• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.11
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• pp.972-978
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• 2012

This paper discusses the parallelization of multifrontal solution method, widely used for finite element structural analyses, for a shared memory architecture. Multifrontal method is easier than other linear solution methods because the solution procedure implies that unknowns can be eliminated simultaneously. Two innovative ideas are introduced to achieve optimal solver performance on a shared memory computer. Those are pairing two frontal matrices and splitting the frontal matrix in order to reduce the temporal memory space required by independent computing tasks. Performance comparisons between original algorithm and proposed one prove that proposed method is more computationally efficient on current multicore machines.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.6
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• pp.445-452
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• 2014

This paper discusses the multifrontal direct solution method with out of core storage for large scale structural analysis in a limited computing resource. Large scale structural analysis requires huge amount of memory space and computation, so out of core solution method is needed in limited computing resource. In this research, out of core multifrontal solution algorithm which utilize the small size of physical memory and minimize the amount of access of low speed out of core storage is introduced. Three ideas, which are stack space in lower trianglar part of square factorization matrix, inverse stack data structure and selective data caching and recovery by data block size, are proposed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.5
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• pp.361-367
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• 2014

In this paper, task scheduling and load balancing methods of multifrontal solution methods of finite element structural analysis in a modern multicore machine are introduced. Many structural analysis problems have generally irregular grid and many kinds of properties and materials. These irregularities and heterogeneities lead to bottleneck of parallelization and cause idle time to analysis. Therefore, task scheduling and load balancing are desired to reduce inefficiency. Several kinds of multithreaded parallelization methods are presented and comparison between static and dynamic task scheduling are shown. To reduce the idle time caused by irregular partitioned subdomains, computational load balancing methods, Balancing all tasks and minmax task pairing balancing, are invented. Theoretical and actual elapsed time are shown and the reason of their performance gap are discussed.