Exploiting Application Tunability for Efficient, Predictable Parallel Resource Management

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. 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 and/or 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 a..