C4.5: programs for machine learning
C4.5: programs for machine learning
MagPIe: MPI's collective communication operations for clustered wide area systems
Proceedings of the seventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Pipelining and Overlapping for MPI Collective Operations
LCN '03 Proceedings of the 28th Annual IEEE International Conference on Local Computer Networks
Efficient implementation of reduce-scatter in MPI
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Parallel, distributed and network-based processing
Performance Analysis of MPI Collective Operations
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 15 - Volume 16
Statistical Models for Empirical Search-Based Performance Tuning
International Journal of High Performance Computing Applications
On optimizing collective communication
CLUSTER '04 Proceedings of the 2004 IEEE International Conference on Cluster Computing
MPI collective algorithm selection and quadtree encoding
EuroPVM/MPI'06 Proceedings of the 13th European PVM/MPI User's Group conference on Recent advances in parallel virtual machine and message passing interface
Adaptive approaches for efficient parallel algorithms on cluster-based systems
International Journal of Grid and Utility Computing
Improving performance of adaptive component-based dataflow middleware
Parallel Computing
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Selecting the close-to-optimal collective algorithm based on the parameters of the collective call at run time is an important step for achieving good performance of MPI applications. In this paper, we explore the applicability of C4.5 decision trees to the MPI collective algorithm selection problem. We construct C4.5 decision trees from the measured algorithm performance data and analyze both the decision tree properties and the expected run time performance penalty. In cases we considered, results show that the C4.5 decision trees can be used to generate a reasonably small and very accurate decision function. For example, the broadcast decision tree with only 21 leaves was able to achieve a mean performance penalty of 2.08%. Similarly, combining experimental data for reduce and broadcast and generating a decision function from the combined decision trees resulted in less than 2.5% relative performance penalty. The results indicate that C4.5 decision trees are applicable to this problem and should be more widely used in this domain.