Hardware Acceleration for CGP: Graphics Processing Units
نویسندگان
چکیده
Graphic Processing Units (GPUs) are fast, highly parallel units. In addition to processing 3D graphics, modern GPUs can be programmed for more general-purpose computation. A GPU consists of a large number of ‘shader processors’, and conceptually operates as a single instruction multiple data (SIMD) or multiple instruction multiple data (MIMD) stream processor. A modern GPU can have several hundred of these stream processors, which, combined with their relatively low cost, makes them an attractive platform for scientific computing. In the last two years, the genetic programming community has begun to exploit GPUs to accelerate the evaluation of individuals in a population [1, 4]. CGP was the first GP technique implemented in a general-purpose fashion on GPUs. By ‘general purpose’, we mean a technique that can be applied to a number of GP applications and not just a single, specialized task. Implementing CGP on GPUs has resulted in very significant performance increases. In this chapter, we discuss several of our implementations of CGP on GPUs. To begin with, we start with an overview of the hardware and software used, before discussing applications and the speed-ups obtained.
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