GPU parallelization strategies for metaheuristics: a survey

Metaheuristics have been showing interesting results in solving hard optimization problems. However, they become limited in terms of effectiveness and runtime for high dimensional problems. Thanks to the independency of metaheuristics components, parallel computing appears as an attractive choice to reduce the execution time and to improve solution quality. By exploiting the increasing performance and programability of graphics processing units (GPUs) to this aim, GPU-based parallel metaheuristics have been implemented using different designs. RecentresultsinthisareashowthatGPUstendtobeeffectiveco-processors forleveraging complex optimization problems.In thissurvey, mechanisms involvedinGPUprogrammingforimplementingparallelmetaheuristicsare presentedanddiscussedthroughastudyofrelevantresearchpapers. Metaheuristics can obtain satisfying results when solving optimization problems in a reasonable time. However, they suffer from the lack of scalability. Metaheuristics become limited ahead complex highdimensional optimization problems. To overcome this limitation, GPU based parallel computing appears as a strong alternative. Thanks to GPUs, parallelmetaheuristicsachievedbetterresultsintermsofcomputation,and evensolutionquality

Parallel Preprocessing for the Optimal Camera Placement Problem

This paper deals with the preprocessing needed for the optimal camera placement problem, which is stated as a unicost set covering problem (USCP). Distributed and massively parallel computations with graphics processing unit (GPU) are proposed in order to perform the reduction and visibility preprocessing respectively. An experimental study reports that a significant speedup can be achieved, and we give a general heterogeneous parallel approach that brings together these parallel computations. In addition to that, a set-based differential evolution (DE) method is applied to solve 10 instances of the considered problem, and promising results are reported