An Efficient Deterministic Parallel Algorithm for Adaptive Multidimensional Numerical Integration on GPUs

Recent development in Graphics Processing Units (GPUs) has enabled a new possibility for highly efficient parallel computing in science and engineering. Their massively parallel architecture makes GPUs very effective for algorithms where processing of large blocks of data can be executed in parallel...

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Vydáno v:Proceedings of the International Conference on Parallel Processing s. 486 - 491
Hlavní autoři: Arumugam, Kamesh, Godunov, Alexander, Ranjan, Desh, Terzic, Bala, Zubair, Mohammad
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2013
Témata:
Benchmark testing
Computer architecture
Educational institutions
Graphics processing units
Instruction sets
Parallel algorithms
ISSN:0190-3918
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Shrnutí:Recent development in Graphics Processing Units (GPUs) has enabled a new possibility for highly efficient parallel computing in science and engineering. Their massively parallel architecture makes GPUs very effective for algorithms where processing of large blocks of data can be executed in parallel. Multidimensional integration has important applications in areas like computational physics, plasma physics, computational fluid dynamics, quantum chemistry, molecular dynamics and signal processing. The computationally intensive nature of multidimensional integration requires a high-performance implementation. In this study, we present an efficient deterministic parallel algorithm for adaptive multidimensional numerical integration on GPUs. Various optimization techniques are applied to maximize the utilization of the GPU. GPU-based implementation outperforms the best known sequential methods and achieves a speed-up of up to 100. It also shows good scalability with the increase in dimensionality.
ISSN:0190-3918
DOI:10.1109/ICPP.2013.59