Multidimensional adaptative and deterministic integration in CUDA and OpenMP

Autor:	A. González-Zamudio, Amilcar Meneses-Viveros, F. Carranza, C. G. Cortés, R. Quintero-Monsebaiz, Alberto Vela
Rok vydání:	2021
Předmět:	Set (abstract data type) CUDA Tensor product Hardware and Architecture Adaptive method Computer science Multidimensional integration Benchmark (computing) Parallel computing Grid Software Information Systems Theoretical Computer Science
Zdroj:	The Journal of Supercomputing. 77:12075-12097
ISSN:	1573-0484 0920-8542
Popis:	Parallelization schemes on many-core architectures, in this case, CUDA and OpenMP, are used to accelerate and improve the accuracy of adaptive multidimensional integration algorithms. The one-dimensional Gauss–Kronrod adaptive method is generalized to 3, 4, 5 and 6 dimensions. The implementation of the traditional tensor product construction of the grid and weights for multidimensional integration is revisited and reformulated taking advantages of the multi and many-core architectures. Tests performed in a set of benchmark functions show that the algorithm is numerically accurate, with accelerations as high as 800X in CUDA and 300X in the OpenMP implementation both compared to a sequential multidimensional integration algorithm.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9a5ce701c0d7988b414262ea271f8a2b https://doi.org/10.1007/s11227-021-03752-1 Zobrazit plný text záznamu Plný text ve formátu PDF