Parallel Simulations for Analysing Portfolios of Catastrophic Event Risk

Proceedings of the 6th Workshop on High Performance Computational Finance

Rau-Chaplin

2013

Aggregate Risk Analysis is a computationally intensive and a data intensive problem, thereby making the application of high-performance computing techniques interesting. In this paper, the design and implementation of a parallel Aggregate Risk Analysis algorithm on multi-core CPU and many-core GPU platforms are explored. The efficient computation of key risk measures, including Probable Maximum Loss (PML) and the Tail Value-at-Risk (TVaR) in the presence of both primary and secondary uncertainty for a portfolio of property catastrophe insurance treaties is considered. Primary Uncertainty is the the uncertainty associated with whether a catastrophe event occurs or not in a simulated year, while Secondary Uncertainty is the uncertainty in the amount of loss when the event occurs.A number of statistical algorithms are investigated for computing secondary uncertainty. Numerous challenges such as loading large data onto hardware with limited memory and organising it are addressed. The results obtained from experimental studies are encouraging. Consider for example, an aggregate risk analysis involving 800,000 trials, with 1,000 catastrophic events per trial, a million locations, and a complex contract structure taking into account secondary uncertainty. The analysis can be performed in just 41 seconds on a GPU, that is 24x faster than the sequential counterpart on a fast multi-core CPU. The results indicate that GPUs can be used to efficiently accelerate aggregate risk analysis even in the presence of secondary uncertainty.

Section: Implementing Methods To Compute Secondary Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accounting for secondary uncertainty

Proceedings of the 6th Workshop on High Performance Computational Finance

Rau-Chaplin

2013

“…We present such an application employed in the financial risk industry, referred to as 'Aggregate Risk Analysis' [28] for validating the feasibility of our proposed multi-tenancy approach. The analysis of financial risk is underpinned by a simulation that is computationally intensive.…”

Section: Financial Risk Applicationmentioning

confidence: 99%

“…A set of contractual financial terms (I) are applied to each loss value of the Event-Loss pair extracted from an ELT to the benefit of the layer. The event loss for each event occurrence in the trial, combined across all ELTs associated with the layer, are subject to further financial terms (T ) [28].…”

Section: Typical Layer Covers Approximately 3 To 30 Individual Elts Amentioning

confidence: 99%

Multi-tenant virtual GPUs for optimising performance of a financial risk application

Prades

Journal of Parallel and Distributed Computing

Reaño

et al. 2017

Graphics Processing Units (GPUs) are becoming popular accelerators in modern High-Performance Computing (HPC) clusters. Installing GPUs on each node of the cluster is not efficient resulting in high costs and power consumption as well as underutilisation of the accelerator. The research reported in this paper is motivated towards the use of few physical GPUs by providing cluster nodes access to remote GPUs on-demand for a financial risk application. We hypothesise that sharing GPUs between several nodes, referred to as multi-tenancy, reduces the execution time and energy consumed by an application. Two data transfer modes between the CPU and the GPUs, namely concurrent and sequential, are explored. The key result from the experiments is that multi-tenancy with few physical GPUs using sequential data transfers lowers the execution time and the energy consumed, thereby improving the overall performance of the application.

“…The second case study is a risk simulation that generates probable maximum losses due to catastrophic events [29]. The simulation considers over a million alternate views of a given year and a number of financial terms to estimate losses.…”

Section: A Case Study Applicationsmentioning

confidence: 99%

Container-Based Cloud Virtual Machine Benchmarking

2016 IEEE International Conference on Cloud Engineering (IC2E)

Subba

Thai

et al. 2016

With the availability of a wide range of cloud Virtual Machines (VMs) it is difficult to determine which VMs can maximise the performance of an application. Benchmarking is commonly used to this end for capturing the performance of VMs. Most cloud benchmarking techniques are typically heavyweight -time consuming processes which have to benchmark the entire VM in order to obtain accurate benchmark data. Such benchmarks cannot be used in real-time on the cloud and incur extra costs even before an application is deployed.In this paper, we present lightweight cloud benchmarking techniques that execute quickly and can be used in near realtime on the cloud. The exploration of lightweight benchmarking techniques are facilitated by the development of DocLite -Docker Container-based Lightweight Benchmarking. DocLite is built on the Docker container technology which allows a user-defined portion (such as memory size and the number of CPU cores) of the VM to be benchmarked. DocLite operates in two modes, in the first mode, containers are used to benchmark a small portion of the VM to generate performance ranks. In the second mode, historic benchmark data is used along with the first mode as a hybrid to generate VM ranks. The generated ranks are evaluated against three scientific high-performance computing applications. The proposed techniques are up to 91 times faster than a heavyweight technique which benchmarks the entire VM. It is observed that the first mode can generate ranks with over 90% and 86% accuracy for sequential and parallel execution of an application. The hybrid mode improves the correlation slightly but the first mode is sufficient for benchmarking cloud VMs.