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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 9/2017

31.01.2017

Mapping of option pricing algorithms onto heterogeneous many-core architectures

verfasst von: Shuai Zhang, Zhao Wang, Ying Peng, Bertil Schmidt, Weiguo Liu

Erschienen in: The Journal of Supercomputing | Ausgabe 9/2017

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Abstract

The rapid development of technologies and applications in recent years poses high demands and challenges for high-performance computing. Because of their competitive performance/price ratio, heterogeneous many-core architectures are widely used in high-performance computing areas. GPU and Xeon Phi are two popular general-purpose many-core accelerators. In this paper, we demonstrate how heterogeneous many-core architectures, powered by multi-core CPUs, CUDA-enabled GPUs and Xeon Phis can be used as an efficient computational platform to accelerate popular option pricing algorithms. In order to make full use of the compute power of this architecture, we have used a hybrid computing model which consists of two types of data parallelism: worker level and device level. The worker level data parallelism uses a distributed computing infrastructure for task distribution, while the device level data parallelism uses both the multi-core CPUs and many-core accelerators for fast option pricing calculation. Experiments show that our implementations achieve good performance and scalability on this architecture and also outperform other state-of-the-art GPU-based solutions for Monte Carlo European/American option pricing and BSDE European option pricing.
Vorheriger Artikel Elastic resource provisioning in hybrid mobile cloud for computationally intensive mobile applications
Nächster Artikel HeDPM: load balancing of linear pipeline applications on heterogeneous systems

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Metadaten
Titel
Mapping of option pricing algorithms onto heterogeneous many-core architectures
verfasst von
Shuai Zhang
Zhao Wang
Ying Peng
Bertil Schmidt
Weiguo Liu
Publikationsdatum
31.01.2017
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 9/2017
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-017-1968-z

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