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Language-based vectorization and parallelization using intrinsics, OpenMP, TBB and Cilk Plus.

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Title: Language-based vectorization and parallelization using intrinsics, OpenMP, TBB and Cilk Plus.
Authors: Stpiczyński, Przemysław1 przem@hektor.umcs.lublin.pl
Source: Journal of Supercomputing. Apr2018, Vol. 74 Issue 4, p1461-1472. 12p.
Subjects: Parallel programs (Computer programs), SIMD (Computer architecture), Multicore processors, Recursive programming, Intel computers
Abstract: The aim of this paper is to evaluate OpenMP, TBB and Cilk Plus as basic language-based tools for simple and efficient parallelization of recursively defined computational problems and other problems that need both task and data parallelization techniques. We show how to use these models of parallel programming to transform a source code of Adaptive Simpson’s Integration to programs that can utilize multiple cores of modern processors. Using the example of Belman-Ford algorithm for solving single-source shortest path problems, we advise how to improve performance of data parallel algorithms by tuning data structures for better utilization of vector extensions of modern processors. Manual vectorization techniques based on Cilk array notation and intrinsics are presented. We also show how to simplify such optimization using Intel SIMD Data Layout Template containers. [ABSTRACT FROM AUTHOR]
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Abstract:The aim of this paper is to evaluate OpenMP, TBB and Cilk Plus as basic language-based tools for simple and efficient parallelization of recursively defined computational problems and other problems that need both task and data parallelization techniques. We show how to use these models of parallel programming to transform a source code of <italic>Adaptive Simpson’s Integration</italic> to programs that can utilize multiple cores of modern processors. Using the example of <italic>Belman-Ford algorithm</italic> for solving single-source shortest path problems, we advise how to improve performance of data parallel algorithms by tuning data structures for better utilization of vector extensions of modern processors. Manual vectorization techniques based on Cilk array notation and intrinsics are presented. We also show how to simplify such optimization using Intel SIMD Data Layout Template containers. [ABSTRACT FROM AUTHOR]
ISSN:09208542
DOI:10.1007/s11227-017-2231-3