Cost vs. performance of VaR on accelerator platforms

Computers & Electrical Engineering

2015

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.

Section: Related Workmentioning

confidence: 99%

The hardware accelerator debate: A financial risk case study using many-core computing

Computers & Electrical Engineering

2015

“…Financial and risk applications in the production setting have progressed from small‐scale clusters to large supercomputers . Recently, an increasing number of financial applications have been migrating from small clusters to multi‐core processors and many‐core accelerators that are available at a low‐cost budget . This includes use of cell broadband engine processors and custom field‐programmable gate arrays architectures .…”

Section: Related Workmentioning

confidence: 99%

Computing probable maximum loss in catastrophe reinsurance portfolios on multi‐core and many‐core architectures

Burke

Rau-Chaplin

Concurrency and Computation

2015

Summary In the reinsurance market, the risks natural catastrophes pose to portfolios of properties must be quantified, so that they can be priced, and insurance offered. The analysis of such risks at a portfolio level requires a simulation of up to 800 000 trials with an average of 1000 catastrophic events per trial. This is sufficient to capture risk for a global multi‐peril reinsurance portfolio covering a range of perils including earthquake, hurricane, tornado, hail, severe thunderstorm, wind storm, storm surge and riverine flooding, and wildfire. Such simulations are both computation and data intensive, making the application of high‐performance computing techniques desirable. In this paper, we explore the design and implementation of portfolio risk analysis on both multi‐core and many‐core computing platforms. Given a portfolio of property catastrophe insurance treaties, key risk measures, such as probable maximum loss, are computed by taking both primary and secondary uncertainties into account. Primary uncertainty is associated with whether or not an event occurs in a simulated year, while secondary uncertainty captures the uncertainty in the level of loss due to the use of simplified physical models and limitations in the available data. A combination of fast lookup structures, multi‐threading and careful hand tuning of numerical operations is required to achieve good performance. Experimental results are reported for multi‐core processors and systems using NVIDIA graphics processing unit and Intel Phi many‐core accelerators. Copyright © 2015 John Wiley & Sons, Ltd.

“…A number of financial applications are being migrated from small clusters to be hosted on multiple core processors and many core coprocessor which are available at a low budget [12]. For example, research related to financial applications exploiting the Cell BE processor [13] [14], FPGAs [15] [16] and GPUs [17], [18], [19], [20].…”

Section: Related Workmentioning

confidence: 99%

Are Clouds Ready to Accelerate Ad Hoc Financial Simulations?

2014 IEEE/ACM International Symposium on Big Data Computing

Barker

2014

Applications employed in the financial services industry to capture and estimate a variety of risk metrics are underpinned by stochastic simulations which are data, memory and computationally intensive. Many of these simulations are routinely performed on production-based computing systems. Ad hoc simulations in addition to routine simulations are required to obtain up-to-date views of risk metrics. Such simulations are currently not performed as they cannot be accommodated on production clusters, which are typically over committed resources. Scalable, on-demand and pay-as-you go Virtual Machines (VMs) offered by the cloud are a potential platform to satisfy the data, memory and computational constraints of the simulation. However, "Are clouds ready to accelerate ad hoc financial simulations?"The research reported in this paper aims to experimentally verify this question by developing and deploying an important financial simulation, referred to as 'Aggregate Risk Analysis' on the cloud. Parallel techniques to improve efficiency and performance of the simulations are explored. Challenges such as accommodating large input data on limited memory VMs and rapidly processing data for real-time use are surmounted. The key result of this investigation is that Aggregate Risk Analysis can be accommodated on cloud VMs. Acceleration of up to 24x using multiple hardware accelerators over the implementation on a single accelerator, 6x over a multiple core implementation and approximately 60x over a baseline implementation was achieved on the cloud. However, computational time is wasted for every dollar spent on the cloud due to poor acceleration over multiple virtual cores. Interestingly, private VMs can offer better performance than public VMs on comparable underlying hardware.