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Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 16-21, 2014

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A Communication-Optimal Framework for Contracting Distributed Tensors

SESSION: Parallel Algorithms

EVENT TYPE: Papers, Best Paper Finalists

TIME: 4:00PM - 4:30PM

SESSION CHAIR: Judith C. Hill

AUTHOR(S):Samyam Rajbhandari, Akshay Nikam, Pai-Wei Lai, Kevin Stock, Sriram Krishnamoorthy, P. Sadayappan

ROOM:391-92

ABSTRACT:

Tensor contractions are extremely compute intensive generalized matrix multiplication operations encountered in many computational science fields, such as quantum chemistry and nuclear physics. Unlike distributed matrix multiplication, which has been extensively studied, limited work has been done in understanding distributed tensor contractions. In this paper, we characterize distributed tensor contraction algorithms on torus networks. We develop a framework with three fundamental communication operators to generate communication-efficient contraction algorithms for arbitrary tensor contractions. We show that for a given amount of memory per processor, our framework is communication optimal for all tensor contractions. We demonstrate performance and scalability of our framework on up to 262,144 cores of BG/Q supercomputer using five tensor contraction examples.

Chair/Author Details:

Judith C. Hill (Chair) - Oak Ridge National Laboratory

Samyam Rajbhandari - Ohio State University

Akshay Nikam - Ohio State University

Pai-Wei Lai - Ohio State University

Kevin Stock - Ohio State University

Sriram Krishnamoorthy - Pacific Northwest National Laboratory

P. Sadayappan - Ohio State University

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society