NDLI: An exploration of optimization algorithms for high performance tensor completion

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An exploration of optimization algorithms for high performance tensor completion

Content Provider	ACM Digital Library
Author	Park, Jongsoo Smith, Shaden Karypis, George
Abstract	Tensor completion is a powerful tool used to estimate or recover missing values in multi-way data. It has seen great success in domains such as product recommendation and healthcare. Tensor completion is most often accomplished via low-rank sparse tensor factorization, a computationally expensive non-convex optimization problem which has only recently been studied in the context of parallel computing. In this work, we study three optimization algorithms that have been successfully applied to tensor completion: alternating least squares (ALS), stochastic gradient descent (SGD), and coordinate descent (CCD++). We explore opportunities for parallelism on shared- and distributed-memory systems and address challenges such as memory- and operation-efficiency, load balance, cache locality, and communication. Among our advancements are an SGD algorithm which combines stratification with asynchronous communication, an ALS algorithm rich in level-3 BLAS routines, and a communication-efficient CCD++ algorithm. We evaluate our optimizations on a variety of real datasets using a modern supercomputer and demonstrate speedups through 1024 cores. These improvements effectively reduce time-to-solution from hours to seconds on real-world datasets. We show that after our optimizations, ALS is advantageous on parallel systems of small-to-moderate scale, while both ALS and CCD++ will provide the lowest time-to-solution on large-scale distributed systems.
Starting Page	1
Ending Page	13
Page Count	13
File Format	PDF
ISBN	9781467388153
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-11-13
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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