Name
Abstract | ||
|---|---|---|
This paper presents a system deployed on parallel clusters to manage a collection of parallel simulations that make up a computational study. It explores how such a system can extend traditional parallel job scheduling and resource allocation techniques to incorporate knowledge specific to the study. Using a UINTAH-based helium gas simulation code (ARCHES) and the SimX system for multi-experiment computational studies, this paper demonstrates that, by using application-specific knowledge in resource allocation and scheduling decisions, one can reduce the run time of a computational study from over 20 hours to under 4.5 hours on a 32-processor cluster, and from almost 11 hours to just over 3.5 hours on a 64-processor cluster. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1504176.1504184 | Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming |
Keywords | Field | DocType |
resource allocation,parallel systems | Cluster (physics),Parallel simulation,Workload,Scheduling (computing),Computer science,Parallel algorithm,High-throughput computing,Parallel computing,Resource allocation,Job scheduler,Distributed computing | Conference |
Volume | Issue | Citations |
44 | 4 | 2 |
PageRank | References | Authors |
0.38 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siu Yau | 1 | 8 | 0.85 |
| Vijay Karamcheti | 2 | 646 | 67.03 |
| Denis Zorin | 3 | 2248 | 151.26 |
| Kostadin Damevski | 4 | 254 | 26.07 |
| Steven G. Parker | 5 | 1323 | 116.91 |