Talks and Poster Presentations (with Proceedings-Entry):

S. Mirsadeghi, J. Träff, P. Balaji, A. Afsahi:
"Exploiting Common Neighborhoods to Optimize MPI Neighborhood Collectives";
Talk: 24th IEEE International Conference on High Performance Computing (HiPC 2017), Jaipur, India; 2017-12-18 - 2017-12-21; in: "Proceedings of the 24th IEEE International Conference on High Performance Computing (HiPC 2017)", IEEE, (2017), ISBN: 978-1-5386-2294-0; 348 - 357.

English abstract:
Neighborhood collectives were added to the Message Passing Interface (MPI) to better support sparse communication patterns found in many applications. These new collectives encourage more scalable programming styles, and greatly extend the scope of MPI collectives by allowing users to define their own collective communication patterns. In this paper, we describe a new, distributed algorithm for computing improved communication schedules for neighborhood collectives. We show how to discover common process neighborhoods in fully general MPI distributed graph topologies, and how to exploit this information to build message-combining communication schedules for the MPI neighborhood collectives. Our experimental results show considerable performance improvements for application communication topologies of various shapes and sizes. On average, the performance gain is around 50%, but it can also be as much as 71% for topologies with larger numbers of neighbors.

MPI; topology; neighborhood collective

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.