A parallel program usually incurs operations on multiple processing resources, interleaving computations, I\/Os, and communications, where each task can only be executed on a processor of a matching category. Many parallel systems also embed special-purpose processors like vector units, floating-point co-processors, and various I\/O processors. Presently, there is no provably good scheduling algorithm that ensures efficient use of multiple resources with functional heterogeneity.<\/p>\n\n\n\n
This paper presents K-RAD, an algorithm that adaptively schedules parallel jobs on multiple processing resources without requiring prior information about the jobs, such as their release times and parallelism profiles. Let K denote the number of categories of heterogenous resources and Pmax<\/sub> denote the maximum number of processors among all categories. We show that, for any set of jobs with arbitrary release times, K-RAD is (K +1-1\/Pmax<\/sub>)- competitive with respect to the makespan. This competitive ratio is provably the best possible for any non-clairvoyant deterministic algorithms for K-resource scheduling. We also show that K-RAD is (4K + 1 – 4K\/(|J | + 1))- competitive with respect to the mean response time for any batched job set J . For the special case of K = 1, i.e., scheduling on homogeneous resources, the best existing mean response time bound for online non-clairvoyant algorithm is 2 + \u221a 3 \u2245 3.73 proved by Edmonds et al. in STOC’97. We show that K-RAD is 3-competitive with respect to the mean response time when K = 1, which offers the best competitive ratio to date.<\/p>\n","protected":false},"excerpt":{"rendered":" A parallel program usually incurs operations on multiple processing resources, interleaving computations, I\/Os, and communications, where each task can only be executed on a processor of a matching category. Many parallel systems also embed special-purpose processors like vector units, floating-point co-processors, and various I\/O processors. 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