Parallel Computing of Laser Propulsion with Hypergraph Partitioning

Abstract:

As one of the most important methods to study laser propelled rocket, the numerical simulation of laser propulsion has drawn an ever increasing attention at present. Nevertheless, the traditional serial computing cannot satisfy the practical requirements because of high calculation precision, insatiable memory overhead and considerable computation time. In this paper, we study on a general algorithm for laser propulsion, and divide the computing domain by using a multilevel hypergraph partitioning algorithm. Furthermore, MPI allreduce, overlapping communication with computation and non blocking communication are adopted to decrease the communication time when dealing with global communication. Finally, parallel performance about two typical configurations on a China-made supercomputer shows the smallest value of speedup ratio is more than 123 when the number of processors is 256. In conclusion, our parallel method is effective and practical in numerical simulation of laser propulsion.

• Periodical: Advanced Materials Research (Volumes 760-762)
• Pages: 311-314
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.760-762.311
• Online since: September 2013
• Authors: Yao Yuan Zeng, Wen Tao Zhao, Zheng Hua Wang
• Keywords: Laser Propulsion, Multilevel Hypergraph Partitioning, Numerical Simulation, Parallel Computing, Parallel Performance

References

[1] A. Kantmwitz. Astronautics and Aeronautics 10 (1972), p.74.

[2] H. Horisawa, S. Sumida, H. Yonamine, and I. Funaki. Vacuum 88 (2013), pp.75-78.

[3] B. Wang, K. Michigami, K. Komurasaki, and Y. Arakawa. Journal of Propulsion and Power 29 (2012), pp.276-278.

[4] Y. Jamil, H. Saeed, M.R. Ahmad, S.A. Khan, H. Farooq, M. Shahid, K.M. Zia, and N. Amin. Applied Physics A 110 (2013), pp.207-210.

DOI: 10.1007/s00339-012-7115-z

[5] A. Cevahir, A. Nukada, and S. Matsuoka. Computer Science-Research and Development 25 (2010), pp.83-91.

[6] E. Fischer, A. Matsliah, and A. Shapira. SIAM Journal on Computing 39 (2010), pp.3155-3185.

[7] U.V. Catalyurek, C. Aykanat, and E. Kayaaslan. SIAM Journal on Scientific Computing 33 (2011), p.1996-(2023).

[8] G. Karypis, R. Aggarwal, V. Kumar, and S. Shekhar. Very Large Scale Integration (VLSI) Systems, IEEE Transactions on 7 (1999), pp.69-79.

DOI: 10.1109/92.748202

[9] U.V. Catalyurek, M. Deveci, K. Kaya, and B. Ucar, Multithreaded clustering for multi-level hypergraph partitioning, Parallel & Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International, IEEE, 2012, pp.848-859.

DOI: 10.1109/ipdps.2012.81

[10] U.V. Catalyurek, D. Bozdag, E.G. Boman, K.D. Devine, R. Heaphy, and L.A. Riesen. Advanced Computational Infrastructures for Parallel and Distributed Applications 66 (2010), pp.311-333.

DOI: 10.1002/9780470558027.ch15

[11] A.R. Mamidala, D. Faraj, S. Kumar, D. Miller, M. Blocksome, T. Gooding, P. Heidelberger, and G. Dozsa, Optimizing MPI Collectives Using Efficient Intra-node Communication Techniques over the Blue Gene/P Supercomputer, Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on, IEEE, 2011, pp.771-780.

DOI: 10.1109/ipdps.2011.220

[12] V. Marjanovi, J. Labarta, E. Ayguade, and M. Valero, Overlapping communication and computation by using a hybrid MPI/SMPSs approach, Proceedings of the 24th ACM International Conference on Supercomputing, ACM, 2010, pp.5-16.

DOI: 10.1145/1810085.1810091

[13] K. Kandalla, H. Subramoni, K. Tomko, D. Pekurovsky, S. Sur, and D.K. Panda. Computer Science-Research and Development 26 (2011), pp.237-246.

DOI: 10.1007/s00450-011-0170-4