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On Using GPU to Compute Options and Derivatives

Option Models
Options (2008), 2008 – OnEye Pty Ltd – Sydney – Australia

@article{title={On Using GPU to Compute Options and Derivatives},

   year={2008}

}

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Algorithmic Trading has created an increasing demand for high performance computing solutions within financial organizations. The actors of portfolio management and risk assessment have the obligation to increase their computing resources in order to provide competitive models for financial management and pricing financial instruments. GPU Stands for “Graphic Processing Unit”. GPU processing (or Stream Processing) defines a class of algorithms that uses Massively Parallel Architectures as a starting point for Software Design. Computational performance is no longer coming from increase in clock speed, but in leveraging the astonishing performances of those massively parallel architectures. This document describes how GPU technologies can improve efficiency and throughput in the computation of Option Prices. It presents a number of benchmarks specific to financial analysis in order to demonstrate the tremendous advantage of porting trading algorithms to the GPU platform. The benefits of porting computing algorithms to the GPU are: Improve application performance from 10 to 700 fold. Quickly build and deliver massively parallel applications. Leverage existing grid infrastructure by simply adding low cost graphic hardware to the units. Applications built to run on GPU hardware have virtually no limits in their scalability.
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