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Mapping of option pricing algorithms onto heterogeneous many-core architectures

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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.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, China

    Shuai Zhang, Zhao Wang & Weiguo Liu

  2. ZhongTai Securities Institute for Financial Studies, Shandong University, Jinan, China

    Ying Peng

  3. Johannes Gutenberg University, Mainz, Germany

    Bertil Schmidt

Authors
  1. Shuai Zhang
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  2. Zhao Wang
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  3. Ying Peng
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  4. Bertil Schmidt
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  5. Weiguo Liu
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Correspondence to Weiguo Liu.

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Zhang, S., Wang, Z., Peng, Y. et al. Mapping of option pricing algorithms onto heterogeneous many-core architectures. J Supercomput 73, 3715–3737 (2017). https://doi.org/10.1007/s11227-017-1968-z

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  • DOI: https://doi.org/10.1007/s11227-017-1968-z

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