Efficient solid state NMR powder simulations using SMP and MPP parallel computation

Methods for parallel simulation of solid state NMR powder spectra are presented for both shared and distributed memory parallel supercomputers. For shared memory architectures the performance of simulation programs implementing the OpenMP application programming interface is evaluated. It is demonst...

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Bibliographic Details
Published in:	Journal of magnetic resonance (1997) Vol. 161; no. 2; pp. 183 - 190
Main Authors:	Kristensen, Jørgen Holm, Farnan, Ian
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.04.2003
Subjects:	Parallel programming in the OpenMP application programming interface and MPI message passing interface Parallel simulation of solid state NMR powder spectra Shared and distributed memory parallel supercomputers Parallel programming in the OpenMP application programming interface and MPI message passing interface Parallel simulation of solid state NMR powder spectra Shared and distributed memory parallel supercomputers
ISSN:	1090-7807, 1096-0856
Online Access:	Get full text
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Summary:	Methods for parallel simulation of solid state NMR powder spectra are presented for both shared and distributed memory parallel supercomputers. For shared memory architectures the performance of simulation programs implementing the OpenMP application programming interface is evaluated. It is demonstrated that the design of correct and efficient shared memory parallel programs is difficult as the performance depends on data locality and cache memory effects. The distributed memory parallel programming model is examined for simulation programs using the MPI message passing interface. The results reveal that both shared and distributed memory parallel computation are very efficient with an almost perfect application speedup and may be applied to the most advanced powder simulations.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	1090-7807 1096-0856
DOI:	10.1016/S1090-7807(03)00007-7