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

Are Clouds Ready to Accelerate Ad hoc Financial Simulations?