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Multi-GPU Computing for Achieving Speedup in Real-time Aggregate Risk Analysis

A. K. Bahl, O. Baltzer, A. Rau-Chaplin, B. Varghese, A. Whiteway
Center for Security, Theory and Algorithmic Research, International Institute of Information Technology, Hyderabad, India
International Institute of Information Technology, 2013
@article{bahl2013multi,

   title={Multi-GPU Computing for Achieving Speedup in Real-time Aggregate Risk Analysis},

   author={Bahl, AK and Baltzer, O and Rau-Chaplin, A and Varghese, B and Whiteway, A},

   year={2013}

}

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Stochastic simulation techniques employed for portfolio risk analysis, often referred to as Aggregate Risk Analysis, can benefit from exploiting state-of-the-art highperformance computing platforms. In this paper, we propose parallel methods to speedup aggregate risk analysis for supporting real-time pricing. To achieve this an algorithm for analysing aggregate risk is proposed and implemented in C and OpenMP for multi-core CPUs and in C and CUDA for many-core GPUs. An evaluation of the performance of the algorithm indicates that GPUs offer a feasible alternative solution over traditional high-performance computing systems. An aggregate simulation on a multi-GPU of 1 million trials with 1000 catastrophic events per trial on a typical exposure set and contract structure is performed in less than 5 seconds. The key result is that the multi-GPU implementation of the algorithm presented in this paper is approximately 77x times faster than the traditional counterpart and can be used in real-time pricing scenarios.
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