Abstract
We evaluate in parallel first-order derivatives given a sequential computer program of the function to be differentiated. Our parallel implementation of an automatic differentiation (AD) algorithm is based on a hierarchical approach. The parallel method is developed by considering as a parallel computational model a shared-memory paradigm. The performance of the derivative codes is evaluated by considering a SGI Origin 2000 and by using the OPENMP standard library. In our computational experiments, we have considered the Flow in a Driven Cavity function belonging to the MINPACK-2 test problem collection. The computational results show the performance gain of the parallel approach over both the sequential one and the stripmining technique.
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© 2002 Springer Science+Business Media New York
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Mancini, M. (2002). A Parallel Hierarchical Approach for Automatic Differentiation. In: Corliss, G., Faure, C., Griewank, A., Hascoët, L., Naumann, U. (eds) Automatic Differentiation of Algorithms. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0075-5_27
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DOI: https://doi.org/10.1007/978-1-4613-0075-5_27
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Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-6543-6
Online ISBN: 978-1-4613-0075-5
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