GPU computing with OpenCL to model 2D elastic wave propagation: exploring memory usage

Graphics processing units (GPUs) have become increasingly powerful in recent years. Programs exploring the advantages of this architecture could achieve large performance gains and this is the aim of new initiatives in high performance computing. The objective of this work is to develop an efficient tool to model 2D elastic wave propagation on parallel computing devices. To this end, we implement the elastodynamic finite integration technique, using the industry open standard open computing language (OpenCL) for cross-platform, parallel programming of modern processors, and an open-source toolkit called [Py]OpenCL. The code written with [Py]OpenCL can run on a wide variety of platforms; it can be used on AMD or NVIDIA GPUs as well as classical multicore CPUs, adapting to the underlying architecture. Our main contribution is its implementation with local and global memory and the performance analysis using five different computing devices (including Kepler, one of the fastest and most efficient high performance computing technologies) with various operating systems.