HPC - High Performance Computing HPC - High Performance Computing

A collection of common HPC (High Performance Computing) benchmarks.

See how your system performs with this suite using the Phoronix Test Suite. It's as easy as running the phoronix-test-suite benchmark hpc command..

Tests In This Suite

  • ACES DGEMM

  • AI Benchmark Alpha

  • Algebraic Multi-Grid Benchmark

  • ArrayFire

  •         Test: BLAS CPU
  • ASKAP

  •         Test: tConvolve OpenCL
  •         Test: tConvolve CUDA
  •         Test: tConvolve MPI
  •         Test: tConvolve OpenMP
  •         Test: tConvolve MT
  •         Test: Hogbom Clean OpenMP
  • Caffe

  •         Model: AlexNet - Acceleration: CPU - Iterations: 100
  •         Model: AlexNet - Acceleration: CPU - Iterations: 200
  •         Model: AlexNet - Acceleration: CPU - Iterations: 1000
  •         Model: GoogleNet - Acceleration: CPU - Iterations: 100
  •         Model: GoogleNet - Acceleration: CPU - Iterations: 200
  •         Model: GoogleNet - Acceleration: CPU - Iterations: 1000
  • CloverLeaf

  •         Input: clover_bm
  •         Input: clover_bm64_short
  •         Input: clover_bm16
  • CP2K Molecular Dynamics

  •         Input: Fayalite-FIST
  •         Input: H20-64
  •         Input: H2O-DFT-LS
  • Darmstadt Automotive Parallel Heterogeneous Suite

  •         Backend: OpenMP - Kernel: Euclidean Cluster
  •         Backend: OpenMP - Kernel: NDT Mapping
  •         Backend: OpenMP - Kernel: Points2Image
  •         Backend: OpenCL - Kernel: Euclidean Cluster
  •         Backend: OpenCL - Kernel: NDT Mapping
  •         Backend: OpenCL - Kernel: Points2Image
  •         Backend: NVIDIA CUDA - Kernel: Euclidean Cluster
  •         Backend: NVIDIA CUDA - Kernel: NDT Mapping
  •         Backend: NVIDIA CUDA - Kernel: Points2Image
  • DeepSpeech

  •         Acceleration: CPU
  • Dolfyn

  • easyWave

  •         Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240
  •         Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200
  •         Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400
  • ECP-CANDLE

  •         Benchmark: P1B2
  •         Benchmark: P3B1
  •         Benchmark: P3B2
  • Faiss

  •         Test: demo_sift1M
  •         Test: bench_polysemous_sift1m
  • FFTE

  •         Test: N=256, 1D Complex FFT Routine
  • FFTW

  •         Build: Stock - Size: 1D FFT Size 32
  •         Build: Stock - Size: 1D FFT Size 4096
  •         Build: Stock - Size: 2D FFT Size 32
  •         Build: Stock - Size: 2D FFT Size 4096
  •         Build: Float + SSE - Size: 1D FFT Size 32
  •         Build: Float + SSE - Size: 1D FFT Size 4096
  •         Build: Float + SSE - Size: 2D FFT Size 32
  •         Build: Float + SSE - Size: 2D FFT Size 4096
  • GNU Octave Benchmark

  • GPAW

  •         Input: Carbon Nanotube
  • Graph500

  •         Scale: 26
  •         Scale: 29
  • GROMACS

  •         Implementation: MPI CPU - Input: water_GMX50_bare
  •         Implementation: NVIDIA CUDA GPU - Input: water_GMX50_bare
  • HeFFTe - Highly Efficient FFT for Exascale

