Speeding up the BioHEL evolutionary learning system using GPGPUs
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چکیده
The BioHEL system is an evolutionary learning system designed to cope with large-scale datasets. This system have several characteristics focused on tackling this kind of problems, such as special representation to determine the relevant attributes in a rule, the usage of a windowing system, among others. Recently, we have extended the system to perform the rule evaluation process inside NVIDIA GPGPUs using CUDA (Compute Unified Device Architecture)[3]. This improvement speeded up the whole learning process up to 58.1X. Moreover, the CUDA-based evaluation was successfully combined with the rest of efficiency enhancement mechanisms within BioHEL. The total speedup using theses techniques was cumulative, obtaining a maximum combined speedup of 765.3X. The following sections will explain how the BioHEL system works and how the CUDA-based fitness function was implemented and incorporated within BioHEL.
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تاریخ انتشار 2010