Parallel Algorithms for Constructing Data Structures for Fast Multipole Methods
Department of Computer Science, University of Maryland, College Park
arXiv:1301.1704 [cs.MS], (8 Jan 2013)
@article{2013arXiv1301.1704H,
author={Hu}, Q. and {Gumerov}, N.~A. and {Duraiswami}, R.},
title={"{Parallel Algorithms for Constructing Data Structures for Fast Multipole Methods}"},
journal={ArXiv e-prints},
archivePrefix={"arXiv"},
eprint={1301.1704},
primaryClass={"cs.MS"},
keywords={Computer Science – Mathematical Software, Computer Science – Distributed, Parallel, and Cluster Computing},
year={2013},
month={jan},
adsurl={http://adsabs.harvard.edu/abs/2013arXiv1301.1704H},
adsnote={Provided by the SAO/NASA Astrophysics Data System}
}
We present efficient algorithms to build data structures and the lists needed for fast multipole methods. The algorithms are capable of being efficiently implemented on both serial, data parallel GPU and on distributed architectures. With these algorithms it is possible to map the FMM efficiently on to the GPU or distributed heterogeneous CPU-GPU systems. Further, in dynamic problems, as the distribution of the particles change, the reduced cost of building the data structures improves performance. Using these algorithms, we demonstrate example high fidelity simulations with large problem sizes by using FMM on both single and multiple heterogeneous computing facilities equipped with multi-core CPU and many-core GPUs.
January 11, 2013 by hgpu
