OpenACC is a directives-based API for code parallelization with GPUs. In contrast, OpenMP is the API for shared-memory parallel processing with CPUs. For applications, programmers insert OpenACC directives before specific code sections, typically with loops, to engage the GPUs. This approach enables existing — especially legacy — codes to be parallelized without extensive rewriting using Nvidia’s CUDA programming language for GPU. However, while a code parallelized with OpenACC directives may require significantly less effort than its equivalent CUDA counterpart, it may also result in less stellar computational performance. For many large existing codes, rewriting them with CUDA is impractical if not impossible. For those cases, OpenACC offers a pragmatic alternative.

What you need to know or do on the SCC

  1. To use OpenACC, compile your Fortran code with the Portland Group compiler, pgfortran (aka pgf90, pgf95) or pgcc. Here is an example on how to compile you C or Fortran code embedded with OpenACC directives:
    scc1% pgfortran -o mycode -acc -Minfo mycode.f90

    In the above, -acc turns on the OpenACC feature while -Minfo returns additional information on the compilation. For details, see this.

  2. To submit your code (with OpenACC directives) to a SCC node with GPUs:
    scc1% qsub -l gpus=1 -b y mycode

    In the above, 1 GPU (and in the absence of multiprocessor request, 1 CPU) is requested.

    More examples of GPU batch jobs are available here.

Additional Information

  1. The following examples demonstrate a matrix multiply (C = A * B) using either multi-threaded OpenMP or OpenACC on a single GPU. The environment variables _OPENMP and _OPENACC are used to determine if the Fortran 90 program was compiled for OpenMP or OpenACC directives.
    • To enable the #ifdef C preprocessor statements in 1, either name your Fortran code with the .F90 suffix or use the compiler flag -Mpreprocess
    • For OpenMP application:
      scc1% pgfortran -o mm_omp matrix_multiply.f90 -mp -Mpreprocess
    • For OpenACC application (with a single GPU device):

      scc1% pgfortran -o mm_acc matrix_multiply.f90 -acc -Mpreprocess
    • For OpenACC application (with multiple GPU devices):
      Not supported yet on the SCC, awaiting PGI compiler version 13.3 for bug fix.

  2. The following demonstrates timing comparisons for OpenACC, OpenMP, MPI:
    The above figure shows the timings comparison of a matrix multiply using a single GPU (via OpenACC) against two other parallel methods: OpenMP and MPI. The figure below shows the timings of matrix multiply using 1, 2, and 3 GPU devices.

Relevant Links