“…The vol function returns the number of quadruples (l, m, x, y) = (l, m, x, 0) that satisfy Equation (1). Let us also define a step function S(k) as…”
Section: Combining Data Elements From Superclassesmentioning
confidence: 99%
“…Another interesting technique for message generation was the essential cycle calculation (ECC) method [1]. In the ECC method, a processor first computes the source/destination processor/data sets of array elements in the first essential cycle of the local array it owns.…”
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.
“…The vol function returns the number of quadruples (l, m, x, y) = (l, m, x, 0) that satisfy Equation (1). Let us also define a step function S(k) as…”
Section: Combining Data Elements From Superclassesmentioning
confidence: 99%
“…Another interesting technique for message generation was the essential cycle calculation (ECC) method [1]. In the ECC method, a processor first computes the source/destination processor/data sets of array elements in the first essential cycle of the local array it owns.…”
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.
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