Memory-constrained data locality optimization for tensor contractions

Alina Bibireata, Sandhya Krishnan, Gerald Baumgartner, Daniel Cociorva, Chi Chung Lam, P. Sadayappan, J. Ramanujam, David E. Bernholdt, Venkatesh Choppella

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

7 Scopus citations

Abstract

The accurate modeling of the electronic structure of atoms and molecules involves computationally intensive tensor contractions over large multi-dimensional arrays. Efficient computation of these contractions usually requires the generation of temporary intermediate arrays. These intermediates could be extremely large, requiring their storage on disk. However, the intermediates can often be generated and used in batches through appropriate loop fusion transformations. To optimize the performance of such computations a combination of loop fusion and loop tiling is required, so that the cost of disk I/O is minimized. In this paper, we address the memory-constrained data-locality optimization problem in the context of this class of computations. We develop an optimization framework to search among a space of fusion and tiling choices to minimize the data movement overhead. The effectiveness of the developed optimization approach is demonstrated on a computation representative of a component used in quantum chemistry suites.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsLawrence Rauchwerger
PublisherSpringer Verlag
Pages93-108
Number of pages16
ISBN (Print)9783540246442
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2958
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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