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An approach to locality-conscious load balancing and transparent memory hierarchy management with a global-address-space parallel programming model

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4 Author(s)
S. Krishnamoorthy ; Dept. of Comput. Sci. & Eng., Ohio State Univ., USA ; U. Catalyurek ; J. Nieplocha ; P. Sadayappan

The development of efficient parallel out-of-core applications is often tedious, because of the need to explicitly manage the movement of data between files and data structures of the parallel program. Several large-scale applications require multiple passes of processing over data too large to fit in memory, where significant concurrency exists within each pass. This paper describes a global-address-space framework for the convenient specification and efficient execution of parallel out-of-core applications operating on block-sparse data. The programming model provides a global view of block-sparse matrices and a mechanism for the expression of parallel tasks that operate on block-sparse data. The tasks are automatically partitioned into phases that operate on memory-resident data, and mapped onto processors to optimize load balance and data locality. Experimental results are presented that demonstrate the utility of the approach

Published in:

Proceedings 20th IEEE International Parallel & Distributed Processing Symposium

Date of Conference:

25-29 April 2006