The hardware accelerator debate: A financial risk case study using many-core computing. (August 2015)

• Record Type: Journal Article
• Authors: Varghese, Blesson
• Is Part Of: Computers & electrical engineering. Volume 46(2015)
• Page Start: 157
• Page End: 175
• Publication Date: 2015-08
• Subjects: Hardware accelerators -- Many-core computing -- Secondary uncertainty -- Financial risk -- Catastrophic risk -- Risk analysis; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.01.012
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: Parallel algorithms for real-time aggregate risk analysis is developed. The algorithms are evaluated on hardware accelerators such as GPUs and Phis. Both hardware accelerators are useful in different contexts for risk analysis. The Phi generates best timing results when employed independently. The GPU is most effective when used in a hybrid platform. Abstract: The risk of reinsurance portfolios covering globally occurring natural catastrophes, such as earthquakes and hurricanes, is quantified by employing simulations. These simulations are computationally intensive and require large amounts of data to be processed. The use of many-core hardware accelerators, such as the Intel Xeon Phi and the NVIDIA Graphics Processing Unit (GPU), are desirable for achieving high-performance risk analytics. In this paper, we set out to investigate how accelerators can be employed in risk analytics, focusing on developing parallel algorithms for Aggregate Risk Analysis, a simulation which computes the Probable Maximum Loss of a portfolio taking both primary and secondary uncertainties into account. The key result is that both hardware accelerators are useful in different contexts; without taking data transfer times into account the Phi had lowest execution times when used independently and the GPU along with a host in a hybrid platform yielded best performance.