9135

Highly Scalable Multiplication for Distributed Sparse Multivariate Polynomials on Many-core Systems

Mickael Gastineau, Jacques Laskar
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}

}

Download Download (PDF)   View View   Source Source   

860

views

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.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: