Understanding the efficiency of GPU algorithms for matrix-matrix multiplication
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Efficient gather and scatter operations on graphics processors
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Communications of the ACM
Accelerating advanced MRI reconstructions on GPUs
Journal of Parallel and Distributed Computing
Benchmarking GPUs to tune dense linear algebra
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
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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.