CPU-GPU Hybrid Parallel Binomial American Option Pricing

Nan Zhang, Eng Gee Lim, Ka Lok Man, Chi-Un Lei
Department of Computer Science and Software, Engineering, Xi’an Jiaotong-Liverpool University (XJTLU), China
International MultiConference of Engineers and Computer Scientists, Vol II, IMECS 2012, March 14-16, 2012


   title={CPU-GPU Hybrid Parallel Binomial American Option Pricing},

   author={Zhang, N. and Lim, E.G. and Man, K.L. and Lei, C.U.},

   journal={Proceedings of the International MultiConference of Engineers and Computer Scientists},




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We present in this paper a novel parallel binomial algorithm that computes the price of an American option. The algorithm partitions a binomial tree constructed for the pricing into blocks of multiple levels of nodes, and assigns each such block to multiple processors. Each of the processors then computes the option’s values at its assigned nodes in two phases. The algorithm is implemented and tested on a heterogeneous system consisting of an Intel multi-core processor and a NVIDIA GPU. The whole task is split and divided over and the CPU and GPU so that the computations are performed on the two processors simultaneously. In the hybrid processing, the GPU is always assigned the last part of a block, and makes use of a couple of buffers in the on-chip shared memory to reduce the number of accesses to the off-chip device memory. The performance of the hybrid processing is compared with an optimised CPU serial code, a CPU parallel implementation and a GPU standalone program.
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