Nested Parallelization of the Flow Solver TFS Using the ParaWise Parallelization Environment

Johnson, S. P.; Leggett, P. F.; Ierotheou, C. S.; Spiegel, Alexander; Mey, Dieter an; Hörschler, Ingolf

doi:10.1007/978-3-540-68555-5_18

Cited by 3 publications

(3 citation statements)

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“…Furthermore, there are situations where only a small number of blocks have been defined for the simulation (in the extreme case a single block), so the block level parallelisation will not satisfy every eventuality. As an overhead is incurred each time a loop is parallelised, the higher the level of the loop the parallelisation is applied at, the lower the overheads will be, as discussed in [10]. Therefore, the ideal loop for an OpenMP parallelisation in the typical COSA is the block loop.…”

Section: Shared Memory Parallelisationmentioning

confidence: 99%

Shared-memory, distributed-memory, and mixed-mode parallelisation of a CFD simulation code

Jackson

Campobasso

2011

Comput Sci Res Dev

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This paper presents some different approaches to the parallelisation of a harmonic balance Navier-Stokes solver for unsteady aerodynamics. Such simulation codes can require very large amounts of computational resource for realistic simulations, and therefore can benefit significantly from parallelisation. The simulation code addressed in this paper can undertake different modes of aerodynamic simulation and includes both harmonic balance and time domain solvers. These different modes have performance characteristics which can affect any potential parallelisation, as can the specifics of the problem being simulated. Therefore, three different techniques have been used for the parallelisation, shared-memory, distributed-memory, and a combination of the two-a hybrid or mixed-mode parallelisation. These different techniques attempt to address the different performance requirements associated with the types of simulation the code can be used for and provide the level of computational resources required for significant simulation problems. We discuss the different parallelisations and the performance they exhibit on a range of computational resources.

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Section: Shared Memory Parallelisationmentioning

confidence: 99%

Shared-memory, distributed-memory, and mixed-mode parallelisation of a CFD simulation code

Jackson

Campobasso

2011

Comput Sci Res Dev

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“…The ParaWise/CAPO automatic parallelization environment [15,16] has been used to assist in the OpenMP parallelization of the TFS multi-block code accomplished by the coauthors of [3] from Parallel Software Products and the University of Greenwich. A parallel version of TFS that can scale to large numbers of processors targeted at Sun Microsystems Sun Fire E25K shared memory parallel systems (SFE25K) was the ultimate goal of this work [17].…”

Section: Fig 3 Computing the Critical Points For A Combustion Enginementioning

confidence: 99%

“…OpenMP is employed on the block and on the loop level. This application puts a high load on the memory system and thus is quite sensitive to ccNUMA effects [3].…”

Section: Introductionmentioning

confidence: 99%

Nested Parallelization with OpenMP

Mey

Sarholz

Terboven

2007

Int J Parallel Prog

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OpenMP is widely accepted as a de facto standard for shared memory parallel programming in Fortran, C and C++. Nested parallelization has been included in the first OpenMP specification, but it took a few years until the first commercially available compilers supported this optional part of the specification. We employed nested parallelization using OpenMP in three production codes: a C++ code for content-based image retrieval, a C++ code for the computation of critical points in multi-block CFD datasets, and a multi-block Navier-Stokes solver written in Fortran90. In this paper we discuss the opportunities as well as the deficiencies of the nested parallelization support in OpenMP.

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