Efficient parallel programming with algorithmic skeletons

Botorog, George Horatiu; Kuchen, Herbert

doi:10.1007/3-540-61626-8_95

Cited by 25 publications

(8 citation statements)

References 8 publications

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“…We can enumerate lots of algorithmic skeleton libraries, all of them based on Muray Cole's work [63,64,65]. Some of them are implemented in Java, some of them in C++ as for example QUAFF [66], eSkel [67], Muesli [68], SkeTo [69] and OSL [70].…”

Section: Algorithmic Skeletonsmentioning

confidence: 99%

FullSWOF Paral: Comparison of two parallelization strategies (MPI and SKELGIS) on a software designed for hydrology applications

et al. 2013

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Abstract. In this paper, we perform a comparison of two approaches for the parallelization of an existing, free software, FullSWOF 2D (http ://www.univ-orleans.fr/mapmo/soft/FullSWOF/ that solves shallow water equations for applications in hydrology) based on a domain decomposition strategy. The first approach is based on the classical MPI library while the second approach uses Parallel Algorithmic Skeletons and more precisely a library named SkelGIS (Skeletons for Geographical Information Systems). The first results presented in this article show that the two approaches are similar in terms of performance and scalability. The two implementation strategies are however very different and we discuss the advantages of each one.

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Section: Algorithmic Skeletonsmentioning

confidence: 99%

FullSWOF Paral: Comparison of two parallelization strategies (MPI and SKELGIS) on a software designed for hydrology applications

et al. 2013

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“…Among them, two C++ skeleton libraries propose two dimensional data structures and are close to SkelGIS: Muesli [7] from Münster and SkeTo [8] from Tokyo. Muesli is less competitive than SkeTo because of lack of C++ optimizations, but provides an hybrid MPI/OpenMP version for SMP clusters.…”

Section: Related Workmentioning

confidence: 99%

Algorithmic skeleton library for scientific simulations: SkelGIS

Coullon¹,

Limet

2013

2013 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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Scientific simulations give rise to complex codes where data size and computation time become very important issues and sometimes a scientific barrier. Therefore, aiding parallelization has become a main topic in computer science. In this paper is introduced a new type of solution to give a totally transparent access to parallel programming for noncomputer scientists. This solution, named SkelGIS, takes place at the crossroads of domain specific languages, domain specific libraries and skeleton libraries, getting advantages of each domain. SkelGIS aims at providing a general solution for simulations in heterogeneous scientific domains. SkelGIS does not require a new language learning, nor to manipulate specific parallel tools, and it ensures a sequential programming style to the user with a totally hidden parallelization. Concepts and technical choices of the library are described in this paper. Furthermore, two very different benchmarks are studied to bring out performances of the library on light and complex simulations.

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“…Within Skil 44, skeletons are not directly part of the language but are implemented with it 135, 136. Skil uses a subset of C language which provides functional language‐like features such as higher order functions, curring, and polymorphic types 137, 138.…”

Section: Individualized Description Of Featuresmentioning

confidence: 99%

A survey of algorithmic skeleton frameworks: high‐level structured parallel programming enablers

2010

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Structured parallel programs ought to be conceived as two separate and complementary entities: computation, which expresses the calculations in a procedural manner, and coordination, which abstracts the interaction and communication. By abstracting commonly used patterns of parallel computation, communication, and interaction, algorithmic skeletons enable programmers to code algorithms without specifying platform‐dependent primitives. This article presents a literature review on algorithmic skeleton frameworks (ASKF), parallel software development environments furnishing a collection of parameterizable algorithmic skeletons, where the control flow, nesting, resource monitoring, and portability of the resulting parallel program is dictated by the ASKF as opposed to the programmer. Consequently, the ASKF can be positioned as high‐level structured parallel programming enablers, as their systematic utilization permits the abstract description of programs and fosters portability by focusing on the description of the algorithmic structure rather than on its detailed implementation. Copyright © 2010 John Wiley & Sons, Ltd.

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Efficient parallel programming with algorithmic skeletons

Cited by 25 publications

References 8 publications

FullSWOF Paral: Comparison of two parallelization strategies (MPI and SKELGIS) on a software designed for hydrology applications

FullSWOF Paral: Comparison of two parallelization strategies (MPI and SKELGIS) on a software designed for hydrology applications

Algorithmic skeleton library for scientific simulations: SkelGIS

A survey of algorithmic skeleton frameworks: high‐level structured parallel programming enablers

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