Abstract
Highly scalable parallel computers, e.g. SCI-coupled workstation clusters, are NUMA architectures. Thus good static locality is essential for high performance and scalability of parallel programs on these machines. This paper describes novel techniques to optimize static locality at compilation time by application of data transformations and data distributions. The metric which guides the optimizations employs Ehrhart polynomials and allows to calculate the amount of static locality precisely. The effectiveness of our novel techniques has been confirmed by experiments conducted on the SCI-coupled workstation cluster of the PC2 at the University of Paderborn.
Cite
CITATION STYLE
Heine, F., & Slowik, A. (2000). Volume driven data distribution for NUMA-machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1900, pp. 415–424). Springer Verlag. https://doi.org/10.1007/3-540-44520-x_53
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