Distributed and Adaptive Parallel Computing for Computational Finance Applications

Abstract

Analysts, scientist, engineers, and multimedia professionals require massive processing power to analyze financial trends, create test simulations, model climate, compile code, render video, decode genomes and other complex tasks. These group of applications can greatly benefit from the use of adaptive, parallel computing middleware that can enable quick parallelization of existing applications and improve application performance by porting these applications on parallel computing platforms like HPC clusters, clouds and multi-core machines.

This work focuses on benchmarking the performance of currently available parallel computing frameworks: OpenMPI and Zircon middleware software, using computation-intensive financial computation applications like binomial option pricing and heston calibrations for option pricing and also compares and contrasts the pros and cons of the two frameworks.