A study of graphics hardware accelerated particle swarm optimization with digital pheromones

Programmable Graphics Processing Units (GPUs) have lately become a promising means to perform scientific computations. Modern GPUs have proven to outperform the number of floating point operations when compared to traditional Central Processing Units (CPUs) through inherent data parallel architecture and higher bandwidth capabilities. They allow scientific computations to be performed without noticeable degradation in accuracy in a fraction of the time compared to traditional CPUs at substantially reduced costs, making them viable alternatives to expensive computer clusters or workstations. GPU programmability however, has fostered the development of a variety of programming languages making it challenging to select a computing language and use it consistently without the pitfall of being obsolete. Some GPU languages are hardware specific and are designed to rake in performance boosts when used with their host GPUs (e.g., Nvidia Cuda). Others are operating system specific (e.g., Microsoft HLSL). A few are platform agnostic lending themselves to be used on a workstation with any CPU and a GPU (e.g., GLSL, OpenCL).