A portable, extensible and fast stochastic volatility model calibration using multi and many‐core processors

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.

A pattern oriented approach for designing scalable analytics applications (invited talk)

“…See [2][3][4] for further details of the application. We introduce some terminology here for ease of exposition.…”

Workshop on high‐performance computational finance

“…S. Li, and J. Lin describe an implementation of Asian option pricing algorithms on many‐core architectures. M. Dixon, M. Zubair, and J. Lötze consider the additional problem of how to preserve portability of compute intensive financial application while simultaneously achieving efficient deployment on the CPU and GPU.…”