The Particle Swarm Optimization (PSO) algorithm is a well known alternative for global optimization based on a bio‐inspired heuristic. PSO has good performance, low computational complexity and few parameters. Heuristic techniques have been widely studied in the last twenty years and the scientific community is still interested in technological alternatives that accelerate these algorithms in order to apply them to bigger and more complex problems. This article presents an empirical study of some parallel variants for a PSO algorithm, implemented on a Graphic Process Unit (GPU) device with multi‐thread support and using the most recent model of parallel programming for these cases. The main idea is to show that, with the help of a multithreading GPU, it is possible to significantly improve the PSO algorithm performance by means of a simple and almost straightforward parallel programming, getting the computing power of cluster in a conventional personal computer.
A parallel iterative Finite Difference (FD) method for solving Poisson's equation on CUDA is implemented. The aim of this paper is to give a detail explanation about the parallel solution of a Partial Differential Equation (PDE). To examine the performance of the implemented iterative algorithm, a number of experiments were tested. The performance shows the benefit of using the implemented approach on GPU devices in terms of execution time.
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