A Framework for Automatic Adaptation of Tunable Distributed Applications
Cluster Computing
Characterization and enhancement of Static Mapping Heuristics for Heterogeneous Systems
HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
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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.