Learning from the Success of MPI
HiPC '01 Proceedings of the 8th International Conference on High Performance Computing
Using graphics processors for high-performance IR query processing
Proceedings of the 17th international conference on World Wide Web
Algorithmic performance studies on graphics processing units
Journal of Parallel and Distributed Computing
A performance study of general-purpose applications on graphics processors using CUDA
Journal of Parallel and Distributed Computing
IEEE Micro
Parallel Computing Experiences with CUDA
IEEE Micro
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A finite-strip geometric nonlinear analysis is presented for elastic problems involving folded-plate structures. Compared with the standard finite-element method, its main advantages are in data preparation, program complexity, and execution time. The finite-strip method, which satisfies the von Karman plate equations in the nonlinear elastic range, leads to the coupling of all harmonics. However, coupling of series terms dramatically increases computation time in existing finite-strip sequential programs when a large number of series terms is used. The research reported in this paper combines various parallelization techniques and architectures (computing clusters and graphic processing units) with suitable programming models (MPI and CUDA) to speed up lengthy computations. In addition, a metric expressing the computational weight of input sets is presented. This metric allows computational complexity comparison of different inputs.