UMBC Center for Interdisciplinary Research and Consulting (CIRC)http://hdl.handle.net/11603/114042019-06-19T00:46:28Z2019-06-19T00:46:28ZIntroduction to Distributed Computing with pbdR at the UMBC High Performance Computing FacilityRaim, Andrew M.http://hdl.handle.net/11603/115622018-10-16T07:02:30Z2013-06-26T00:00:00ZIntroduction to Distributed Computing with pbdR at the UMBC High Performance Computing Facility
Raim, Andrew M.
2013-06-26T00:00:00ZAn Implementation of Binomial Method of Option Pricing using Parallel ComputingPopuri, Sai K.Raim, Andrew M.Neerchal, Nagaraj K.Gobbert, Matthias K.http://hdl.handle.net/11603/115362018-10-16T07:02:30ZAn Implementation of Binomial Method of Option Pricing using Parallel Computing
Popuri, Sai K.; Raim, Andrew M.; Neerchal, Nagaraj K.; Gobbert, Matthias K.
The Binomial method of option pricing is based on iterating over discounted option payoffs in a recursive fashion to calculate the present value of an option. Implementing the Binomial method to exploit the resources of a parallel computing cluster is non-trivial as the method is not easily parallelizable. We propose a procedure to transform the method into an “embarrassingly parallel” problem by mapping Binomial probabilities to Bernoulli paths. We have used the parallel computing capabilities in R with the Rmpi package to implement the methodology on the cluster tara in the UMBC High Performance Computing Facility, which has 82 compute nodes with two quad-core Intel Nehalem processors and 24 GB of memory on a quad-data rate InfiniBand interconnect. With high-performance clusters and multi-core desktops becoming increasingly accessible, we believe that our method will have practical appeal to financial trading firms.