NDLI: Automating wavefront parallelization for sparse matrix computations

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Automating wavefront parallelization for sparse matrix computations

Content Provider	ACM Digital Library
Author	Park, Jongsoo Barik, Rajkishore Strout, Michelle Mills Mohammadi, Mahdi Soltan Hall, Mary Venkat, Anand Rong, Hongbo
Abstract	This paper presents a compiler and runtime framework for parallelizing sparse matrix computations that have loop-carried dependences. Our approach automatically generates a runtime inspector to collect data dependence information and achieves wavefront parallelization of the computation, where iterations within a wavefront execute in parallel, and synchronization is required across wavefronts. A key contribution of this paper involves dependence simplification, which reduces the time and space overhead of the inspector. This is implemented within a polyhedral compiler framework, extended for sparse matrix codes. Results demonstrate the feasibility of using automatically-generated inspectors and executors to optimize ILU factorization and symmetric Gauss-Seidel relaxations, which are part of the Preconditioned Conjugate Gradient (PCG) computation. Our implementation achieves a median speedup of 2.97X on 12 cores over the reference sequential PCG implementation, significantly outperforms PCG parallelized using Intel's Math Kernel Library (MKL), and is within 6% of the median performance of manually-parallelized PCG.
Starting Page	1
Ending Page	12
Page Count	12
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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