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Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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An Input-Adaptive and In-Place Approach to Dense Tensor-Times-Matrix Multiply

SESSION: Tensor Computation


EVENT TAG(S): Algorithms, Scientific Computing, Analytics

TIME: 3:30PM - 4:00PM

SESSION CHAIR(S): David Abramson

AUTHOR(S):Jiajia Li, Casey Battaglino, Ioakeim Perros, Jimeng Sun, Richard Vuduc



This paper describes a novel framework, called InTensLi (“intensely”), for producing fast single-node implementations of dense tensor-times-matrix multiply (Ttm) of arbitrary dimension. Whereas conventional implementations of Ttm rely on explicitly converting the input tensor operand into a matrix—in order to be able to use any available and fast general matrix-matrix multiply (Gemm) implementation— our framework’s strategy is to carry out the Ttm in-place, avoiding this copy. As the resulting implementations expose tuning parameters, this paper also describes a heuristic em- pirical model for selecting an optimal configuration based on the Ttm’s inputs. When compared to widely used Ttm implementations that are available in the Tensor Tool- box and Cyclops Tensor Framework (Ctf), InTensLi’s in-place and input-adaptive Ttm implementations achieve 4X and 13X speedups, showing Gemm-like performance on a variety of input sizes.

Chair/Author Details:

David Abramson (Chair) - University of Queensland|

Jiajia Li - Georgia Institute of Technology

Casey Battaglino - Georgia Institute of Technology

Ioakeim Perros - Georgia Institute of Technology

Jimeng Sun - Georgia Institute of Technology

Richard Vuduc - Georgia Institute of Technology

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society