Kai Sun
Gary D. Egbert
Anna Kelbert
Naser Meqbel
Hao Dong
2024
<div id="abstracts" class="Abstracts u-font-serif text-s"><div id="abs0010" class="abstract author" lang="en"><div id="abssec0010"><p id="abspara0010"><span>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 </span>magnetotelluric<span> 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 are 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 three-dimensional numerical examples. Test results show promising 30× 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.</span></p></div></div></div>
application/pdf
10.1016/j.cageo.2024.105518
en
Elsevier
Hybrid CPU-GPU solution to regularized divergence-free curl-curl equations for electromagnetic inversion problems
article