Abstract
This chapter presents several solutions to accelerate the SAX/GA algorithm using a GPU with each solution aiming at maximizing the parallel potential of SAX/GA. The first implementation, Solution A, focuses in the bottleneck identified previously, the SAX transformation, while Solution B and C try to explore a new method to optimize the populations of individuals and take further advantage of the GPU architecture to the benefit of SAX/GA.
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Reference
M. Boyer. Memory transfer overhead, http://www.cs.virginia.edu/~mwb7w/cuda_support/memory_transfer_overhead.html
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Baúto, J., Neves, R., Horta, N. (2018). GPU-Accelerated SAX/GA. In: Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-73329-6_5
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DOI: https://doi.org/10.1007/978-3-319-73329-6_5
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73328-9
Online ISBN: 978-3-319-73329-6
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