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Multi-GPU Computing for Achieving Speedup in Real-time Aggregate Risk Analysis
Year Of Publication: 2013
Month Of Publication: January
Pages: 9
Download Count: 12
View Count: 1992
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 3-2-2013
Publisher: Administrator
Summary
Stochastic simulation techniques employed for portfolio risk analysis, often referred to as Aggregate Risk Analysis, can bene?t 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.
Author(s)
Rau-Chaplin, Andrew Sign in to follow this author
Varghese, Blesson Sign in to follow this author
Bahl, Aman Kumar Sign in to follow this author
Baltzer, Oliver Sign in to follow this author
Whiteway, Aaron Sign in to follow this author
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