Massively parallel modeling of electromagnetic field in conductive media: An MPI-CUDA implementation on Multi-GPU computers

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xiaolei Tu, Esteban Jeremy Bowles-Martinez, Adam Schultz
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Abstract

Numerical modeling of electromagnetic (EM) fields in a conductive marine environment is crucial for marine EM data interpretation. During marine controlled-source electromagnetic (MCSEM) surveys, a variety of transmitter locations are used to introduce electric currents. The resulting electric and magnetic fields are then concurrently logged by a network of receivers. The forward simulation of MCSEM data for a subsea structure whose electrical properties vary in all three dimensions is computationally intensive. We demonstrate how such computations may be substantially accelerated by adapting algorithms to operate efficiently on modern GPUs with many core architectures. The algorithm we present features a hybrid MPI-CUDA programming model suitable for multi-GPU computers and consists of three levels of parallelism. We design the optimal kernels for different components to minimize redundant memory accesses. We have tested the algorithm on NVIDIA Kepler architecture and achieved up to 105 × speedup compared with the serial code version. We further showcased the algorithm's performance advantages through its application to a realistic marine model featuring complex geological structures. Our algorithm's significant efficiency increase opens the possibility of 3D MCSEM data interpretation based on probabilistic or machine learning approaches, which require tens of thousands of forward simulations for every survey.

导电介质中电磁场的大规模并行建模:多 GPU 计算机上的 MPI-CUDA 实现
导电海洋环境中电磁(EM)场的数值建模对于海洋电磁数据解释至关重要。在海洋可控源电磁(MCSEM)勘测过程中,会使用各种发射器位置引入电流。由此产生的电场和磁场由接收器网络同时记录。海底结构的电特性在所有三个维度上都会发生变化,对 MCSEM 数据进行正向模拟需要大量计算。我们展示了如何通过调整算法,使其在采用多核架构的现代 GPU 上高效运行,从而大幅加快此类计算速度。我们介绍的算法采用适合多 GPU 计算机的 MPI-CUDA 混合编程模型,包含三个并行级别。我们为不同组件设计了最佳内核,以尽量减少冗余内存访问。我们在英伟达开普勒架构上测试了该算法,与串行代码版本相比,速度提高了 105 倍。我们将该算法应用于一个具有复杂地质结构的现实海洋模型,进一步展示了该算法的性能优势。我们的算法显著提高了效率,为基于概率或机器学习方法的三维 MCSEM 数据解释提供了可能,而这些方法需要对每次勘测进行数以万计的前向模拟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
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