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

Stochastic simulation techniques employed for portfolio risk analysis often referred to as Aggregate Risk Analysis, can benefit from exploiting state-of-the-art high-performance computing platforms. In this paper, the authors propose parallel methods to speedup aggregate risk analysis for supporting real-time pricing. To achieve this an algorithm for analyzing 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.

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Dalhousie University