A message combining approach for efficient array redistribution in non-all-to-all communication networks

Abstract

The Array redistribution problem is the heart of a number of applications in parallel computing. This paper presents a message combining approach for scheduling runtime array redistribution of one-dimensional arrays. The important contribution of the proposed scheme is that it eliminates the need for local data reorganization, as noted by Sundar in 2001; the blocks destined for each processor are combined in a series of messages exchanged between neighbouring nodes, so that the receiving processors do not need to reorganize the incoming data blocks before storing them to memory locations. Local data reorganization is of great importance, especially in networks where there is no direct communication between all nodes (like tori, meshes, and trees). Thus, a block must travel through a number of relays before reaching the target processor. This requires a higher number of messages generated, therefore, a higher number of data permutations within the memory of each target processor should be made to assure correct data order. The strategy is based on a relation between groups of communicating processor pairs called superclasses.

Bridging theory and application: An in-depth study of the greatest common divisor and least common multiple in mathematics and real-world settings

“…It is possible to provide simultaneous data transmission without interference by sending data at synchronized intervals set by the LCM. This leads to dependable and effective communication [7].…”