GPUaccelerated rectangular decomposition for sound propagation modeling in 2D Adaptive Rectangular Decomposition (ARD) is an efficient technique for modeling the sound propagation. This technique reduces the computational overhead and memory requirements by using nondispersive subrectangular domains suitable for parallel computation. In order to use this technique, the scene should be decomposed into large inscribed rectangles. For large and complex scenes, it is not feasible to find an optimal set of large rectangles and thus an approximation should be used. In this paper, we present a GPUaccelerated algorithm for searching an adequate rectangular decomposition of a 2D scene in a reasonable time. Our algorithm performs a fast parallel search in the entire domain. As a result, large and complex scenes can be decomposed in seconds and it enables them to be numerically modeled in parallel using ARD.

GPU Support for Automatic Generation of FiniteDifferences Stencil Kernels The growth of data to be processed in the Oil u0026 Gas industry matches the requirements imposed by evolving algorithms based on stencil computations, such as Full Waveform Inversion and Reverse Time Migration. Graphical processing units (GPUs) are an attractive architectural target for stencil computations because of its high degree of data parallelism. However, the rapid architectural and technological progression makes it difficult for even the most proficient programmers to remain uptodate with the technological advances at a microarchitectural level. In this work, we present an extension for an open source compiler designed to produce highly optimized finite difference kernels for use in inversion methods named Devitoxc2xa9. We embed it with the Oxford Parallel Domain Specific Language (OPDSL) in order to enable automatic code generation for GPU architectures from a highlevel representation. We aim to enable users coding in a symbolic representation level to effortlessly get their implementations leveraged by the processing capacities of GPU architectures. The implemented backend is evaluated on a NVIDIAxc2xae GTX Titan Z, and on a NVIDIAxc2xae Tesla V100 in terms of operational intensity through the roofline model for varying spaceorder discretization levels of 3D acoustic isotropic wave propagation stencil kernels with and without symbolic optimizations. It achieves approximately 63% of V100xe2x80x99s peak performance and 24% of Titan Zxe2x80x99s peak performance for stencil kernels over grids with 2563 points. Our study reveals that improving memory usage should be the most efficient strategy for leveraging the performance of the implemented solution on the evaluated architectures.