A Parallel Butterfly Algorithm

Poulson, Jack; Demanet, Laurent; Maxwell, Nicholas; Ying, Lexing

A Parallel Butterfly Algorithm

Type

Article

Authors

Poulson, Jack
Demanet, Laurent
Maxwell, Nicholas
Ying, Lexing

Online Publication Date

2014-02-04

Print Publication Date

2014-01

Date

2014-02-04

Abstract

The butterfly algorithm is a fast algorithm which approximately evaluates a discrete analogue of the integral transform (Equation Presented.) at large numbers of target points when the kernel, K(x, y), is approximately low-rank when restricted to subdomains satisfying a certain simple geometric condition. In d dimensions with O(Nd) quasi-uniformly distributed source and target points, when each appropriate submatrix of K is approximately rank-r, the running time of the algorithm is at most O(r2Nd logN). A parallelization of the butterfly algorithm is introduced which, assuming a message latency of α and per-process inverse bandwidth of β, executes in at most (Equation Presented.) time using p processes. This parallel algorithm was then instantiated in the form of the open-source DistButterfly library for the special case where K(x, y) = exp(iΦ(x, y)), where Φ(x, y) is a black-box, sufficiently smooth, real-valued phase function. Experiments on Blue Gene/Q demonstrate impressive strong-scaling results for important classes of phase functions. Using quasi-uniform sources, hyperbolic Radon transforms, and an analogue of a three-dimensional generalized Radon transform were, respectively, observed to strong-scale from 1-node/16-cores up to 1024-nodes/16,384-cores with greater than 90% and 82% efficiency, respectively. © 2014 Society for Industrial and Applied Mathematics.

Citation

Poulson J, Demanet L, Maxwell N, Ying L (2014) A Parallel Butterfly Algorithm. SIAM Journal on Scientific Computing 36: C49–C65. Available: http://dx.doi.org/10.1137/130921544.

Acknowledgements

This work was partially supported by NSF CAREER grant 0846501 (L.Y.), DOE grant DE-SC0009409 (L.Y.), and KAUST. Furthermore, this research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357.