  •         Test: r2c - Backend: Stock - Precision: float - X Y Z: 128
  •         Test: r2c - Backend: Stock - Precision: float - X Y Z: 256
  •         Test: r2c - Backend: Stock - Precision: float - X Y Z: 512
  •         Test: r2c - Backend: Stock - Precision: float - X Y Z: 1024
  •         Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 128
  •         Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 256
  •         Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 512
  •         Test: r2c - Backend: Stock - Precision: float-long - X Y Z: 1024
  •         Test: r2c - Backend: Stock - Precision: double - X Y Z: 128
  •         Test: r2c - Backend: Stock - Precision: double - X Y Z: 256
  •         Test: r2c - Backend: Stock - Precision: double - X Y Z: 512
  •         Test: r2c - Backend: Stock - Precision: double - X Y Z: 1024
  •         Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 128
  •         Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 256
  •         Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 512
  •         Test: r2c - Backend: Stock - Precision: double-long - X Y Z: 1024
  •         Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128
  •         Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256
  •         Test: r2c - Backend: FFTW - Precision: float - X Y Z: 512
  •         Test: r2c - Backend: FFTW - Precision: float - X Y Z: 1024
  •         Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 128
  •         Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 256
  •         Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 512
  •         Test: r2c - Backend: FFTW - Precision: float-long - X Y Z: 1024
  •         Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128
  •         Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256
  •         Test: r2c - Backend: FFTW - Precision: double - X Y Z: 512
  •         Test: r2c - Backend: FFTW - Precision: double - X Y Z: 1024
  •         Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 128
  •         Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 256
  •         Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 512
  •         Test: r2c - Backend: FFTW - Precision: double-long - X Y Z: 1024
  •         Test: c2c - Backend: Stock - Precision: float - X Y Z: 128
  •         Test: c2c - Backend: Stock - Precision: float - X Y Z: 256
  •         Test: c2c - Backend: Stock - Precision: float - X Y Z: 512
  •         Test: c2c - Backend: Stock - Precision: float - X Y Z: 1024
  •         Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 128
  •         Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 256
  •         Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 512
  •         Test: c2c - Backend: Stock - Precision: float-long - X Y Z: 1024
  •         Test: c2c - Backend: Stock - Precision: double - X Y Z: 128
  •         Test: c2c - Backend: Stock - Precision: double - X Y Z: 256
  •         Test: c2c - Backend: Stock - Precision: double - X Y Z: 512
  •         Test: c2c - Backend: Stock - Precision: double - X Y Z: 1024
  •         Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 128
  •         Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 256
  •         Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 512
  •         Test: c2c - Backend: Stock - Precision: double-long - X Y Z: 1024
  •         Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128
  •         Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256
  •         Test: c2c - Backend: FFTW - Precision: float - X Y Z: 512
  •         Test: c2c - Backend: FFTW - Precision: float - X Y Z: 1024
  •         Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 128
  •         Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 256
  •         Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 512
  •         Test: c2c - Backend: FFTW - Precision: float-long - X Y Z: 1024
  •         Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128
  •         Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256
  •         Test: c2c - Backend: FFTW - Precision: double - X Y Z: 512
  •         Test: c2c - Backend: FFTW - Precision: double - X Y Z: 1024
  •         Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 128
  •         Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 256
  •         Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 512
  •         Test: c2c - Backend: FFTW - Precision: double-long - X Y Z: 1024
  • High Performance Conjugate Gradient

  •         X Y Z: 104 104 104 - RT: 60
  •         X Y Z: 104 104 104 - RT: 1800
  •         X Y Z: 144 144 144 - RT: 60
  •         X Y Z: 144 144 144 - RT: 1800
  •         X Y Z: 160 160 160 - RT: 60
  •         X Y Z: 160 160 160 - RT: 1800
  •         X Y Z: 192 192 192 - RT: 60
  •         X Y Z: 192 192 192 - RT: 1800
  • Himeno Benchmark

  • HPC Challenge

  •         Test / Class: G-HPL
  •         Test / Class: G-Ptrans
  •         Test / Class: G-Random Access
  •         Test / Class: G-Ffte
  •         Test / Class: EP-STREAM Triad
  •         Test / Class: EP-DGEMM
  •         Test / Class: Random Ring Latency
  •         Test / Class: Random Ring Bandwidth
  •         Test / Class: Max Ping Pong Bandwidth
  • HPL Linpack

  • Intel MPI Benchmarks

  •         Test: IMB-MPI1 PingPong
  •         Test: IMB-MPI1 Sendrecv
  •         Test: IMB-MPI1 Exchange
  •         Test: IMB-P2P PingPong
  • Kripke

  • Laghos

  •         Test: Sedov Blast Wave, ube_922_hex.mesh
  •         Test: Triple Point Problem
  • LAMMPS Molecular Dynamics Simulator

  •         Model: Rhodopsin Protein
  •         Model: 20k Atoms
  • LeelaChessZero

  •         Backend: BLAS
  • libxsmm

  •         M N K: 32
  •         M N K: 64
  •         M N K: 128
  •         M N K: 256
  • Llama.cpp

  •         Model: llama-2-7b.Q4_0.gguf
  •         Model: llama-2-13b.Q4_0.gguf
  •         Model: llama-2-70b-chat.Q5_0.gguf
  • Llamafile

