NPAC Technical Report SCCS-273b
Software Issues and Performance of a Parallel Model for Stock Option Pricing
Kim Mills, Gang Cheng, Michael Vinson, Sanjay Ranka, Geoffry Fox
Submitted December 07 1992
Abstract
The finance industry is beginning to adopt parallel computing for numerical
computation, and will soon be in a position to use parallel supercomputers.
This paper examines software issues and performance of a stock option pricing
model running on the Connection Machine-2 and DECmpp-12000. Pricing models
incorporating stochastic volatility with American call (early exercise) are
computationally intensive and require substantial communication. Three
parallel versions of a stock option pricing model were developed which varied
in data distribution, load balancing, and communication. The performance of
this set of increasingly refined models ranged over no improvement, 10 times,
and 100 times faster than a sequential model. A straightforward approach to
this problem involves use of two-dimensional dynamic arrays. When asymmetric
arrays are mapped on the DECmpp-12000, distribution of data to physical
processors is inefficient and performance suffers. The regular communication
patterns in the model can also be expressed in one-dimensional arrays,
improving data distribution. Performance of this version is similar on both
parallel machines. Combining one-dimensional parallel and sequential arrays
achieves efficient data distribution, reduces interprocessor communication,
and further improves performance (100 times faster than a sequential
workstation model). The performance improvements possible on parallel
supercomputers presents new opportunities for pricing entire portfolios,
performing large scale model and market comparisons, and using optimization
techniques to improve model price estimates.