Details of the abstract

Title of paper Hybrid GPU solution to regularized divergence-free curl-curl equations for electromagnetic inversion problems
List of authors Dong, H., Sun, K., Egbert, G., Kelbert, A., Meqbel, N.
Affiliation(s) China University of Geosciences (Beijing), Nvidia Corporation, Oregon State University, United States Geological Survey, 3D Consulting-GEO GmbH.
Summary The Curl-Curl equation is the key to the time-harmonic electromagnetic (EM) problems in geophysics. The efficiency of its solution is decisive to the performance of EM simulations, which account for over 90% of the computation cost in inversions like Magnetotellurics or controlled source EM problems. However, most published EM computation codes are still CPU-based and cannot utilize the recent computation techniques with GPUs. Based on the 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 computational ability of GPUs, the study proposes a hybrid CPU-GPU framework to solve the forward and adjoint problems of the EM inversions. The large sparse linear systems arise from the staggered-grid finite difference approximation of curl-curl problems are solved with a new mixed-precision Krylov subspace solver.
The algorithm is implemented with the ModEM modular inversion package and with both synthetic and real-world magnetotelluric examples. The results show a promising 15-30x speed-up for the solution stage of the curl-curl equations over single-CPU calculations. On real-world inversion test cases, the overall performance of a GPU-attached computation node with the new hybrid framework is comparable to that of four CPU-only nodes with conventional ModEM implementation. This would make the large-scale frequency domain EM inversions possible on smaller modern GPU platforms with reduced carbon footprint.
Session Keyword 2.0 Theory, Modelling and Inversion
File upload 2.0_hybrid_gpu_solution_to_re_dong_01.pdf
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