CUDA based Particle Swarm Optimization for geophysical inversion

Many geophysical problems are computationally expensive owing to their iterative nature or due to the programs processing to large datasets. Such problems are challenging and have to be approached with extreme caution because a wrong parameter selection will not only lead to wrong results but will also take up a lot of time. The Compute Unified Device Architecture (CUDA) introduced by NVIDIA has enabled programmers to execute tasks in parallel on a Graphics Processing Unit (GPU) using a high level language like C and C++. GPU´s are massively parallel architectures with computing output several MFLOPS (10⁶ Floating Point Operations per second) higher than Central Processing Unit. They posses high memory bandwidth and low memory latency which makes it ideally suited for parallel computation. There are a number of geophysical processes which can benefit from reduced computing time. Iterative optimization procedures are one of them. We have implemented a CUDA version of the Particle Swarm Optimization (PSO) algorithm and used it to invert Self Potential, Magnetic and Resistivity data. The CUDA version of the algorithm was compared to an efficient CPU implementation of the same. We observed significant speed up compared to a CPU only version and the results of the CUDA version were as good as the CPU version.