  •         Test: mistral-7b-instruct-v0.2.Q8_0 - Acceleration: CPU
  •         Test: llava-v1.5-7b-q4 - Acceleration: CPU
  •         Test: wizardcoder-python-34b-v1.0.Q6_K - Acceleration: CPU
  • LULESH

  • miniBUDE

  •         Implementation: OpenMP - Input Deck: BM1
  •         Implementation: OpenMP - Input Deck: BM2
  • miniFE

  •         Problem Size: Small
  •         Problem Size: Medium
  •         Problem Size: Large
  • Mlpack Benchmark

  •         Benchmark: scikit_svm
  •         Benchmark: scikit_linearridgeregression
  •         Benchmark: scikit_qda
  •         Benchmark: scikit_ica
  • Mobile Neural Network

  • Monte Carlo Simulations of Ionised Nebulae

  •         Input: Dust 2D tau100.0
  •         Input: Gas HII40
  • NAMD

  •         Input: ATPase with 327,506 Atoms
  •         Input: STMV with 1,066,628 Atoms
  • NAS Parallel Benchmarks

  •         Test / Class: BT.C
  •         Test / Class: EP.C
  •         Test / Class: EP.D
  •         Test / Class: FT.C
  •         Test / Class: LU.C
  •         Test / Class: SP.B
  •         Test / Class: SP.C
  •         Test / Class: IS.D
  •         Test / Class: MG.C
  •         Test / Class: CG.C
  • NCNN

  •         Target: CPU
  •         Target: Vulkan GPU
  • Nebular Empirical Analysis Tool

  • nekRS

  •         Input: TurboPipe Periodic
  •         Input: Kershaw
  • Neural Magic DeepSparse

  •         Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
  •         Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
  •         Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
  •         Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
  •         Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
  •         Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
  •         Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
  •         Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
  •         Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream
  •         Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
  •         Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
  •         Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
  •         Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
  •         Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
  •         Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
  •         Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
  •         Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
  •         Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
  •         Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
  •         Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
  •         Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream
  •         Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
  •         Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
  •         Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
  • Numenta Anomaly Benchmark

  •         Detector: Bayesian Changepoint
  •         Detector: Windowed Gaussian
  •         Detector: Relative Entropy
  •         Detector: Earthgecko Skyline
  •         Detector: KNN CAD
  •         Detector: Contextual Anomaly Detector OSE
  • Numpy Benchmark

  • NWChem

  • oneDNN

  •         Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
  •         Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
  •         Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
  •         Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
  •         Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
  •         Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
  •         Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
  •         Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
  •         Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
  •         Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
  •         Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
  •         Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
  •         Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
  •         Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
  •         Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
  •         Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
  •         Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
  •         Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
  •         Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
  •         Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
  •         Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
  • ONNX Runtime

  •         Model: yolov4 - Device: CPU - Executor: Standard
  •         Model: yolov4 - Device: CPU - Executor: Parallel
  •         Model: fcn-resnet101-11 - Device: CPU - Executor: Standard
  •         Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel
  •         Model: super-resolution-10 - Device: CPU - Executor: Standard
  •         Model: super-resolution-10 - Device: CPU - Executor: Parallel
  •         Model: bertsquad-12 - Device: CPU - Executor: Standard
  •         Model: bertsquad-12 - Device: CPU - Executor: Parallel
  •         Model: GPT-2 - Device: CPU - Executor: Standard
  •         Model: GPT-2 - Device: CPU - Executor: Parallel
  •         Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard
  •         Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel
  •         Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard
  •         Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Parallel
  •         Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard
  •         Model: CaffeNet 12-int8 - Device: CPU - Executor: Parallel
  •         Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard
  •         Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Parallel
  •         Model: T5 Encoder - Device: CPU - Executor: Standard
  •         Model: T5 Encoder - Device: CPU - Executor: Parallel
  • OpenCV

  •         Test: DNN - Deep Neural Network
  • OpenFOAM

  •         Input: motorBike
  •         Input: drivaerFastback, Small Mesh Size
  •         Input: drivaerFastback, Medium Mesh Size
  •         Input: drivaerFastback, Large Mesh Size
  • OpenRadioss

  •         Model: Bird Strike on Windshield
  •         Model: Rubber O-Ring Seal Installation
  •         Model: Cell Phone Drop Test
  •         Model: Bumper Beam
  •         Model: INIVOL and Fluid Structure Interaction Drop Container
  •         Model: Chrysler Neon 1M
  •         Model: Ford Taurus 10M
  • OpenVINO

