Many scientific, technical and engineering applications in
finance, medical imaging, modeling, simulation, and image
processing can benefit greatly from the floating point
acceleration offered by modern general purpose graphics
processing units (GPGPU). Today’s graphics processors have
evolved into sophisticated, massively-parallel, highly-
programmable compute engines ideally suited for algorithms with
a high degree of data parallelism. Combined with modern parallel
programming languages and application interfaces such as
industry standard OpenCL™, GPGPU offers a new paradigm for high-
performance computing (HPC).In this article we plan to examine
the use of GPGPU to accelerate iterative, grid-based, finite-
difference methods used extensively in HPC applications. Finite-
Difference Time-Domain (FDTD) solvers are used to model
electromagnetic wave propagation. Velocity-stress finite-
difference time-domain (VS-FDTD) solvers are used to model
acoustic wave propagation in seismic applications. Both
algorithms are p