https://github.com/jeng1220/openacc_fortran_examples

Simple OpenACC Fortran Examples
https://github.com/jeng1220/openacc_fortran_examples

cuda fortran openacc

Last synced: 3 months ago
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Simple OpenACC Fortran Examples

• Host: GitHub
• URL: https://github.com/jeng1220/openacc_fortran_examples
• Owner: jeng1220
• License: apache-2.0
• Created: 2020-02-20T08:06:51.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2021-08-01T07:03:50.000Z (over 3 years ago)
• Last Synced: 2024-10-14T20:47:46.775Z (3 months ago)
• Topics: cuda, fortran, openacc
• Language: Fortran
• Homepage:
• Size: 119 KB
• Stars: 52
• Watchers: 6
• Forks: 10
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Simple OpenACC Fortran Examples #

Author: Jeng Bai-Cheng([email protected])

An example code is worth a thousand words. This repository intends to host fundamental, but useful examples. Each example is just a few dozen lines of code. Most of them come from my past experience in HPC projects, but readers do not need to have the HPC background to understand the examples.

## Eexamples ##

### Basic ###

* [acc_async](/acc_async) - faster way to enqueue GPU routines(kernels)

* [access_efficiency](/access_efficiency) - faster way to access a GPU arrary

* [alternative_nested_parallelism](/alternative_nested_parallelism) - alternative to nested parallelism on the GPU\

* [array_of_pointers](/array_of_pointers) - array of pointers usage

* [array_setting](/array_setting) - faster way to initialize a GPU array

* [atomic_op](/atomic_op) - use atomic operation to maximize parallelism

* [auto_depend](/auto_depend) - automatic dependency solution

* [cuda_c_binding](/cuda_c_binding) - call CUDA C from Fortran

* [cuda_graph](/cuda_graph) - faster way to launch GPU kernels

* [device_routine](/device_routine) - usage of GPU routine. Call other routines in the GPU kernel

* [device_variable](/device_variable) - usage of GPU variable. Access a global variable from other modules in the GPU kernel

* [do_concurrent](/do_concurrent) - DO CONCURRENT construct (Fortran 2008)

* [hybrid_omp_acc](/hybrid_omp_acc) - usage of OpenMP and OpenACC

### MPI ###

* [cuda_mpi_sendrecv](/cuda_mpi_sendrecv) - CUDA-Aware MPI, faster way to use MPI on GPU

* [cuda_unified_memory_mpi_bcast](/cuda_unified_memory_mpi_bcast) - usage of CUDA Unified Memory and MPI, more convenient way to use MPI on GPU

* [nccl_alltoall](/nccl_alltoall) - faster Alltoall on GPU

* [nccl_alltoallv](/nccl_alltoallv) - faster Alltoallv on GPU

### Profiling ###

* [auto_nvtx](/auto_nvtx) - use compiler to insert CPU profiling routines automatically

* [profiling_range](/profiling_range) - demonstration of focused profiling via profiling tool

## Requirement ##

* NVIDIA HPC SDK 21.3

To install HPC SDK via Docker, visit NVIDIA GPU Cloud: https://ngc.nvidia.com/catalog/containers/nvidia:nvhpc/tags

Or download HPC SDK from official website: https://developer.nvidia.com/hpc-sdk

## Bulid ##

```sh

$ cd 

$ make

```

## Run ##

```sh

$ cd 

$ ./

```