  •         Model: Face Detection FP16 - Device: CPU
  •         Model: Face Detection FP16-INT8 - Device: CPU
  •         Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
  •         Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
  •         Model: Person Detection FP16 - Device: CPU
  •         Model: Person Detection FP32 - Device: CPU
  •         Model: Weld Porosity Detection FP16-INT8 - Device: CPU
  •         Model: Weld Porosity Detection FP16 - Device: CPU
  •         Model: Vehicle Detection FP16-INT8 - Device: CPU
  •         Model: Vehicle Detection FP16 - Device: CPU
  •         Model: Person Vehicle Bike Detection FP16 - Device: CPU
  •         Model: Machine Translation EN To DE FP16 - Device: CPU
  •         Model: Face Detection Retail FP16 - Device: CPU
  •         Model: Face Detection Retail FP16-INT8 - Device: CPU
  •         Model: Handwritten English Recognition FP16 - Device: CPU
  •         Model: Handwritten English Recognition FP16-INT8 - Device: CPU
  •         Model: Road Segmentation ADAS FP16 - Device: CPU
  •         Model: Road Segmentation ADAS FP16-INT8 - Device: CPU
  • Palabos

  •         Grid Size: 100
  •         Grid Size: 400
  •         Grid Size: 500
  •         Grid Size: 1000
  •         Grid Size: 4000
  • Parboil

  •         Test: OpenMP CUTCP
  •         Test: OpenMP MRI-Q
  •         Test: OpenMP MRI Gridding
  •         Test: OpenMP Stencil
  •         Test: OpenMP LBM
  • Pennant

  •         Test: leblancbig
  •         Test: sedovbig
  • PETSc

  •         Test: Streams
  • PlaidML

  •         FP16: No - Mode: Inference - Network: ResNet 50 - Device: CPU
  •         FP16: No - Mode: Inference - Network: VGG16 - Device: CPU
  • PyHPC Benchmarks

  •         Device: CPU - Backend: Aesara - Project Size: 16384 - Benchmark: Equation of State
  •         Device: CPU - Backend: Aesara - Project Size: 16384 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Aesara - Project Size: 65536 - Benchmark: Equation of State
  •         Device: CPU - Backend: Aesara - Project Size: 65536 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Aesara - Project Size: 262144 - Benchmark: Equation of State
  •         Device: CPU - Backend: Aesara - Project Size: 262144 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Equation of State
  •         Device: CPU - Backend: Aesara - Project Size: 1048576 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Equation of State
  •         Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Equation of State
  •         Device: CPU - Backend: Numpy - Project Size: 16384 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Equation of State
  •         Device: CPU - Backend: Numpy - Project Size: 65536 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Equation of State
  •         Device: CPU - Backend: Numpy - Project Size: 262144 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Equation of State
  •         Device: CPU - Backend: Numpy - Project Size: 1048576 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of State
  •         Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: JAX - Project Size: 16384 - Benchmark: Equation of State
  •         Device: CPU - Backend: JAX - Project Size: 16384 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: JAX - Project Size: 65536 - Benchmark: Equation of State
  •         Device: CPU - Backend: JAX - Project Size: 65536 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: JAX - Project Size: 262144 - Benchmark: Equation of State
  •         Device: CPU - Backend: JAX - Project Size: 262144 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: JAX - Project Size: 1048576 - Benchmark: Equation of State
  •         Device: CPU - Backend: JAX - Project Size: 1048576 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Equation of State
  •         Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Numba - Project Size: 16384 - Benchmark: Equation of State
  •         Device: CPU - Backend: Numba - Project Size: 16384 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Numba - Project Size: 65536 - Benchmark: Equation of State
  •         Device: CPU - Backend: Numba - Project Size: 65536 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Numba - Project Size: 262144 - Benchmark: Equation of State
  •         Device: CPU - Backend: Numba - Project Size: 262144 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Numba - Project Size: 1048576 - Benchmark: Equation of State
  •         Device: CPU - Backend: Numba - Project Size: 1048576 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Equation of State
  •         Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Equation of State
  •         Device: CPU - Backend: PyTorch - Project Size: 16384 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Equation of State
  •         Device: CPU - Backend: PyTorch - Project Size: 65536 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Equation of State
  •         Device: CPU - Backend: PyTorch - Project Size: 262144 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Equation of State
  •         Device: CPU - Backend: PyTorch - Project Size: 1048576 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Equation of State
  •         Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Equation of State
  •         Device: CPU - Backend: TensorFlow - Project Size: 16384 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Equation of State
  •         Device: CPU - Backend: TensorFlow - Project Size: 65536 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Equation of State
  •         Device: CPU - Backend: TensorFlow - Project Size: 262144 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Equation of State
  •         Device: CPU - Backend: TensorFlow - Project Size: 1048576 - Benchmark: Isoneutral Mixing
  •         Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Equation of State
  •         Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Isoneutral Mixing
  • PyTorch

