPU
NAS Parallel Benchmarks
Test / Class: IS.D
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixing
SPECFEM3D
Model: Homogeneous Halfspace
ASKAP
Test: Hogbom Clean OpenMP
Remhos
Test: Sample Remap Example
ACES DGEMM
Sustained Floating-Point Rate
SPECFEM3D
Model: Tomographic Model
SPECFEM3D
Model: Mount St. Helens
Numenta Anomaly Benchmark
Detector: Relative Entropy
GNU Octave Benchmark
NAS Parallel Benchmarks
Test / Class: EP.C
oneDNN
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
Timed MAFFT Alignment
Multiple Sequence Alignment - LSU RNA
CP2K Molecular Dynamics
Input: H20-64
CloverLeaf
Lagrangian-Eulerian Hydrodynamics
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 256
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256
Pennant
Test: sedovbig
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: double - X Y Z: 256
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 256
NAS Parallel Benchmarks
Test / Class: FT.C
miniBUDE
Implementation: OpenMP - Input Deck: BM1
miniBUDE
Implementation: OpenMP - Input Deck: BM1
NAS Parallel Benchmarks
Test / Class: CG.C
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixing
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of State
Rodinia
Test: OpenMP CFD Solver
Xcompact3d Incompact3d
Input: input.i3d 129 Cells Per Direction
FFTW
Build: Stock - Size: 1D FFT Size 4096
Rodinia
Test: OpenMP Streamcluster
FFTW
Build: Float + SSE - Size: 2D FFT Size 32
Pennant
Test: leblancbig
NAS Parallel Benchmarks
Test / Class: SP.B
TNN
Target: CPU - Model: SqueezeNet v2
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 256
Numenta Anomaly Benchmark
Detector: Windowed Gaussian
FFTW
Build: Stock - Size: 1D FFT Size 32
FFTW
Build: Stock - Size: 2D FFT Size 32
NAS Parallel Benchmarks
Test / Class: MG.C
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 256
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: double - X Y Z: 256
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 256
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256
FFTE
Test: N=256, 1D Complex FFT Routine
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 256
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: float - X Y Z: 256
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
PyHPC Benchmarks
Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of State
FFTW
Build: Float + SSE - Size: 1D FFT Size 32
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 256
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 256
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: float - X Y Z: 256
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: double - X Y Z: 128
LAMMPS Molecular Dynamics Simulator
Model: Rhodopsin Protein
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 128
Parboil
Test: OpenMP CUTCP
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: Stock - Precision: float - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: double - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 128
HeFFTe - Highly Efficient FFT for Exascale
Test: r2c - Backend: Stock - Precision: float - X Y Z: 128
HPC Challenge
Test / Class: Max Ping Pong Bandwidth
HPC Challenge
Test / Class: Random Ring Bandwidth
HPC Challenge
Test / Class: Random Ring Latency
HPC Challenge
Test / Class: G-Random Access
HPC Challenge
Test / Class: EP-STREAM Triad
HPC Challenge
Test / Class: G-Ptrans
HPC Challenge
Test / Class: EP-DGEMM
HPC Challenge
Test / Class: G-Ffte
Phoronix Test Suite v10.8.4