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Conference object . 2016
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https://doi.org/10.1109/mcsoc....
Article . 2016 . Peer-reviewed
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The ForeC Synchronous Deterministic Parallel Programming Language for Multicores

Authors: Yip, Eugene; Girault, Alain; Roop, Partha; Biglari-Abhari, Morteza;

The ForeC Synchronous Deterministic Parallel Programming Language for Multicores

Abstract

Cyber-physical systems (CPSs) are embedded systems that are tightly integrated with their physical environment. The correctness of a CPS depends on the output of its computations and on the timeliness of completing the computations. This paper proposes the ForeC language for the deterministic parallel programming of CPS applications on multi-core execution platforms. ForeC's synchronous semantics is designed to greatly simplify the understanding and debugging of parallel programs. ForeC allows programmers to express many forms of parallel patterns while ensuring that programs are amenable to static timing analysis. One of ForeC's main innovation is its shared variable semantics that provides thread isolation and deterministic thread communication. Through benchmarking, we demonstrate that ForeC can achieve better parallel performance than Esterel, a widely used synchronous language for concurrent safety-critical systems, and OpenMP, a popular desktop solution for parallel programming. We demonstrate that the worst-case execution time of ForeC programs can be estimated precisely.

Keywords

[INFO.INFO-ES] Computer Science [cs]/Embedded Systems, [INFO.INFO-PL] Computer Science [cs]/Programming Languages [cs.PL]

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Top 10%
Green