石油物探2018，Vol.57Issue(3)：470-477,8.DOI:10.3969/j.issn.1000-1441.2018.03.017
MT Occam反演的CPU/GPU异构混合并行算法研究
Hybrid parallelism for MT Occam inversion on CPU/GPU heterogeneous clusters
摘要
Abstract
Owing to the limited observation frequencies,the conventional parallelization of electromagnetic forward modeling and inversion based on frequency division does not have extensible parallelism;even increasing the cluster nodes still makes it difficult to improve the officiency.This work is devoted to the development of an efficient algorithm for magnetotelluric (MT) Occam inversion on small GPU clusters.An MPI-OpenMP-CUDA multi-level hybrid parallel scheme has been designed,which extends the conventional coarse-grained decomposition by excavating the fine-grained parallelism components of linear algebraic computations and matrix operations.On the first level,the coarse-grained tasks over multiple compute nodes are distributed by MPI.On the second level,the intra-node medium-grained parallelization process is realized by OpenMP.Finally,the kernel computations on the GPUs are performed by using CUDA.The theoretical background,parallelism analysis,workflow design,and applicability discussions are presented in the different sections of this article.The scheme was tested on several synthetic models to validate the correctness of our code and to illustrate the precision evaluation and performance comparison.Results of the experiments on synthetic models showed that the performance for large-scale model (type 2 in this article) inversion was satisfactory,achieving an average speed of up to 16 times and a maximum speed of up to 23 times with only four nodes.关键词
大地电磁/反演/异构/并行计算/MPI-OpenMP-CUDAKey words
magnetotelluric (MT)/inversion/heterogeneous/parallel computing/MPI-OpenMP-CUDA分类
天文与地球科学引用本文复制引用
刘羽,熊壬浩,肖熠..MT Occam反演的CPU/GPU异构混合并行算法研究[J].石油物探,2018,57(3):470-477,8.基金项目
国家自然科学基金(41264005,41374079)联合资助.This research is financially supported by the National Natural Science Foundation of China (Grant Nos.41264005,41374079). （41264005,41374079）
