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A parallel algorithm of kirchhoff pre-stack depth migration based on GPU

  • Yida Wang
  • , Chao Li
  • , Yang Tian
  • , Haihua Yan
  • , Changhai Zhao
  • , Jianlei Zhang
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Kirchhoff pre-stack depth migration (KPSDM) algorithm, as one of the most widely used migration algorithms, plays an important part in getting the real image of the earth. However, this program takes considerable time due to its high computational cost; hence the working efficiency of the oil industry is affected. The general purpose Graphic Processing Unit (GPU) and the Compute Unified Device Architecture (CUDA) developed by NVIDIA have provided a new solution to this problem. In this study, we have proposed a parallel algorithm of the Kirchhoff pre-stack depth migration and an optimization strategy based on the CUDA technology. Our experiments indicate that for large data computations, the accelerated algorithm achieves a speedup of 8~15 times compared with NVIDIA GPU.

Original languageEnglish
Title of host publicationAlgorithms and Architectures for Parallel Processing - 14th International Conference, ICA3PP 2014, Proceedings
PublisherSpringer Verlag
Pages207-218
Number of pages12
EditionPART 2
ISBN (Print)9783319111933
DOIs
StatePublished - 2014
Event14th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2014 - Dalian, China
Duration: 24 Aug 201427 Aug 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8631 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2014
Country/TerritoryChina
CityDalian
Period24/08/1427/08/14

Keywords

  • CUDA
  • GPU
  • Kirchhoff pre-stack depth migration
  • optimization
  • parallel algorithm

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