Best practices for performance

Offloading data transfers

Minimizing data transfer overhead is paramount in GPU programming to optimize performance, efficiently utilize resources, enhance energy efficiency, and maintain data integrity, among other benefits. It is a crucial aspect for achieving high-performance in GPU-intensive applications.

In general, the process to minimize data transfers using OpenMP offload directives is as follows:

  1. Identify offload regions: Determine which parts of your code are computationally-intensive and can benefit from offloading computations to the GPU (e.g. OpenMP target, teams, distribute and parallel directives).
  2. Specify data mapping: Control the transfer of data between the host and target by indicating the variables transferred from the host to the device (e.g. OpenMP clause to), from the device to the host (e.g. clause from) and those allocated on the device (e.g. clause alloc).
  3. Minimize data transfers through data management, data scoping and data layout: Ensure moving the necessary data only, avoiding transferring data that is not needed within the offload region (e.g. do not transfer read-only variables, transfer only the sub-arrays actually used in a loop, use OpenMP clauses private, firstprivate and shared). Also consider using GPU-friendly data layouts to reduce data transfers and memory access times.

Alternatively, the OpenACC offload directives also enable the implementation of efficient data transfers in GPU programming.

Overall, the Codee tools support GPU programming for C/C++/Fortran using OpenMP and OpenACC. It can automatically insert OpenMP or OpenACC directives in the source code to offload to accelerator devices such as GPUs, helping novice programmers to write GPU code at the expert level. Codee provides a systematic, more predictable approach that leverages parallel programming best practices.