Name
Abstract | ||
|---|---|---|
Understanding how parallel applications behave is crucial for using high-performance computing (HPC) resources efficiently. However, the task of performance analysis is becoming increasingly difficult due to the growing complexity of scientific codes and the size of machines. Even though many tools have been developed over the past years to help in this task, current approaches either only offer an overview of the application discarding temporal information, or they generate huge trace files that are often difficult to handle. In this paper we propose the use of event flow graphs for monitoring MPI applications, a new and different approach that balances the low overhead of profiling tools with the abundance of information available from tracers. Event flow graphs are captured with very low overhead, require orders of magnitude less storage than standard trace files, and can still recover the full sequence of events in the application. We test this new approach with the NERSC-8/Trinity Benchmark suite and achieve compression ratios up to 119x. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-09873-9-1 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
MPI event flow graphs,trace compression,trace reconstruction,performance monitoring | Graph,Trace compression,Performance monitoring,Computer science,Flow (psychology),Parallel computing,Distributed computing | Conference |
Volume | ISSN | Citations |
8632 | 0302-9743 | 4 |
PageRank | References | Authors |
0.43 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xavier Aguilar | 1 | 31 | 6.49 |
| Karl Fürlinger | 2 | 157 | 22.73 |
| Erwin Laure | 3 | 369 | 44.71 |