STREAM, for lots of devices written in many programming models

Python extension language using accelerators

CLAW Compiler for Performance Portability

Code examples for CUDA and OpenACC

High Performance Computing Strategies for Boundary Value Problems

RTM

OpenACC for Python

The repository containing everything you need to compete in the IHPCSS 2019 programming challenge.

Simple OpenACC Fortran Examples

Soil particles contact simulation

jacobi - a benchmark by solving 2D laplace equation with jacobi iterative method. GPU or Xeon Phi can be used.

Matrix multiplication example performed with OpenMP, OpenACC, BLAS, cuBLABS, and CUDA

OpenMP programming tips for GPU offloading

a test of GPU direct with CUDA/OpenACC.

gpumm - matrix-matrix multiplication by using CUDA, cublas, cublasxt and OpenACC.

Interchangeable backends in C++, OpenMP, CUDA, OpenCL, OpenACC

Mandelbrot set by MPI/OpenMP/OpenACC.

Code repository for paper "Exploring translation of OpenMP to OpenACC 2.5: Lessons Learned"

Object-Oriented Implementation of the Conjugate Gradients Method

can - a simple dense matrix-matrix multiplication benchmark with MPI/OpenMP/OpenACC. MPI version is based on Cannon's algorithm.

the base for a web-based parallel programming environment build over a microservice approach

Materials for "Differences between OpenACC and OpenMP offloading models" tutorial.

A performance study of various parallelisation tools on a few benchmarks

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.

📦 mandelboxes for a course, SFWR ENG 4F03

Just another build script for gcc an nvptx

A mirror to https://github.com/olcf/PETSC-OpenACC -- An example of accelerating PETSc with OpenACC

Rodinia 2.1 benchmark modified to run with OpenACC 2.7 and PGI 18.4

Some Fortran codes to practice programming in Fortran.