A parallel solver for huge dense linear systems
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Altres documents de l'autoria: Badía, José; Movilla, Jose L.; Climente, Juan I.; Castillo Catalán, María Isabel; Marqués-Andrés, Mercedes; Mayo, Rafael; Quintana-Orti, Enrique S.; Planelles, Josep
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http://dx.doi.org/10.1016/j.cpc.2011.06.010 |
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Títol
A parallel solver for huge dense linear systemsAutoria
Data de publicació
2011-11Editor
ElsevierISSN
0010-4655Cita bibliogràfica
Computer Physics Communications (November 2011) vol. 182, no. 11, 2441–2442Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
http://www.sciencedirect.com/science/article/pii/S0010465511002207Paraules clau / Matèries
Resum
HDSS (Huge Dense Linear System Solver) is a Fortran Application Programming Interface (API) to facilitate the parallel solution of very large dense systems to scientists and engineers. The API makes use of parallelism ... [+]
HDSS (Huge Dense Linear System Solver) is a Fortran Application Programming Interface (API) to facilitate the parallel solution of very large dense systems to scientists and engineers. The API makes use of parallelism to yield an efficient solution of the systems on a wide range of parallel platforms, from clusters of processors to massively parallel multiprocessors. It exploits out-of-core strategies to leverage the secondary memory in order to solve huge linear systems O(100.000).
The API is based on the parallel linear algebra library PLAPACK, and on its Out-Of-Core (OOC) extension POOCLAPACK. Both PLAPACK and POOCLAPACK use the Message Passing Interface (MPI) as the communication layer and BLAS to perform the local matrix operations.
The API provides a friendly interface to the users, hiding almost all the technical aspects related to the parallel execution of the code and the use of the secondary memory to solve the systems. In particular, the API can automatically select the best way to store and solve the systems, depending of the dimension of the system, the number of processes and the main memory of the platform.
Experimental results on several parallel platforms report high performance, reaching more than 1 TFLOP with 64 cores to solve a system with more than 200 000 equations and more than 10 000 right-hand side vectors. [-]
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