Exploiting Application Tunability for Efficient, Predictable Parallel Resource Management
NEW YORK UNIV NY COURANT INST OF MATHEMATICAL SCIENCES
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Parallel computing is becoming increasingly 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. Unfortunately, traditional resource management approaches in both parallel and real-time systems are inadequate for meeting this objective the parallel approaches focus primarily on improving application performance andor system utilization at the cost of arbitrarily delaying a given application. while the real-time approaches are overly conservative sacrificing system utilization in order to meet application deadlines. 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 applications ability to trade off resource requirements over time, while maintaining a desired level of output quality. In other words, a large allocation of resources in one stage of the computations lifetime may compensate. in a parameterizable manner, for a smaller allocation in another stage. We first describe language extensions to support tunability in the Calypso programming system, a component of the MILAN metacomputing project, and evaluate their expressiveness using an image processing application. We then characterize the performance benefits of tunability, using a synthetic task system to systematically identify its benefits and shortcomings.
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