Achieving Speedup in Aggregate Risk Analysis using Multiple GPUs

TL;DR

This paper demonstrates that using multiple GPUs can significantly accelerate aggregate risk analysis, enabling real-time insurance risk pricing by achieving up to 77 times speedup over CPU implementations.

Contribution

The paper introduces a parallel algorithm for aggregate risk analysis optimized for GPUs, enabling real-time processing of large-scale simulations.

Findings

GPU implementation achieves 77x speedup over CPU

Simulation of 1 million trials completed in under 5 seconds

GPU-based approach is feasible for real-time risk pricing

Abstract

Stochastic simulation techniques employed for the analysis of portfolios of insurance/reinsurance risk, often referred to as `Aggregate Risk Analysis', can benefit from exploiting state-of-the-art high-performance computing platforms. In this paper, parallel methods to speed-up aggregate risk analysis for supporting real-time pricing are explored. An algorithm for analysing aggregate risk is proposed and implemented for multi-core CPUs and for many-core GPUs. Experimental studies indicate that GPUs offer a feasible alternative solution over traditional high-performance computing systems. A simulation of 1,000,000 trials with 1,000 catastrophic events per trial on a typical exposure set and contract structure is performed in less than 5 seconds on a multiple GPU platform. The key result is that the multiple GPU implementation can be used in real-time pricing scenarios as it is approximately 77x times faster than the sequential counterpart implemented on a CPU.