Parallel Runtime and Algorithms for Small Datasets

Brice Videau 1, 2 Érik Saule 1, 3 Jean-François Mehaut 2
2 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
3 MOAIS - PrograMming and scheduling design fOr Applications in Interactive Simulation
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : In this paper, we put forward PaSTeL, an engine dedicated to parallel algorithms. PaSTeL offers both a programming model, to build parallel algorithms and an execution model based on work-stealing. Special care has been taken on using optimized thread activation and synchronization mechanisms. In order to illustrate the use of PaSTeL a subset of the STL's algorithms was implemented, which were also used on performance experiments. PaSTeL's performance is evaluated on a laptop computer using two cores, but also on a 16 cores platform. PaSTeL shows better performance than other implementations of the STL, especially on small datasets.
Type de document :
Communication dans un congrès
Proceedings of the 2nd International IEEE Workshop on Multi-Core Computing Systems (MuCoCoS), 2009, Fukuoka, Japan. pp.651-656, 2009, 〈10.1109/CISIS.2009.76〉
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https://hal.inria.fr/hal-00788922
Contributeur : Arnaud Legrand <>
Soumis le : vendredi 15 février 2013 - 13:46:27
Dernière modification le : jeudi 11 janvier 2018 - 06:22:02

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Brice Videau, Érik Saule, Jean-François Mehaut. Parallel Runtime and Algorithms for Small Datasets. Proceedings of the 2nd International IEEE Workshop on Multi-Core Computing Systems (MuCoCoS), 2009, Fukuoka, Japan. pp.651-656, 2009, 〈10.1109/CISIS.2009.76〉. 〈hal-00788922〉

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