Abstract
Finite difference methods (FDM) have been developed and optimized in a technology context that has radically changed. When FDMs became a standard it used to be that memory was a scarce resource and that algorithms were either memory or compute bound. As a consequence traditional FDMs have been designed to minimize the number of operations and the memory footprint given a certain level of accuracy. In this paper we describe how the potential of GPU computing can be exploited to rethink the way FDM are implemented in the context of financial applications.
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Albanese, C., del Baño Rollin, S., Pietronero, G. (2016). Recasting Finite Difference Methods in Finance to Exploit GPU Computing. In: Russo, G., Capasso, V., Nicosia, G., Romano, V. (eds) Progress in Industrial Mathematics at ECMI 2014. ECMI 2014. Mathematics in Industry(), vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-23413-7_17
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DOI: https://doi.org/10.1007/978-3-319-23413-7_17
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