Name
Title | ||
|---|---|---|
Exploiting Application Tunability for Efficient, Predictable Parallel Resource Management |
Abstract | ||
|---|---|---|
Parallel computing is becoming increasing central and mainstream, driven both by the widespread availability of commodity SMP and high-performance cluster platforms, as well as the growing use of parallelism in general-purpose applications such as image recognition, virtual reality, and media processing. In addition to performance requirements, the latter computations impose soft real-time constraints, necessitating efficient, predictable parallel resource management. In this paper, we propose a novel approach for increasing parallel system utilization while meeting application soft real-time deadlines. Our approach exploits the application tunability found in several general-purpose computations. Tunability refers to an application's ability to trade off resource requirements over time, while maintaining a desired level of output quality. We first describe language extensions to support tunability in the Calypso system, then characterize the performance benefits of tunability, using a synthetic task system to systematically identify its benefits. Our results show that application tunability is convenient to express and can significantly improve parallel system utilization for computations with predictability requirements. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/IPPS.1999.760560 | IPPS/SPDP |
Keywords | Field | DocType |
calypso system,system utilization,exploiting application tunability,calypso programming system,parallel resource management,predictable parallel resource management,general-purpose application,parallel computing,parallel approach,application performance,parallel system utilization,image processing application,novel approach,synthetic task system,general-purpose computation,meeting application,application deadline,application tunability,availability,parallel computer,resource allocation,predictions,parallel systems,tuning,hardware,real time,concurrent computing,image recognition,resource manager,image processing,computer programming,virtual reality,parallel processing,real time systems,resource management | Resource management,Predictability,Virtual reality,Computer science,Exploit,Resource allocation,Concurrent computing,Computer programming,Computation,Distributed computing | Conference |
ISSN | ISBN | Citations |
1063-7133 | 0-7695-0143-5 | 6 |
PageRank | References | Authors |
0.68 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fangzhe Chang | 1 | 248 | 15.32 |
| Vijay Karamcheti | 2 | 646 | 67.03 |
| Zvi Kedem | 3 | 710 | 369.44 |