Processor Mapping Techniques Toward E cient DataRedistribution Edgar

Run-time data redistribution can enhance algorithm performance in distributed-memory machines. Explicit redistribution of data can be performed between algorithm phases when a diierent data decomposition is expected to deliver increased performance for a subsequent phase of computation. Redistribution, however, represents increased program overhead as algorithm computation is discontinued while data are exchanged among processor memories. In this paper, we present a technique that minimizes the amount of data exchange for BLOCK to CYCLIC(c) (or vice-versa) redistributions of arbitrary number of dimensions. Preserving the semantics of the target (destination) distribution pattern, the technique manipulates the data to logical processor mapping of the target pattern. When implemented on an IBM SP-x, the mapping technique demonstrates redistribution performance improvements of approximately 40% over traditional data to processor mapping. Relative to the traditional mapping technique, the proposed method aaords greater exibility in specifying precisely which data elements are redistributed and which elements remain on-processor.