RESUMO
Nowadays, with the increase of computational analysis in sciences such as biology and neuroscience, the computational aspect is one of the most challenging. The purpose of this work is the achieve the possibility to apply spatio-temporal networks inference techniques on brain to perform network analysis. One of the problems of spatio-temporal network applications is the computational time, and it becomes impractical to keep developing studies when it takes a long time to analyze and compute the results. We present a GPU-based system used to speed up the computation of spatio-temporal networks applied to different brain data; thanks to the architecture of these devices we are able to obtain an average increase in the performances of ~ 35× on a single GPU and ~ 78× on multi GPU with the respect of CPU execution.
