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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 7/2020

27.03.2019

Programming bsp and multi-bsp algorithms in ml

Erschienen in: The Journal of Supercomputing | Ausgabe 7/2020

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Abstract

The bsml and multi-ml languages have been designed for programming, à laml, algorithms of the respectively bsp and multi-bsp bridging models. multi-bsp is an extension of the well-know bsp model by taking into account the different levels of networks and memories of modern hierarchical architectures. This is a rather new model, as well as multi-ml is a new language, while bsp and bsml have been used for a long time in many different domains. Intuitively, designing and programming multi-bsp algorithms seems more complex than with bsp, and one can ask whether it is beneficial to rewrite bsp algorithms using the multi-bsp model. In this paper, we thus investigate the pro and cons of the aforementioned models and languages by experimenting with them on different typical applications. For this, we use a methodology to measure the level of difficulty of writing code and we also benchmark them in order to show whether writing multi-ml code is worth the effort.
Vorheriger Artikel Detecting semantic violations of lock-free data structures through C++ contracts
Nächster Artikel Generation of high-performance code based on a domain-specific language for algorithmic skeletons

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Fußnoten
1
We currently make the hypothesis that the more complicated the codes are, the more complicated the algorithmic design is. We leave the problematic of algorithmic design for future work and we focus on code writing only.
 
2
We do not show the bsp parameters nor the multi-bsp ones because we do not use them at all in this work. In fact, cost prediction is rather impossible in some of our application cases, e.g. the state-space construction where the number of computing states is unknown and no upper bound is possible in practice [9].
 
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Metadaten
Titel
Programming bsp and multi-bsp algorithms in ml
Publikationsdatum
27.03.2019
Erschienen in
The Journal of Supercomputing / Ausgabe 7/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-019-02822-9

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