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{

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   "name": "Hybrid CPU-GPU solution to regularized divergence-free curl-curl equations for electromagnetic inversion problems",
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       "value": "10.1016/j.cageo.2024.105518",
       "url": "https://doi.org/10.1016/j.cageo.2024.105518"
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   "journal": {
     "@type": "Periodical",
     "name": "Computers and Geosciences",
     "volumeNumber": "184",
     "issueNumber": null
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   "inLanguage": "en",
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   "datePublished": "2024",
   "dateModified": "2024-01-29",
   "abstract": "The Curl-Curl equation is the foundation of time-harmonic electromagnetic (EM) problems in\u00a0geophysics. The efficiency of its solution is key to\u00a0EM simulations, accounting for over 95% of the computation cost in geophysical inversions for\u00a0magnetotelluric\u00a0or controlled-source EM problems. However, most published EM inversion codes are still\u00a0central processing unit\u00a0(CPU)-based and cannot utilize recent computational developments on the\u00a0graphic processing units\u00a0(GPUs). Based on a previously proposed divergence-free algorithm developed on CPUs, this study demonstrates the current limits of the CPU-based\u00a0inversion procedure. To exploit the\u00a0high throughput\u00a0capability of GPUs, we propose a hybrid CPU-GPU framework to solve forward and\u00a0adjoint\u00a0problems required for EM inversions. The large sparse\u00a0linear systems\u00a0arising from the staggered-grid finite difference approximation of the Curl-Curl equation are solved with a mixed-precision\u00a0Krylov subspace\u00a0solver 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\u00d7 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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