high performance computing on graphics processing units: hgpu.org

Fish School Search (FSS) is a computational intelligence technique invented by Bastos-Filho and Lima-Neto in 2007 and first presented in Bastos-Filho et al. (2008). FSS was conceived to solve search problems and it is based on the social behavior of schools of fish. In the FSS algorithm, the search space is bounded and each possible position in the search space represents a possible solution for the problem. During the algorithm execution, each fish has its positions and weights adjusted according to four FSS operators, namely, feeding, individual movement, collective-instinctive movement and collective-volitive movement. FSS is inherently parallel since the fitness can be evaluated for each fish individually. Hence, it is quite suitable for parallel implementations. In the recent years, the use of Graphic Processing Units (GPUs) have been proposed for various general purpose computing applications. Thus, GPU-based platforms afford great advantages on applications requiring intensive parallel computing. The GPU parallel floating point processing capacity allows one to obtain high speedups. These advantages together with FSS architecture suggest that GPU based FSS may produce marked reduction in execution time, which is very likely because the fitness evaluation and the update processes of the fish can be parallelized in different threads. Nevertheless, there are some aspects that should be considered to adapt an application to be executed in these platforms, such as memory allocation and communication between blocks.