Name
Abstract | ||
|---|---|---|
Manually tuning MPI runtime parameters is a practice commonly employed to optimise MPI application performance on a specific architecture. However, the best setting for these parameters not only depends on the underlying system but also on the application itself and its input data. This paper introduces a novel approach based on machine learning techniques to estimate the values of MPI runtime parameters that tries to achieve optimal speedup for a target architecture and any unseen input program. The effectiveness of our optimization tool is evaluated against two benchmarks executed on a multi-core SMP machine. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-03770-2_26 | PVM/MPI |
Keywords | Field | DocType |
mpi runtime parameter,novel approach,input data,unseen input program,mpi application performance,specific architecture,machine learning,optimal speedup,target architecture,optimizing mpi runtime parameter,multi-core smp machine,best setting,multi core,optimization,mpi | Architecture,Computer science,Artificial intelligence,Multi-core processor,Machine learning,Speedup | Conference |
Volume | ISSN | Citations |
5759 | 0302-9743 | 14 |
PageRank | References | Authors |
0.92 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Pellegrini | 1 | 72 | 6.09 |
| Jie Wang | 2 | 14 | 0.92 |
| Thomas Fahringer | 3 | 2847 | 254.09 |
| Hans Moritsch | 4 | 50 | 6.78 |