openacc
There are 8 repositories under openacc topic.
- llama2.cLanguage:C 1380
openhackathons-org / gpubootcamp
This repository consists for gpu bootcamp material for HPC and AI
Language:Jupyter Notebook 495ParRes / Kernels
This is a set of simple programs that can be used to explore the features of a parallel platform.
Language:C 401- Language:Python 330
alpaka-group / alpaka
Abstraction Library for Parallel Kernel Acceleration :llama:
Language:C++ 321- BabelStream
UoB-HPC / BabelStream
STREAM, for lots of devices written in many programming models
Language:C++ 309 - Language:C++ 240
- Language:Fortran 159
- Language:Fortran 119
- Language:C 80
OpenACC / openacc-training-materials
Training materials provided by OpenACC.org.
Language:C 73- Language:Fortran 49
- Language:C 46
claw-project / claw-compiler
CLAW Compiler for Performance Portability
Language:Java 40- Language:Fortran 39
- Language:HTML 37
- Language:C 36
- elphbolt
nakib / elphbolt
A solver for the coupled and decoupled electron and phonon Boltzmann transport equations.
Language:Fortran 36 - Language:Cuda 35
N-Ways to GPU Programming Bootcamp
Language:Jupyter Notebook 34intel / intel-application-migration-tool-for-openacc-to-openmp
OpenACC* to OpenMP* API assisting migration tool
Language:Python 29The sources for the OpenACC Programming and Best Practices Guide.
Language:TeX 28- Language:Python 20
ShadyBoukhary / GPU-research-FFT-OpenACC-CUDA
Case studies constitute a modern interdisciplinary and valuable teaching practice which plays a critical and fundamental role in the development of new skills and the formation of new knowledge. This research studies the behavior and performance of two interdisciplinary and widely adopted scientific kernels, a Fast Fourier Transform and Matrix Multiplication. Both routines are implemented in the two current most popular many-core programming models CUDA and OpenACC. A Fast Fourier Transform (FFT) samples a signal over a period of time and divides it into its frequency components, computing the Discrete Fourier Transform (DFT) of a sequence. Unlike the traditional approach to computing a DFT, FFT algorithms reduce the complexity of the problem from O(n2) to O(nLog2n). Matrix multiplication is a cornerstone routine in Mathematics, Artificial Intelligence and Machine Learning. This research also shows that the nature of the problem plays a crucial role in determining what many-core model will provide the highest benefit in performance.
Language:Cuda 10capellil / IHPCSS_Programming_challenge_2019
The repository containing everything you need to compete in the IHPCSS 2019 programming challenge.
Language:Fortran 9- Language:Fortran 8
- Language:C++ 8
dc-fukuoka / jacobi
jacobi - a benchmark by solving 2D laplace equation with jacobi iterative method. GPU or Xeon Phi can be used.
Language:Fortran 7mnicely / computeWorks_examples
Matrix multiplication example performed with OpenMP, OpenACC, BLAS, cuBLABS, and CUDA
Language:C++ 6PawseySC / sc20-gpu-offloading
Materials for "Differences between OpenACC and OpenMP offloading models" tutorial.
Language:C 6stfc / PSycloneBench
Various benchmarks used to inform PSyclone optimisations
Language:Fortran 6- Language:C 5
olcf-tutorials / openmp_offloading
OpenMP programming tips for GPU offloading
Language:C++ 5Interoperability examples for OpenACC.
Language:C 5- Language:Fortran 5
yasahi-hpc / P3-miniapps
Kinetic plasma simulation code parallelized with C++ parallel algorithm
Language:C++ 4