  •         Device: CPU - Batch Size: 1 - Model: ResNet-50
  •         Device: CPU - Batch Size: 1 - Model: ResNet-152
  •         Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l
  •         Device: CPU - Batch Size: 16 - Model: ResNet-50
  •         Device: CPU - Batch Size: 16 - Model: ResNet-152
  •         Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l
  •         Device: CPU - Batch Size: 32 - Model: ResNet-50
  •         Device: CPU - Batch Size: 32 - Model: ResNet-152
  •         Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l
  •         Device: CPU - Batch Size: 64 - Model: ResNet-50
  •         Device: CPU - Batch Size: 64 - Model: ResNet-152
  •         Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l
  •         Device: CPU - Batch Size: 256 - Model: ResNet-50
  •         Device: CPU - Batch Size: 256 - Model: ResNet-152
  •         Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l
  •         Device: CPU - Batch Size: 512 - Model: ResNet-50
  •         Device: CPU - Batch Size: 512 - Model: ResNet-152
  •         Device: CPU - Batch Size: 512 - Model: Efficientnet_v2_l
  •         Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-50
  •         Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: ResNet-152
  •         Device: NVIDIA CUDA GPU - Batch Size: 1 - Model: Efficientnet_v2_l
  •         Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-50
  •         Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: ResNet-152
  •         Device: NVIDIA CUDA GPU - Batch Size: 16 - Model: Efficientnet_v2_l
  •         Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-50
  •         Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: ResNet-152
  •         Device: NVIDIA CUDA GPU - Batch Size: 32 - Model: Efficientnet_v2_l
  •         Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-50
  •         Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: ResNet-152
  •         Device: NVIDIA CUDA GPU - Batch Size: 64 - Model: Efficientnet_v2_l
  •         Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-50
  •         Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: ResNet-152
  •         Device: NVIDIA CUDA GPU - Batch Size: 256 - Model: Efficientnet_v2_l
  •         Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-50
  •         Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: ResNet-152
  •         Device: NVIDIA CUDA GPU - Batch Size: 512 - Model: Efficientnet_v2_l
  • QMCPACK

  •         Input: simple-H2O
  •         Input: Li2_STO_ae
  •         Input: FeCO6_b3lyp_gms
  •         Input: O_ae_pyscf_UHF
  •         Input: LiH_ae_MSD
  •         Input: H4_ae
  • Quantum ESPRESSO

  •         Input: AUSURF112
  • Quicksilver

  •         Input: CORAL2 P1
  •         Input: CORAL2 P2
  •         Input: CTS2
  • R Benchmark

  • RELION

  •         Test: Basic - Device: CPU
  • Remhos

  •         Test: Sample Remap Example
  • RNNoise

  • Rodinia

  •         Test: OpenMP CFD Solver
  •         Test: OpenMP LavaMD
  •         Test: OpenMP Leukocyte
  •         Test: OpenMP Streamcluster
  • Scikit-Learn

