Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/8097
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dc.contributor.authorallAlessi, F.; Department of Computer Science, Sapienza University of Rome, Italyen
dc.contributor.authorallMassini, A.; Department of Computer Science, Sapienza University of Rome, Italyen
dc.contributor.authorallBasili, R.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma1, Roma, Italiaen
dc.date.accessioned2012-10-09T08:06:52Zen
dc.date.available2012-10-09T08:06:52Zen
dc.date.issued2012en
dc.identifier.urihttp://hdl.handle.net/2122/8097en
dc.description.abstractIn this work we propose a high performance parallelization of the software package COMPSYN, devoted to the production of syntethic seismograms, on a cluster of multicore processors with multiple GPUs. To design and implement the proposed high performance version, we started from a na¨ıve parallel version of COMPSYN. The na¨ıve version consists in a simple parallelization on both device side, obtained by exploiting CUDA, and host side, obtained by exploiting the MPI paradigm and OpenMP API. The proposed high performance version implements several practical techniques of CUDA programming and deeply exploits the GPU architecture, thus achieving a much better performance with respect to the na¨ıve version. We compare the performance of the proposed high performance version and that of the na¨ıve one with the performance of the version running on the cluster of multicore processors without invoking the GPUs. We obtain for the high performance GPU version a speedup of 25x over the version running on the cluster of multicore processors without GPUs against the 10x of the na¨ıve version. Regarding the sequential version, we estimate about 380x the speedup of the high performance GPU version against the about 140x of the na¨ıve version.en
dc.description.sponsorshipCollaboration Agreement between Dept. of Computer Science, Sapienza University of Rome and Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy, 2011. Project n. C26G074ABJ, 2007, Cluster of multicore processor for advanced computation, Sapienza University of Rome.en
dc.language.isoEnglishen
dc.publisher.nameElsevier Science Limiteden
dc.relation.ispartofProcedia Computer Scienceen
dc.relation.ispartofseries/9 (2012)en
dc.subjectGPUen
dc.subjectCUDAen
dc.subjectsynthetic seismogramen
dc.titleHigh Performance Parallelization of COMPSYN on a Cluster of Multicore Processors with GPUsen
dc.typearticleen
dc.description.statusPublisheden
dc.type.QualityControlPeer-revieweden
dc.description.pagenumber966-975en
dc.identifier.URLhttp://www.sciencedirect.com/science/article/pii/S1877050912002244en
dc.subject.INGV04. Solid Earth::04.06. Seismology::04.06.99. General or miscellaneousen
dc.subject.INGV04. Solid Earth::04.06. Seismology::04.06.03. Earthquake source and dynamicsen
dc.subject.INGV04. Solid Earth::04.06. Seismology::04.06.04. Ground motionen
dc.subject.INGV04. Solid Earth::04.06. Seismology::04.06.11. Seismic risken
dc.identifier.doi10.1016/j.procs.2012.04.103en
dc.relation.references[1] P. Spudich, L. Xu, Documentation of software package COMPSYN sxv3.11: programs for earthquake ground motion calculation using complete 1-D Greens functions, International Handbook of Earthquake and Engineering Seismology, 2002. [2] F. Alessi, A. Massini, R. Basili, Accelerating the production of synthetic seismograms by a multicore processor cluster with multiple GPUs, in: PDP, 2012. [3] The Top 500 Supercomputer Sites, http://www.top500.org/ (2011). [4] R. Abdelkhalek, ´ Evaluation des acc´el´erateurs de calcul GPGPU pour la mod´elisation sismique, master thesis, ENSEIRB, Bordeaux, France (2007). [5] P. Micikevicius, 3D finite difference computation on GPUs using CUDA, in: Workshop on General Purpose Processing on Graphics Processing Units, ACM, 2009. [6] R. Abdelkhalek, H. Calandra, O. Coulaud, J. Roman, G. Latu, Fast seismic modeling and Reverse Time Migration on a GPU cluster, in: High Performance Computing & Simulation, IEEE, 2009. [7] D. Mich´ea, D. Komatitsch, Accelerating a three-dimensional finite-difference wave propagation code using gpu graphics cards, Geophys. J. Int. 182 (2010) 380–402. [8] D. G¨oddeke, R. Strzodka, J. Mohd-Yusof, P. McCormick, S. H. M. Buijssen, M. Grajewski, S. Turek, Exploring weak scalability for FEM calculations on a GPU–enhanced cluster, Parallel Computing 33 (10–11) (2007) 685–699. [9] M. Geveler, D. Ribbrock, D. G¨oddeke, P. Zajac, S. Turek, Efficient finite element geometric multigrid solvers for unstructured grids on GPUs, in: PARENG, 2011. [10] D. Komatitsch, D. Mich´ea, G. Erlebacher, Porting a high-order finite-element earthquake modeling application to NVIDIA graphics cards using CUDA, J. Parallel Distrib. Comput. 69 (2009) 451–460. [11] D. Komatitsch, D. G¨oddeke, G. Erlebacher, D. Mich´ea, Modeling the propagation of elastic waves using spectral elements on a cluster of 192 GPUs, Computer Science - Research and Development 25 (1–2) (2010) 75–82. [12] P. Spudich, R. Archuleta, Techniques for earthquake ground-motion calculation with applications to source parameterization of finite faults, Seismic Strong Motion Synthetics, B. A. Bolt, 1987, pp. 205–265. [13] A. Olson, J. Orcutt, G. Frazier, The discrete wavenumber/finite element method for synthetic seismograms, Geophys. J. Int. 77 (1984) 421– 460. [14] NVIDIA CUDA C Best practices guide, vers. 3.2 (2010). [15] The OpenMP API specification for parallel programming, http://openmp.org. [16] L. Smith, Mixed Mode MPI/OpenMP Programming, Edinburgh Parallel Computing Centre, 2000. [17] MPI: A Message-Passing Interface standard, http://www.mpi.org. [18] CPTI Working Group, Catalogo parametrico dei terremoti italiani, version 2004 (CPTI04), http://emidius.mi.ingv.it/CPTI04/, INGV, Bologna (2004).en
dc.description.obiettivoSpecifico4.1. Metodologie sismologiche per l'ingegneria sismicaen
dc.description.obiettivoSpecifico4.2. TTC - Modelli per la stima della pericolosità sismica a scala nazionaleen
dc.description.journalTypeN/A or not JCRen
dc.description.fulltextrestricteden
dc.relation.issn1877-0509en
dc.contributor.authorAlessi, F.en
dc.contributor.authorMassini, A.en
dc.contributor.authorBasili, R.en
dc.contributor.departmentDepartment of Computer Science, Sapienza University of Rome, Italyen
dc.contributor.departmentDepartment of Computer Science, Sapienza University of Rome, Italyen
dc.contributor.departmentIstituto Nazionale di Geofisica e Vulcanologia, Sezione Roma1, Roma, Italiaen
item.openairetypearticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.grantfulltextrestricted-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
crisitem.author.deptDepartment of Computer Science, Sapienza University of Rome, Italy-
crisitem.author.deptDepartment of Computer Science, Sapienza University of Rome, Italy-
crisitem.author.deptIstituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Roma1, Roma, Italia-
crisitem.author.orcid0000-0002-1213-0828-
crisitem.author.parentorgIstituto Nazionale di Geofisica e Vulcanologia-
crisitem.classification.parent04. Solid Earth-
crisitem.classification.parent04. Solid Earth-
crisitem.classification.parent04. Solid Earth-
crisitem.classification.parent04. Solid Earth-
crisitem.department.parentorgIstituto Nazionale di Geofisica e Vulcanologia-
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