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are in general accessible through the World Wide Web. Abstract. Algorithmic differentiation (AD) is a mathematical concept which evolved over the last decades to a very robust and well understood tool for computation of derivatives. It can be applied to mathematical algorithms, codes for numerical simulation, and whenever derivatives are needed. In this(More)
We consider the Algorithmic Differentiation (also know as Automatic Differentiation; AD) of numerical simulation programs which contain calls to solvers for parameterized systems of n nonlinear equations. The local computational overhead as well as the additional memory requirement for the computation of directional derivatives or adjoints of the solution(More)
We discuss software tool support for the algorithmic differentiation (AD), also known as automatic differentiation, of numerical simulation programs that contain calls to solvers for parameterized systems of <i>n</i> nonlinear equations. The local computational overhead and the additional memory requirement for the computation of directional derivatives or(More)
We consider a GPU accelerated program using Monte Carlo simulation to price a basket call option on 10 FX rates driven by a 10 factor local volatility model. We develop an adjoint version of this program using algorithmic differentiation. The code uses mixed precision. For our test problem of 10,000 sample paths with 360 Euler time steps, we obtain a(More)