  •         Benchmark: 20 Newsgroups / Logistic Regression
  •         Benchmark: Covertype Dataset Benchmark
  •         Benchmark: Feature Expansions
  •         Benchmark: GLM
  •         Benchmark: Glmnet
  •         Benchmark: Hist Gradient Boosting
  •         Benchmark: Hist Gradient Boosting Adult
  •         Benchmark: Hist Gradient Boosting Categorical Only
  •         Benchmark: Hist Gradient Boosting Higgs Boson
  •         Benchmark: Hist Gradient Boosting Threading
  •         Benchmark: Isolation Forest
  •         Benchmark: Isotonic / Perturbed Logarithm
  •         Benchmark: Isotonic / Logistic
  •         Benchmark: Isotonic / Pathological
  •         Benchmark: Kernel PCA Solvers / Time vs. N Components
  •         Benchmark: Kernel PCA Solvers / Time vs. N Samples
  •         Benchmark: Lasso
  •         Benchmark: LocalOutlierFactor
  •         Benchmark: SGDOneClassSVM
  •         Benchmark: Plot Fast KMeans
  •         Benchmark: Plot Hierarchical
  •         Benchmark: Plot Incremental PCA
  •         Benchmark: Plot Lasso Path
  •         Benchmark: Plot Neighbors
  •         Benchmark: Plot Non-Negative Matrix Factorization
  •         Benchmark: Plot OMP vs. LARS
  •         Benchmark: Plot Parallel Pairwise
  •         Benchmark: Plot Polynomial Kernel Approximation
  •         Benchmark: Plot Singular Value Decomposition
  •         Benchmark: Plot Ward
  •         Benchmark: Sparse Random Projections / 100 Iterations
  •         Benchmark: RCV1 Logreg Convergencet
  •         Benchmark: SAGA
  •         Benchmark: Sample Without Replacement
  •         Benchmark: SGD Regression
  •         Benchmark: Sparsify
  •         Benchmark: Text Vectorizers
  •         Benchmark: Tree
  •         Benchmark: MNIST Dataset
  •         Benchmark: TSNE MNIST Dataset
  • SHOC Scalable HeterOgeneous Computing

  •         Target: OpenCL - Benchmark: Bus Speed Download
  •         Target: OpenCL - Benchmark: Bus Speed Readback
  •         Target: OpenCL - Benchmark: Max SP Flops
  •         Target: OpenCL - Benchmark: Texture Read Bandwidth
  •         Target: OpenCL - Benchmark: FFT SP
  •         Target: OpenCL - Benchmark: GEMM SGEMM_N
  •         Target: OpenCL - Benchmark: MD5 Hash
  •         Target: OpenCL - Benchmark: Reduction
  •         Target: OpenCL - Benchmark: Triad
  •         Target: OpenCL - Benchmark: S3D
  • spaCy

  • SPECFEM3D

  •         Model: Layered Halfspace
  •         Model: Water-layered Halfspace
  •         Model: Homogeneous Halfspace
  •         Model: Mount St. Helens
  •         Model: Tomographic Model
  • TensorFlow

  •         Device: CPU - Batch Size: 1 - Model: VGG-16
  •         Device: CPU - Batch Size: 1 - Model: ResNet-50
  •         Device: CPU - Batch Size: 1 - Model: AlexNet
  •         Device: CPU - Batch Size: 1 - Model: GoogLeNet
  •         Device: CPU - Batch Size: 16 - Model: VGG-16
  •         Device: CPU - Batch Size: 16 - Model: ResNet-50
  •         Device: CPU - Batch Size: 16 - Model: AlexNet
  •         Device: CPU - Batch Size: 16 - Model: GoogLeNet
  •         Device: CPU - Batch Size: 32 - Model: VGG-16
  •         Device: CPU - Batch Size: 32 - Model: ResNet-50
  •         Device: CPU - Batch Size: 32 - Model: AlexNet
  •         Device: CPU - Batch Size: 32 - Model: GoogLeNet
  •         Device: CPU - Batch Size: 64 - Model: VGG-16
  •         Device: CPU - Batch Size: 64 - Model: ResNet-50
  •         Device: CPU - Batch Size: 64 - Model: AlexNet
  •         Device: CPU - Batch Size: 64 - Model: GoogLeNet
  •         Device: CPU - Batch Size: 256 - Model: VGG-16
  •         Device: CPU - Batch Size: 256 - Model: ResNet-50
  •         Device: CPU - Batch Size: 256 - Model: AlexNet
  •         Device: CPU - Batch Size: 256 - Model: GoogLeNet
  •         Device: CPU - Batch Size: 512 - Model: VGG-16
  •         Device: CPU - Batch Size: 512 - Model: ResNet-50
  •         Device: CPU - Batch Size: 512 - Model: AlexNet
  •         Device: CPU - Batch Size: 512 - Model: GoogLeNet
  •         Device: GPU - Batch Size: 1 - Model: VGG-16
  •         Device: GPU - Batch Size: 1 - Model: ResNet-50
  •         Device: GPU - Batch Size: 1 - Model: AlexNet
  •         Device: GPU - Batch Size: 1 - Model: GoogLeNet
  •         Device: GPU - Batch Size: 16 - Model: VGG-16
  •         Device: GPU - Batch Size: 16 - Model: ResNet-50
  •         Device: GPU - Batch Size: 16 - Model: AlexNet
  •         Device: GPU - Batch Size: 16 - Model: GoogLeNet
  •         Device: GPU - Batch Size: 32 - Model: VGG-16
  •         Device: GPU - Batch Size: 32 - Model: ResNet-50
  •         Device: GPU - Batch Size: 32 - Model: AlexNet
  •         Device: GPU - Batch Size: 32 - Model: GoogLeNet
  •         Device: GPU - Batch Size: 64 - Model: VGG-16
  •         Device: GPU - Batch Size: 64 - Model: ResNet-50
  •         Device: GPU - Batch Size: 64 - Model: AlexNet
  •         Device: GPU - Batch Size: 64 - Model: GoogLeNet
  •         Device: GPU - Batch Size: 256 - Model: VGG-16
  •         Device: GPU - Batch Size: 256 - Model: ResNet-50
  •         Device: GPU - Batch Size: 256 - Model: AlexNet
  •         Device: GPU - Batch Size: 256 - Model: GoogLeNet
  •         Device: GPU - Batch Size: 512 - Model: VGG-16
  •         Device: GPU - Batch Size: 512 - Model: ResNet-50
  •         Device: GPU - Batch Size: 512 - Model: AlexNet
  •         Device: GPU - Batch Size: 512 - Model: GoogLeNet
  • TensorFlow Lite

