North Carolina State University
Current implementations of MPI are unaware of accelerator memory (i.e., GPU device memory) and require programmers to explicitly move data between memory spaces. This approach is inefficient, especially for intranode communication where it can result in several extra copy operations. In this paper, the authors integrate GPU-awareness into a popular MPI runtime system and develop techniques to significantly reduce the cost of intranode communication involving one or more GPUs. Experiment results show an up to 2x increase in bandwidth, resulting in an average of 4.3% improvement to the total execution time of a halo exchange benchmark.