Dynamic data distributions in Vienna Fortran
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
An approach to communication-efficient data redistribution
ICS '94 Proceedings of the 8th international conference on Supercomputing
Processor Mapping Techniques Toward Efficient Data Redistribution
IEEE Transactions on Parallel and Distributed Systems
Efficient address generation for block-cyclic distributions
ICS '95 Proceedings of the 9th international conference on Supercomputing
Benchmark Evaluation of the IBM SP2 for Parallel Signal Processing
IEEE Transactions on Parallel and Distributed Systems
Fast runtime block cyclic data redistribution on multiprocessors
Journal of Parallel and Distributed Computing
Scheduling Block-Cyclic Array Redistribution
IEEE Transactions on Parallel and Distributed Systems
A Basic-Cycle Calculation Technique for Efficient Dynamic Data Redistribution
IEEE Transactions on Parallel and Distributed Systems
Algorithmic Redistribution Methods for Block-Cyclic Decompositions
IEEE Transactions on Parallel and Distributed Systems
Efficient Algorithms for Block-Cyclic Array Redistribution Between Processor Sets
IEEE Transactions on Parallel and Distributed Systems
Runtime performance of parallel array assignment: an empirical study
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Efficient Algorithms for Array Redistribution
IEEE Transactions on Parallel and Distributed Systems
Efficient Algorithms for Multi-dimensional Block-Cyclic Redistribution of Arrays
ICPP '97 Proceedings of the international Conference on Parallel Processing
FRONTIERS '95 Proceedings of the Fifth Symposium on the Frontiers of Massively Parallel Computation (Frontiers'95)
Algorithmic redistribution methods for block cyclic decompositions
Algorithmic redistribution methods for block cyclic decompositions
A message passing strategy for array redistributions in a torus network
The Journal of Supercomputing
International Journal of Computer Mathematics
Hi-index | 0.00 |
This paper describes a pipeline technique which is used to redistribute data on a multiprocessor grid during runtime. The main purposes of the algorithm are to minimize the data transfer time, prevent congestion on the ports of the receiving processors, and minimize the number of idle processors. One of the key ideas for this algorithm is the creation of processor classes, firstly introduced by Desprez et al. [IEEE Transactions on Parallel and Distributed Systems 9(2):102 (1998).] Based on the idea of classes, we create the pipeline tasks used to organize the redistribution of data. Our experimental results show that this pipeline technique can significantly reduce the amount of time required to complete a dynamic data transfer task.