2015
DOI: 10.1002/cpe.3494
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The SIPSim implicit parallelism model and the SkelGIS library

Abstract: Scientific simulations give rise to complex codes where data size and computation time become very important issues, and sometimes a scientific barrier. Thus, parallelization of scientific simulations becomes a significant work. Many time and human efforts are deployed to produce efficient parallel programs. But still, many simulations could not be parallelized because of lack of time to learn parallel programming or lack of human resources. Therefore, aiding parallelization through abstracted parallelism or i… Show more

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Cited by 8 publications
(10 citation statements)
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References 18 publications
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“…For instance, data may need to be merged into a single result. is computation strategy is the one followed by the parallel algorithmic skeletons [16] on data structures [19,35].…”
Section: Parallelizing Computationsmentioning
confidence: 99%
“…For instance, data may need to be merged into a single result. is computation strategy is the one followed by the parallel algorithmic skeletons [16] on data structures [19,35].…”
Section: Parallelizing Computationsmentioning
confidence: 99%
“…As an example of production numerical simulation, we consider FullSWOF2D 4 [5] (denoted FS2D), developed at the MAPMO laboratory, University of Orléans, France. FS2D consists in solving the Shallow Water equations (two dimensional Navier-Stokes equations) using finite volumes methods especially chosen for hydrodynamic purposes (transitions between wet and dry areas, small water heights, etc.).…”
Section: A Fullswof2d Applicationmentioning
confidence: 99%
“…In MSL these techniques are implemented by using the Message Passing Interface (MPI) and the OpenMP Application Programming Interface. The four code versions produced by MSL are: (1) MpiBase, where data parallelization is applied by domain decomposition and by using MPI; (2) MpiOmpFor, where data parallelization is introduced at two different levels, first, by domain decomposition with MPI, and second, by using parallel loops of OpenMP; (3) MpiOmpForkJoin, where both data and task parallelization techniques are combined, and where the adopted task parallelization technique is a static fork/join scheduling implemented using OpenMP; and finally (4) MpiOmpDyn, where both data and task parallelization techniques are also combined, but where the adopted task parallelization technique is the dynamic scheduling of tasks introduced in OpenMP 4.5 5 .…”
Section: B the Multi-stencil Languagementioning
confidence: 99%
“…Coullon et al [15], in their paper Implicit parallelism on 2D meshes using SkelGIS, tackle the issue of overcoming restrictions in parallelization of scientific simulations because of the complexity of functional concepts and specific features. Parallelization of scientific simulations requires a lot of efforts and field-specific knowledge to produce efficient parallel programs.…”
Section: Software Systems Languages and Librariesmentioning
confidence: 99%