A portable, extensible and fast stochastic volatility model calibration using multi and many‐core processors. (20th November 2015)

• Record Type: Journal Article
• Authors: Dixon, Matthew; Lotze, Jörg; Zubair, Mohammad
• Is Part Of: Concurrency and computation. Volume 28:Number 3(2016)
• Page Start: 866
• Page End: 877
• Publication Date: 2015-11-20
• Subjects: calibration -- stochastic volatility -- GPGPU -- C++; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3727
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Financial markets change precipitously, and on‐demand pricing and risk models must be constantly recalibrated to reduce risk. However, certain classes of models are computationally intensive to robustly calibrate to intraday prices – stochastic volatility models being an archetypal example due to the non‐convexity of the objective function. In order to accelerate this procedure through parallel implementation, financial application developers are faced with an ever growing plethora of low‐level high‐performance computing frameworks such as Open Multi‐Processing, Open Computing Language, compute unified device architecture, or single instruction multiple data intrinsics, and forced to make a trade‐off between performance versus the portability, flexibility, and modularity of the code required to facilitate rapid in‐house model development and productionisation. This paper describes the acceleration of stochastic volatility model calibration on multi‐core CPUs and graphics processing units (GPUs) using the Xcelerit platform. By adopting a simple programming model, the Xcelerit platform enables the application developer to write sequential, high‐level C++ code, without concern for low‐level high‐performance computing frameworks. This platform provides the portability, flexibility, and modularity required by application developers. Speedups of up to 30x and 293x are respectively achieved on an Intel Xeon CPU and NVIDIA Tesla K40 GPU, compared with a sequential CPU implementation. The Xcelerit platform implementation is further shown to be equivalent in performance to a low‐level compute unified device architecture version. Overall, we are able to reduce the entire calibration process time of the sequential implementation from 6189 to 183.8 and 17.8 s on the CPU and GPU, respectively, without requiring the developer to reimplement in low‐level high‐performance computing frameworks. Copyright © 2015 John Wiley & Sons, Ltd.