Accelerating a three-dimensional finite-difference wave propagation code using GPU graphics cards

Abstract

S U M M A R YWe accelerate a 3-D finite-difference in the time domain wave propagation code by a factor between about 20 and 60 compared to a serial implementation using graphics processing unit computing on NVIDIA graphics cards with the CUDA programming language. We describe the implementation of the code in CUDA to simulate the propagation of seismic waves in a heterogeneous elastic medium. We also implement convolution perfectly matched layers on the graphics cards to efficiently absorb outgoing waves on the fictitious edges of the grid. We show that the code that runs on a graphics card gives the expected results by comparing our results to those obtained by running the same simulation on a classical processor core. The methodology that we present can be used for Maxwell's equations as well because their form is similar to that of the seismic wave equation written in velocity vector and stress tensor.

Elastic surface waves in crystals – Part 2: Cross-check of two full-wave numerical modeling methods

“…Another important property of the SEM is the fact that it can be parallelized efficiently to take advantage of the distributed structure of modern supercomputers [33], and in particular on clusters of Graphics Processing Units (GPU) graphics cards [34][35][36], reaching speedup factors of more than an order of magnitude compared to a reference serial implementation on a CPU core; this makes it compare well in terms of performance to less flexible algorithms such as finite differences in the time domain (FDTD), which can also be implemented efficiently on GPUs [37,38].…”

Numerical modeling of 2D seismic wave propagation in fluid saturated porous media using graphics processing unit (GPU): Study case of realistic simple structural hydrocarbon trap

“…A GPU is rarely on a vast number of cores with slower execution speed. However, if all cores capable to perform a calculation simultaneously, the usage of a high number of cores will be an advantage [14]. We use GTX TITAN Black GPU with CUDA technology as computing device.…”

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“…For example, for a moderate number of three-component seismograms, 0.8 million and 2.3 million central processing unit hours were used to generate the tomographic models of the southern California crust and the European upper mantle, respectively Zhu et al, 2012). The severity of the cost issue may be remedied when simulations are ported to the graphic processing unit (GPU) hardware (e.g., Komatitsch et al, 2010;Michéa and Komatitsch, 2010). However, ray-based tomographic methods remains the most popular and accessible techniques for mapping the heterogeneous structures of the Earth's interior (e.g., Li et al, 2008;Hung et al, 2011;Tong et al, 2012;Zhao et al, 2012).…”