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Scientific Programming
Volume 2016 (2016), Article ID 2360492, 14 pages
Research Article

A New Parallel Method for Binary Black Hole Simulations

1Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
2Institute of Applied Mathematics, Academy of Mathematics and Systems Science Chinese Academy of Sciences, Beijing 100190, China
3Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA
4School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA

Received 1 January 2016; Revised 25 May 2016; Accepted 6 June 2016

Academic Editor: Bormin Huang

Copyright © 2016 Quan Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Simulating binary black hole (BBH) systems are a computationally intensive problem and it can lead to great scientific discovery. How to explore more parallelism to take advantage of the large number of computing resources of modern supercomputers is the key to achieve high performance for BBH simulations. In this paper, we propose a scalable MPM (Mesh based Parallel Method) which can explore both the inter- and intramesh level parallelism to improve the performance of BBH simulation. At the same time, we also leverage GPU to accelerate the performance. Different kinds of performance tests are conducted on Blue Waters. Compared with the existing method, our MPM can improve the performance from 5x speedup (compared with the normalized speed of 32 MPI processes) to 8x speedup. For the GPU accelerated version, our MPM can improve the performance from 12x speedup to 28x speedup. Experimental results also show that when only enough CPU computing resource or limited GPU computing resource is available, our MPM can employ two special scheduling mechanisms to achieve better performance. Furthermore, our scalable GPU acceleration MPM can achieve almost ideal weak scaling up to 2048 GPU computing nodes which enables our software to handle even larger BBH simulations efficiently.