Automatically tuned collective communications
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Network performance-aware collective communication for clustered wide-area systems
Parallel Computing - Clusters and computational grids for scientific computing
Building a high-performance collective communication library
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Collective Communication on Dedicated Clusters of Workstations
Proceedings of the 6th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
The Analysis and Optimization of Collective Communications on a Beowulf Cluster
ICPADS '02 Proceedings of the 9th International Conference on Parallel and Distributed Systems
Send-receive considered harmful: Myths and realities of message passing
ACM Transactions on Programming Languages and Systems (TOPLAS)
A Reconfigurable MPI Broadcast Function
HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
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Message Passing Interface (MPI) Collective Communication Functions (MCCF) are usually implemented in programming libraries utilizing invariable algorithms. Not always do such algorithms yield the best performance with all kinds of applications and over all execution environments. In this paper, we present, simulate, analytically model, verify and analyze reconfigurable MCCF that present variable structures and behaviors, in order to provide optimized configurations, flexibility and performance. In this paper we propose and present a set of optimized reconfigurable MCCF, which add flexibility and high performance to collective communications. We simulate, analytically model, verify and analyze the proposed functions, and compare them with invariable implementations. Our results show that reconfiguration at the algorithm level really yields flexibility and performance gains in MCCF.