Kennedy, J. ACM Google Scholar. Mussi, L. Nashed, Y. Poli, R. Storn, R. Suganthan, P.
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Natural Computing, 1—50 Google Scholar. Vesterstrom, J. Wets, F. Zhou, Y. Additionally, some algorithms can also be used as a starting point for further research. Help Centre.
GPU-based Parallel Implementation of Swarm Intelligence Algorithms
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Presents a concise but sufficient introduction to general-purpose GPU computing which can help the layman become familiar with this emerging computing technique Describes implementation details, such as parallel models and performance metrics, so readers can easily utilize the techniques to accelerate their algorithmic programs Appeals to readers from the domain of high performance computing HPC who will find the relatively young research domain of swarm intelligence very interesting Includes many real-world applications, which can be of great help in deciding whether or not swarm intelligence algorithms or GPGPU is appropriate for the task at hand.
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Have an account? Swarm intelligence algorithms have been widely used to solve difficult real world problems in both academic and engineering domains. Thanks to the inherent parallelism, various parallelized swarm intelligence algorithms have been proposed to speed up the optimization process, especially on the massively parallel processing architecture GPUs.
tim.dev3.develag.com/suzuki-sidekick-reparacin-de-manual.php However, conventional swarm intelligence algorithms are usually not designed specifically for the GPU architecture. They either can not fully exploit the tremendous computational power of GPUs or can not extend effectively as the problem scales go large. Rate this item: 1.
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