NDLI: Locality Conscious Processor Allocation and Scheduling for Mixed Parallel Applications

Content Provider	IEEE Xplore Digital Library
Author	Vydyanathan, N. Krishnamoorthy, S. Sabin, G. Catalyurek, U. Kurc, T. Sadayappan, P. Saltz, J.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Ohio State Univ., Columbus, OH (Vydyanathan, N.; Krishnamoorthy, S.; Sabin, G.)
Abstract	Complex applications can often be viewed as a collection of coarse-grained data-parallel application components with precedence constraints. It has been shown that combining task and data parallelism (mixed parallelism) can be an effective execution paradigm for these applications. In this paper, we present an algorithm to compute the appropriate mix of task and data parallelism based on the scalability characteristics of the tasks as well as the intertask data communication costs, such that the parallel completion time (makespan) is minimized. The algorithm iteratively reduces the makespan by increasing the degree of data parallelism of tasks on the critical path that have good scalability and a low degree of potential task parallelism. Data communication costs along the critical path are minimized by exploiting parallel transfer mechanisms and use of a locality conscious backfill scheduler. Evaluation using benchmark task graphs derived from real applications as well as synthetic graphs shows that our algorithm consistently performs better than previous scheduling schemes
Starting Page	1
Ending Page	10
File Size	1029604
Page Count	10
File Format	PDF
ISBN	1424403278
ISSN	15525244
DOI	10.1109/CLUSTR.2006.311861
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-25
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Processor scheduling Parallel processing Costs Application software Iterative algorithms Scalability Data communication Scheduling algorithm Concurrent computing Computational efficiency
Content Type	Text
Resource Type	Article

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