Abstract
The tremendous power of graphics processing unit (GPU) computing relative to prior CPU-only architectures presents new opportunities for efficient solutions of previously intractable large-scale optimization problems. Although most previous work in this field focused on scientific applications in the areas of medicine and physics, here we present a Compute Unified Device Architecture-based (CUDA) GPU solution to solve the topology control problem in hybrid radio frequency and free space optics wireless mesh networks by adapting and adjusting the transmission power and the beam-width of individual nodes according to QoS requirements. Our approach is based on a stochastic global optimization technique inspired by the social behavior of flocking birds so-called particle swarm optimization' and was implemented on the NVIDIA GeForce GTX 285 GPU. The implementation achieved a performance speedup factor of 392 over a CPU-only implementation. Several innovations in the memory/execution structure in our approach enabled us to surpass all prior known particle swarm optimization GPU implementations. Our results provide a promising indication of the viability of GPU-based approaches towards the solution of large-scale optimization problems such as those found in radio frequency and free space optics wireless mesh network design. Copyright (c) 2011 John Wiley & Sons, Ltd.