Browse > Article
http://dx.doi.org/10.7582/GGE.2018.21.4.213

Comparison of Parallel Computation Performances for 3D Wave Propagation Modeling using a Xeon Phi x200 Processor  

Lee, Jongwoo (Department of Energy Resources Engineering, Pukyong National University)
Ha, Wansoo (Department of Energy Resources Engineering, Pukyong National University)
Publication Information
Geophysics and Geophysical Exploration / v.21, no.4, 2018 , pp. 213-219 More about this Journal
Abstract
In this study, we simulated 3D wave propagation modeling using a Xeon Phi x200 processor and compared the parallel computation performance with that using a Xeon CPU. Unlike the 1st generation Xeon Phi coprocessor codenamed Knights Corner, the 2nd generation x200 Xeon Phi processor requires no additional communication between the internal memory and the main memory since it can run an operating system directly. The Xeon Phi x200 processor can run large-scale computation independently, with the large main memory and the high-bandwidth memory. For comparison of parallel computation, we performed the modeling using the MPI (Message Passing Interface) and OpenMP (Open Multi-Processing) libraries. Numerical examples using the SEG/EAGE salt model demonstrated that we can achieve 2.69 to 3.24 times faster modeling performance using the Xeon Phi with a large number of computational cores and high-bandwidth memory compared to that using the 12-core CPU.
Keywords
three-dimensional; wave propagation modeling; Xeon Phi; OpenMP; MPI;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Jo, S.-H., and Ha, W., 2018, 3D time-domain wave propagation modeling using high-performance Python libraries, J. Korea Inst. Mineral Mining Eng., 55(3), 213-218 (in Korean with English abstract).
2 Kim, A., Ryu, D., and Ha, W., 2016, Time-domain 3D wave propagation modeling and memory management using graphics processing units, Geophys. and Geophys. Explor., 19(3), 145-152 (in Korean with English abstract).   DOI
3 Lu, L., Renwei, D., Hongwei, L., and Hong, L., 2015, 3D hybrid-domain full waveform inversion on GPU, Comput. Geosci., 83, 27-36.   DOI
4 Min, D.-J., Pyun, S., Ha, W., Kwak, S., Chung, W., and Shin, C., 2016, Numerical Analysis for Geophysics, CIR, Seoul, Korea, 37-52.
5 Pacheco, P., 2011, An introduction to parallel programming, Morgan Kaufmann, 15-82.
6 Reinders, J., and Jeffers, J., 2015, High performance parallelism pearls: Multicore and Many-core Programming Approaches, Morgan Kaufmann, 377-396.
7 Rodriguez, S., Farre, P., Rosas, C., and Hanzich, M., 2017, Evaluating directive-based programming models on Wave Propagation Kernels, 79th EAGE Conference and Exhibition 2017-Workshops, Paris, France.
8 Ryu, D., Jo, S. H., and Ha, W., 2017, Parallelizing 3D frequencydomain acoustic wave propagation modeling using a Xeon Phi coprocessor, Geophys. and Geophys. Explor., 20(3), 129-136 (in Korean with English abstract).   DOI
9 Sourouri, M., and Birger Raknes, E., 2017, Accelerating 3D Elastic Wave Equations on Knights Landing based Intel Xeon Phi processors, 19th EGU General Assembly Conference Abstracts, 19, 7790p.
10 Tobin, J., Breuer, A., Heinecke, A., Yount, C., and Cui, Y., 2017, Accelerating seismic simulations using the Intel Xeon Phi knights landing processor, 2017 International Supercomputing Conference, High Performance Computing, 139- 157.
11 Jeffers, J., Reinders, J., and Sodani, A., 2016, Intel Xeon Phi processor high performance programming: Knights Landing edition, Morgan Kaufmann, 3-145.
12 Wikipedia, 2018, https://en.wikipedia.org/wiki/Stencil_code (July 30, 2018 Accessed)
13 Abdelkhalek, R., Calandra, H., Coulaud, O., Roman, J., and Latu, G., 2009, Fast seismic modeling and reverse time migration on a GPU cluster, 2009 International Conference on High Performance Computing & Simulation, 36-43.
14 Heinecke, A., Breuer, A., Bader, M., and Dubey, P., 2016, High order seismic simulations on the intel Xeon Phi processor (Knights Landing). 2016 International Conference on High Performance Computing & Simulation, 343-362.
15 Intel, 2018, https://ark.intel.com/compare/81908,94033 (August 20, 2018 Accessed)