A framework for automating the configuration of OpenCL

Environmental research and scientific simulations use information acquired by sensors to validate the modeling and representation of environmental behaviors. The computational processing cost of this context tends to be extremely high due to the amount of information and the model's calculation complexities which demand the use of computational parallel solutions. This paper presents JSeriesCL, a framework for parallel processing of spatiotemporal series using graphics processors (GPGPU), more specifically OpenCL. GPU is cheaper than other solutions for parallel processing, such as clusters or grid, and JSeriesCL changes the way that GPU are used because it automates the configuration and management aspects of such devices. Fractal dimension and SEBS were used to validate the application of JSeriesCL over environmental data.