On the efficacy of GPU-integrated MPI for scientific applications
Ashwin M. Aji, Lokendra S. Panwar, Feng Ji, Milind Chabbi, Karthik Srinivasa Murthy, Pavan Balaji, Keith Bisset, James Dinan, Wu-chun Feng, John Mellor‐Crummey, Xiaosong Ma, Rajeev Thakur · 2013
Scientific computing applications are quickly adapting to leverage the massive parallelism of GPUs in large-scale clusters. However, the current hybrid programming models require application developers to explicitly manage the disjointed host and GPU memories, thus reducing both efficiency and productivity. Consequently, GPU-integrated MPI solutions, such as MPI-ACC and MVAPICH2-GPU, have been developed that provide unified programming interfaces and optimized implementations for end-to-end data communication among CPUs and GPUs. To date, however, there lacks an in-depth performance characterization of the new optimization spaces or the productivity impact of such GPU-integrated communication systems for scientific applications.