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We 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. [ABSTRACT FROM AUTHOR]

Abstract: We port a high-order finite-element application that performs the numerical simulation of seismic wave propagation resulting from earthquakes in the Earth on NVIDIA GeForce 8800 GTX and GTX 280 graphics cards using CUDA. This application runs in single precision and is therefore a good candidate for implementation on current GPU hardware, which either does not support double precision or supports it but at the cost of reduced performance. We discuss and compare two implementations of the code: one that has maximum efficiency but is limited to the memory size of the card, and one that can handle larger problems but that is less efficient. We use a coloring scheme to handle efficiently summation operations over nodes on a topology with variable valence. We perform several numerical tests and performance measurements and show that in the best case we obtain a speedup of 25. [Copyright &y& Elsevier]

Abstract: We implement a high-order finite-element application, which performs the numerical simulation of seismic wave propagation resulting for instance from earthquakes at the scale of a continent or from active seismic acquisition experiments in the oil industry, on a large cluster of NVIDIA Tesla graphics cards using the CUDA programming environment and non-blocking message passing based on MPI. Contrary to many finite-element implementations, ours is implemented successfully in single precision, maximizing the performance of current generation GPUs. We discuss the implementation and optimization of the code and compare it to an existing very optimized implementation in C language and MPI on a classical cluster of CPU nodes. We use mesh coloring to efficiently handle summation operations over degrees of freedom on an unstructured mesh, and non-blocking MPI messages in order to overlap the communications across the network and the data transfer to and from the device via PCIe with calculations on the GPU. We perform a number of numerical tests to validate the single-precision CUDA and MPI implementation and assess its accuracy. We then analyze performance measurements and depending on how the problem is mapped to the reference CPU cluster, we obtain a speedup of 20x or 12x. [ABSTRACT FROM AUTHOR]