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A Short Overview of Executing Γ Chemical Reactions over the ΣC and τ C Dataflow Programming Models

Authors: Cudennec, Loïc; Goubier, Thierry;

A Short Overview of Executing Γ Chemical Reactions over the ΣC and τ C Dataflow Programming Models

Abstract

AbstractMany-core processors offer top computational power while keeping the energy consumption rea- sonable compared to complex processors. Today, they enter both high-performance computing systems, as well as embedded systems. However, these processors require dedicated programming models to efficiently benefit from their massively parallel architectures. The chemical programming paradigm has been introduced in the late eighties as an elegant way of formally describing distributed programs. Data are seen as molecules that can freely react thanks to operators to create new data. This paradigm has also been used within the context of grid Computing and now seems to be relevant for many-core processors. Very few implementations of runtimes for chemical programming have been proposed, none of them giving serious elements on how it can be deployed onto a real architecture. In this paper, we propose to implement some parts of the chemical paradigm over the ΣC dataflow programming language, that is dedicated to many-core processors. We show how to represent molecules using agents and communication links, and to iteratively build the dataflow graph following the chemical reactions. A prelim- inary implementation of the chemical reaction mechanisms is provided using the τ C dataflow compilation toolchain, a language close to ΣC, in order to demonstrate the relevance of the proposition.

Keywords

dataflow programming, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], many-core processor, Chemical programming

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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!
0
Average
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