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Seismic exploration multicore mode Parallel Computing

Author: ZhouGuangYao
Tutor: TianJiDong
School: Southwest Petroleum University
Course: Petroleum Engineering Computing Technology
Keywords: Parallel Computing Seismic exploration OpenMP Multicore
CLC: TP338.6
Type: Master's thesis
Year: 2011
Downloads: 37
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Currently, many areas of engineering calculation process large amounts of data are required . How fast and efficient processing of these data become scientists and the most concern . Parallel processing technology allows fast processing of the generation of large amounts of data is possible. In the field of oil exploration , with the exploration of oil and gas reservoirs now been expanding , making exploration targets facing increasingly complex geological bodies ; while reservoir exploration and development efforts led to the deepening seismic data volume and computation exponentially growth , which are presented in parallel processing technology urgent needs. In the parallel processing, the message passing programming model (MPI) is the main parallel model , the model efficiency is mainly affected by two factors , namely Internet speed and computing power of a single node . Personal computer on the popularity of multi-core CPU provides the CPU on a single compute node computing resources mining space . The main purpose is to increase the computing power of a single node in parallel to improve the efficiency of the program . To improve computing performance of nodes , the paper introduces multi-threaded OpenMP parallel programming . OpenMP is a shared memory parallel programming architecture standards , with the popularity of multi-core processors are widely used in parallel OpenMP parallel programming techniques to become the development direction . Kriging is an important seismic data processing method . This Kriging method for the major part in the MP, OpenMP and MPI OpenMP experiments three modes , and the obtained results were compared MPI OpenMP is a relatively efficient parallel strategy and has good scalability conclusions . Finally, the MPI OpenMP applied to kriging method, experimental results show that : solved in parallel speedup achieved 1.72 .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Electronic digital computer (not a continuous role in computer ) > A variety of electronic digital computer > Parallel computer
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