Optimisation of multiplet identifier processing on a $PLAYSTATION^{(R)}$ 3

플레이스테이션 3 상에서 수행되는 멀티플렛 식별자의 최적화

  • Received : 2009.09.19
  • Accepted : 2009.12.04
  • Published : 2010.02.18

Abstract

To enable high-performance computing (HPC) for applications with large datasets using a $Sony^{(R)}$ $PLAYSTATION^{(R)}$ 3 ($PS3^{TM}$) video game console, we configured a hybrid system consisting of a $Windows^{(R)}$ PC and a $PS3^{TM}$. To validate this system, we implemented the real-time multiplet identifier (RTMI) application, which identifies multiplets of microearthquakes in terms of the similarity of their waveforms. The cross-correlation computation, which is a core algorithm of the RTMI application, was optimised for the $PS3^{TM}$ platform, while the rest of the computation, including data input and output remained on the PC. With this configuration, the core part of the algorithm ran 69 times faster than the original program, accelerating total computation speed more than five times. As a result, the system processed up to 2100 total microseismic events, whereas the original implementation had a limit of 400 events. These results indicate that this system enables high-performance computing for large datasets using the $PS3^{TM}$, as long as data transfer time is negligible compared with computation time.

소니 플레이스테이션3 (PS3) 비디오 게임 콘솔을 이용하여 대용량자료에 고성능 계산을 적용시키기 위하여 개인용컴퓨터 (PC) 의 윈도우 시스템과 PS3로 구성된 하이브리드 시스템을 제작하였다. 이 시스템의 성능을 검증하기 위해 파형모양의 유사도를 이용하여 미세지진의 멀티플렛을 알아내는 실시간 멀티플렛 식별자 (RTMI)를 수행하여보았다. RTMI의 핵심 알고리즘인 상호상관 계산은 PS3 플랫폼에 최적화 되었고 자료의 압출력을 포함하는 다른 계산들은 PC 상에서 수행되었다. 이 경우에 알고리즘의 핵심 부분이 원래의 경우보다 50 배 이상 빨리 수행되어 결과적으로 개발된 시스템은 과거 400개의 신호밖에 처리하지 못하던 것을 총 2100개까지의 미소진통 신호등을 처리 할 수 있게 하였다. 이 결과는 자료전송시간이 계산시간에 비해 무시할 수 있는 한 PS3를 이용한 대용량 자료의 고성능 계산이 가능하다는 것을 잘 보여주고 있다.

Keywords

References

  1. Arrowsmith, S. J., and Eisner, L., 2006, A technique for identifying microseismic multiplets and application to the Valhall Field, North Sea: Geophysics, 71, V31. doi:10.1190/1.2187804
  2. Eisner, L., Fischer, T., and Le Calvez, J. H., 2006, Detection of repeated hydraulic fracturing (out-of-zone growth) by microseismic monitoring: The Leading Edge, 25, 548–554. doi:10.1190/1.2202655
  3. Hattori, M., and Mizuno, T., 2007, Real-time seismic data processing on PLAYSTATION$^{\circledR}$ 3: Proceedings of the SEGJ Conference, 117, 92–94.
  4. IBM. Spain-Based Repsol, Barcelona Supercomputing Centre Use IBM Technology to Tap Into New Frontiers of Oil Exploration. Press release. Available online at: http://www-03.ibm.com/press/us/en/pressrelease/24556.wss [verified January 2010].
  5. Kurzak, J., Buttari, A., and Dongarra, J., 2007, Solving Systems of Linear Equations on the CELL Processor Using Cholesky Factorization –LAPACK Working Note 184, Technical Report UT-CS-07–596: Department of Computer Science, University of Tennessee.
  6. Michaud, G, and Leaney, S., 2008, Continuous microseismic mapping for real-time event detection and location: SEG Expanded Abstracts, 27, 1357–1361.
  7. Waldhauser, F. M., and Ellsworth, W. L., 2000, A double-difference earthquake location algorithm: Method and application to the northern Hayward fault, California: Bulletin of the Seismological Society of America, 90, 1353–1368. doi:10.1785/0120000006
  8. Williams, S., Shalf, J., Oliker, L., Kamil, S., Husbands, P., and Yelick, K., 2006, The Potential of the Cell Processor for Scientific Computing: Available online at: http://bebop.cs.berkeley.edu/pubs/williams2006-cell-scicomp.pdf [verified January 2010].