SIPMaP: A Tool for Modeling Irregular Parallel Computations in the Super Instruction Architecture

Jindal, Nakul; Lotrich, Victor F.; Deumens, Erik; Sanders, Beverly A.

doi:10.1109/ipdps.2013.35

Cited by 2 publications

(1 citation statement)

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“…The provision of a DSL enhances programmer productivity and provides a programming language infrastructure (type checking, AST generation etc.) that has been used to develop SiPMap [6], a tool that can automatically generate performance models from SIAL source code. The success of the enhancements to utilize accelerators provides further evidence supporting the benefits of the ideas underlying the SIA.…”

Section: Discussionmentioning

confidence: 99%

Exploiting GPUs with the Super Instruction Architecture

Jindal

Lotrich

Deumens

et al. 2014

Int J Parallel Prog

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The Super Instruction Architecture (SIA) is a parallel programming environment designed for problems in computational chemistry involving complicated expressions defined in terms of tensors. Tensors are represented by multidimensional arrays which are typically very large. The SIA consists of a domain specific programming language, Super Instruction Assembly Language (SIAL), and its runtime system, Super Instruction Processor. An important feature of SIAL is that algorithms are expressed in terms of blocks (or tiles) of multidimensional arrays rather than individual floating point numbers. In this paper, we describe how the SIA was enhanced to exploit GPUs, obtaining speedups ranging from two to nearly four for computational chemistry calculations, thus saving hours of elapsed time on large-scale computations. The results provide evidence that the "programming-with-blocks" approach embodied in the SIA will remain successful in modern, heterogeneous computing environments.

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Section: Discussionmentioning

confidence: 99%