Huge growth in the trading and complexity of credit derivative instruments over the past five years has driven the need for ever more computationally demanding mathematical models. This has led to massive growth in data center compute capacity, power and cooling requirements. We report the results of an on-going joint project between J.P. Morgan and specialist acceleration solutions provider Maxeler Technologies to improve the priceperformance for calculating the value and risk of a large complex credit derivatives portfolio. Our results show that valuing tranches of Collateralized Default Obligations (CDOs) on Maxeler accelerated systems is over 30 times faster per cubic foot and per Watt than solutions using standard multi-core Intel Xeon processors. We also report some preliminary results of further work that extends the approach to classes of interest rate derivatives.

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SUMMARY We report new results from an on‐going project to accelerate derivatives computations. Our earlier work was focused on accelerating the valuation of credit derivatives. In this paper, we extend our work in two ways: by applying the same techniques, first, to accelerate the computation of portfolio level risk for credit derivatives and, second, to different asset classes using a different type of mathematical model, which together present challenges that are quite different to those dealt with in our earlier work. Specifically, we report acceleration over 270 times faster than a single Intel Core for a multi‐asset Monte Carlo model. We also explore the implications for risk. Copyright © 2011 John Wiley & Sons, Ltd.

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