Highly Scalable Multiplication for Distributed Sparse Multivariate Polynomials on Many-core Systems
IMCCE-CNRS UMR8028, Observatoire de Paris, UPMC, Astronomie et Systemes Dynamiques, 77 Avenue Denfert-Rochereau, 75014 Paris, France
arXiv:1303.7425 [cs.SC],
@article{2013arXiv1303.7425G,
author={Gastineau}, M. and {Laskar}, J.},
title={"{Highly Scalable Multiplication for Distributed Sparse Multivariate Polynomials on Many-core Systems}"},
journal={ArXiv e-prints},
archivePrefix={"arXiv"},
eprint={1303.7425},
primaryClass={"cs.SC"},
keywords={Computer Science – Symbolic Computation, Astrophysics – Instrumentation and Methods for Astrophysics, Computer Science – Distributed, Parallel, and Cluster Computing, Computer Science – Mathematical Software},
year={2013},
month={mar},
adsurl={http://adsabs.harvard.edu/abs/2013arXiv1303.7425G},
adsnote={Provided by the SAO/NASA Astrophysics Data System}
}
We present a highly scalable algorithm for multiplying sparse multivariate polynomials represented in a distributed format. This algo- rithm targets not only the shared memory multicore computers, but also computers clusters or specialized hardware attached to a host computer, such as graphics processing units or many-core coprocessors. The scal- ability on the large number of cores is ensured by the lacks of synchro- nizations, locks and false-sharing during the main parallel step.
April 8, 2013 by hgpu
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