解算器
计算机科学
计算科学
中央处理器
Curl(编程语言)
并行计算
反演(地质)
超级计算机
计算电磁学
图形处理单元
算法
应用数学
数学优化
数学
电磁场
物理
地质学
古生物学
构造盆地
量子力学
程序设计语言
操作系统
作者
Hui Dong,Kaiqiong Sun,G. D. Egbert,Anna Kelbert,Naser Meqbel
标识
DOI:10.1016/j.cageo.2024.105518
摘要
The Curl-Curl equation is the foundation of time-harmonic electromagnetic (EM) problems in geophysics. The efficiency of its solution is key to EM simulations, accounting for over 95% of the computation cost in geophysical inversions for magnetotelluric or controlled-source EM problems. However, most published EM inversion codes are still central processing unit (CPU)-based and cannot utilize recent computational developments on the graphic processing units (GPUs). Based on a previously proposed divergence-free algorithm developed on CPUs, this study demonstrates the current limits of the CPU-based inversion procedure. To exploit the high throughput capability of GPUs, we propose a hybrid CPU-GPU framework to solve forward and adjoint problems required for EM inversions. The large sparse linear systems arising from the staggered-grid finite difference approximation of the Curl-Curl equation is solved with a mixed-precision Krylov subspace solver implemented on a GPU. The algorithm is then tested in EM forward and adjoint calculations, with real-world 3D numerical examples. Test results show promising 30x kernel-level speed-ups over the conventional CPU algorithm. This approach may further take the complex frequency domain EM inversions onto the next, practical stage on small affordable GPU platforms.
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