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Automatic Task-Based Code Generation for High Performance Domain Specific Embedded Language

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Abstract

Providing high level tools for parallel programming while sustaining a high level of performance has been a challenge that techniques like Domain Specific Embedded Languages try to solve. In previous works, we investigated the design of such a DSEL—NT\(^2\)—providing a Matlab -like syntax for parallel numerical computations inside a C++ library. In this paper, we show how NT\(^2\!\) has been redesigned for shared memory systems in an extensible and portable way. The new NT\(^2\!\) design relies on a tiered Parallel Skeleton system built using asynchronous task management and automatic compile-time taskification of user level code. We describe how this system can operate various shared memory runtimes and evaluate the design by using two benchmarks implementing linear algebra algorithms.

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Notes

  1. As defined by Czarnecki et al. [20].

  2. The Parallel Linear Algebra for Multicore Architectures [2, 13] is a software framework that rewrites a major part of LAPACK subroutines to take advantage of multicore architectures. PLASMA implements tile algorithms and uses both tile data layout and dynamic task scheduling to achieve good performance.

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Authors and Affiliations

  1. LRI, INRIA, Université Paris-Sud XI, Orsay, France

    Antoine Tran Tan, Joel Falcou & Daniel Etiemble

  2. CCT, Louisiana State University, Baton Rouge, LA, USA

    Hartmut Kaiser

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  1. Antoine Tran Tan
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  2. Joel Falcou
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  3. Daniel Etiemble
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  4. Hartmut Kaiser
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Correspondence to Antoine Tran Tan.

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Tran Tan, A., Falcou, J., Etiemble, D. et al. Automatic Task-Based Code Generation for High Performance Domain Specific Embedded Language. Int J Parallel Prog 44, 449–465 (2016). https://doi.org/10.1007/s10766-015-0354-9

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