Array redistribution is usually required for more efficiently executing a data-parallel program on distributed memory multi-computers. In performing array redistribution using synchronous communication mode, data communications among the processors should be properly arranged to avoid incurring higher data transfer cost. Some efficient communication scheduling methods for the Block-Cyclic redistribution have been proposed. On the other hand, the processor mapping technique can help reduce the data transfer cost of redistribution. To avoid degrading the benefit of data transfer cost reduction, it is needed to construct optimal communication schedules for the redistribution in which the processor mapping technique is applied. In this paper, we present a unified approach to constructing optimal communication schedules for the processor mapping technique applied Block-Cyclic redistribution. The proposed method is founded on the processor mapping technique and can more efficiently construct the required communication schedules than other optimal scheduling methods.
Array redistribution is usually needed for more efficiently executing a dataparallel program on distributed memory multicomputers. To minimize the redistribution data transfer cost, processor mapping techniques were proposed to reduce the amount of redistributed data elements. Theses techniques demand that the beginning data elements on a processor not be redistributed in the redistribution. On the other hand, for satisfying practical computation needs, a programmer may require other data elements to be un-redistributed (localized) in the redistribution. In this paper, we propose a flexible processor mapping technique for the Block-Cyclic redistribution to allow the programmer to localize the required data elements in the redistribution. We also present an efficient redistribution method for the redistribution employing our proposed technique. The data transfer cost reduction and system performance improvement for the redistributions with data localization are analyzed and presented in our experimental results.
Data alignment that facilitates data locality so that the data access communication costs can be minimized, helps distributed memory parallel machines improve their throughput. Most data alignment methods are devised mainly to align the arrays referenced using linear subscripts or quadratic subscripts with few (one or two) loop index variables. In this paper, we propose two communication-free alignment techniques to align the arrays referenced using linear subscripts or quadratic subscripts with multiple loop index variables. The experimental results from our techniques on Vector Loop and TRFD of the Perfect Benchmarks reveal that our techniques can improve the execution times of the subroutines in these benchmarks.
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