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Quantlib is a long-lived open-source library for pricing for financial derivative contracts. It is in production use in a number commercial organisations for both pricing and risk analysis and management purposes. This type of computing is best classified as <<simulation>> but as I will show in this talk, with QuantLib as an example, there is an increasing convergence between <<simulation>> computing and <<big data>> computing. The reasons for this convergence include:
1) Desire to reuse scale-out and on-demand-scaling technologies developed for big-data analysis
2) Very high data volume output from the simulations which is costly to reproduce, meaning that the outputs are saved and mined subsequently for multiple scenarios and analyses.
A similar convergence pattern can be seen in a number of other fields in science and engineering, e.g., climate modelling simulation, which will make this talk of interest to a wide audience.
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