  •         Model: Mobilenet Float
  •         Model: Mobilenet Quant
  •         Model: NASNet Mobile
  •         Model: SqueezeNet
  •         Model: Inception ResNet V2
  •         Model: Inception V4
  • Timed HMMer Search

  • Timed MAFFT Alignment

  • Timed MrBayes Analysis

  • TNN

  •         Target: CPU - Model: DenseNet
  •         Target: CPU - Model: MobileNet v2
  •         Target: CPU - Model: SqueezeNet v1.1
  •         Target: CPU - Model: SqueezeNet v2
  • Whisper.cpp

  •         Model: ggml-base.en - Input: 2016 State of the Union
  •         Model: ggml-small.en - Input: 2016 State of the Union
  •         Model: ggml-medium.en - Input: 2016 State of the Union
  • WRF

  •         Input: conus 2.5km
  • Xcompact3d Incompact3d

  •         Input: input.i3d 129 Cells Per Direction
  •         Input: input.i3d 193 Cells Per Direction
  •         Input: X3D-benchmarking input.i3d

Revision History Revision History

pts/hpc-1.1.9     Sun, 06 Aug 2023 17:01:43 GMT
Add libxsmm, heffte, remhos, laghos, palabos, faiss, and petsc tests to HPC suite.

pts/hpc-1.1.8     Sat, 25 Mar 2023 18:15:31 GMT
Add specfem3d to HPC suite.

pts/hpc-1.1.7     Fri, 18 Nov 2022 14:55:03 GMT
Add nekrs and minibude to HPC test suite

pts/hpc-1.1.6     Thu, 13 Oct 2022 16:47:18 GMT
Add OpenRadioss to HPC test suite.

pts/hpc-1.1.5     Fri, 28 Jan 2022 07:50:31 GMT
Add Graph500 to HPC suite.

pts/hpc-1.1.4     Fri, 22 Oct 2021 15:23:06 GMT
Add PyHPC to HPC test suite.

pts/hpc-1.1.3     Tue, 27 Apr 2021 18:01:43 GMT
Add WRF to HPC test suite.

pts/hpc-1.1.2     Tue, 09 Mar 2021 11:24:37 GMT
Add hpl to suite.

pts/hpc-1.1.11     Sat, 27 Jan 2024 19:48:56 GMT
Add QuickSilver to HPC suite.

pts/hpc-1.1.10     Sat, 14 Oct 2023 20:33:55 GMT
Add easywave to test suite

pts/hpc-1.1.1     Mon, 18 Jan 2021 21:03:32 GMT
Add IOR to HPC suite for disk / I/O performance.

pts/hpc-1.1.0     Thu, 14 Jan 2021 13:53:57 GMT
Add new tests.

pts/hpc-1.0.0     Wed, 08 Apr 2020 15:08:51 GMT
Initial commit of HPC high performance computing benchmarks suite for easy access.