Abstract
The architecture of the latest Graphic Processing Unit (GPU) has surpassed the previous application-specific stream architecture. They consist of a number of uniform programmable units integrated on the same chip which facilitate the general-purpose computing beyond the graphic processing. With the multiple programmable units executing in parallel, the latest GPU shows superior performance for many different applications. Furthermore, programmers can have a direct control on the GPU pipeline using easy-to-use parallel programming environments, whereas they had to rely on specific graphics API's in the past. These advances in hardware and software make General-Purpose GPU (GPGPU) computing widespread. In this paper, using the latest GPU and its software environment, we parallelize a computationally demanding financial application based on Monte-Carlo methods and optimize its performance. Experimental results show that a GPU can achieve a superior performance, greater than 190x, compared with the CPU-only case.
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