Max Stein, TU Kaiserslautern
GPU accelerated AD for a financial application
Pricing call options using Monte Carlo simulation is massively parallel and hence very well suited for GPU acceleration, where one might wonder if this is also the case for its adjoint reverse calculation when seeking for retrieving adjoints. After reviewing some basics from finance including modeling option prices and estimating the volatility using the Dupire formula, a GPU accelerated implementation calculating the derivatives with respect to all input variables will be presented. Concerning the implementation, there are some race conditions arising in a naive implementation which have to be resolved and there are additionally some efforts necessary for dealing with the limited GPU memory by storing very efficiently only the absolutely necessary information from forward for the reverse run. Furthermore there will be ways presented for improving performance like calculating in mixed precision, using shared memory and using texture memory. Finally the runtime and accuracy of CPU vs GPU version will be compared which will show the benefit and importance of using GPU acceleration for this application.