ET International and Pico Computing used a FPGA cluster to provide near-real-time Value-at-Risk (VaR) and options pricing analysis, representing a greater than 1100X overall application speed-up when compared to an optimized software-only application. VaR is an analysis method based on the probability and magnitude of losses given a specific time horizon, and has become the standard risk management index. However, VaR suffers several drawbacks due to its need for large numbers of iterative calculations. These drawbacks include increased costs for computing hardware, and long run times to generate results.
The ETI aGreen FPGA-accelerated solution, which has been implemented using an FPGA cluster available from Pico Computing, has been shown to deliver more than 1100X acceleration when compared to a software solution running optimized Monte Carlo analytics code on an Intel Xeon processor.
From a cost perspective, ETI has shown that dramatic saving can be obtained by replacing 1100 or more high-end processors with a small cluster of FPGAs fitting in one server chassis. Total operational costs, including hardware purchase and power consumption, can cut by 20X. Multiple cards can be installed in one 4U server (up to 14) with each board having up to 16 FPGA devices, or individual PCIe cards can be added into existing servers. The ETI aGreen solution runs on broadly available PCIe compatible platforms, simplifying deployment. The solution is inherently scalable, and is extensible via libraries.
In recent years, exotic financial instruments such as debt swaps and collateralized debt obligations (CDOs) have played larger roles in fund performance and volatility. These risky instruments and the associated uncertainties in the current financial environment create a greater need for risk measurement, using risk-adjusted return methods.
Asset pricing using the Black-Scholes equation forms the theoretical backbone for modern asset pricing, for example in the valuation of options. However, the financial community and regulators have found the accuracy of the Black-Scholes is not supported by empirical data. As a result, much research work has been done to extend the Black-Scholes model to accommodate new understanding of market behaviors. Stochastic methods that rely on Monte Carlo simulation using very fine time increments have shown to be more accurate for financial analytics, but are compute-intensive and therefore expensive to deploy on traditional servers.