Abstract
We describe the effort to implement the HPCG benchmark using OpenSHMEM and MPI one-sided communication. Unlike the High Performance LINPACK (HPL) benchmark that places emphasis on large dense matrix computations, the HPCG benchmark is dominated by sparse operations such as sparse matrix-vector product, sparse matrix triangular solve, and long vector operations. The MPI one-sided implementation is developed using the one-sided OpenSHMEM implementation. Preliminary results comparing the original MPI, OpenSHMEM, and MPI one-sided implementations on an SGI cluster, Cray XK7 and Cray XC30 are presented. The results suggest the MPI, OpenSHMEM, and MPI one-sided implementations all obtain similar overall performance but the MPI one-sided implementation seems to slightly increase the run time for multigrid preconditioning in HPCG on the Cray XK7 and Cray XC30.
| Original language | English |
|---|---|
| Title of host publication | OpenSHMEM and Related Technologies |
| Subtitle of host publication | Enhancing OpenSHMEM for Hybrid Environments - 3rd Workshop, OpenSHMEM 2016, Revised Selected Papers |
| Editors | Manjunath Gorentla Venkata, Neena Imam, Swaroop Pophale, Tiffany M. Mintz |
| Publisher | Springer Verlag |
| Pages | 193-203 |
| Number of pages | 11 |
| ISBN (Print) | 9783319509945 |
| DOIs | |
| State | Published - 2016 |
| Event | 3rd workshop on OpenSHMEM and Related Technologies, OpenSHMEM 2016 - Baltimore, United States Duration: Aug 2 2016 → Aug 4 2016 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10007 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 3rd workshop on OpenSHMEM and Related Technologies, OpenSHMEM 2016 |
|---|---|
| Country/Territory | United States |
| City | Baltimore |
| Period | 08/2/16 → 08/4/16 |
Funding
Notice: “This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This work was supported by the United States Department of Defense (DoD) and used resources of the Computational Research and Development Programs and the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory.