Dirk Pflüger

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With the ever-increasing complexity, accuracy, dimensionality, and size of simulations, a step in the direction of data-intensive scientific discovery becomes necessary. Parameter-dependent simulations are an example of such a data-intensive tasks: The researcher, who is interested in the dependency of the simulation’s result on a set of input parameters,(More)
Gaining knowledge out of vast datasets is a main challenge in data-driven applications nowadays. Sparse grids provide a numerical method for both classification and regression in data mining which scales only linearly in the number of data points and is thus well-suited for huge amounts of data. Due to the recursive nature of sparse grid algorithms, they(More)
The sparse grid discretization technique enables a compressed representation of higher-dimensional functions. In its original form, it relies heavily on recursion and complex data structures, thus being far from well-suited for GPUs. In this paper, we describe optimizations that enable us to implement compression and decompression, the crucial sparse grid(More)
We present the parallelization of a sparse grid finite element discretization of the Black-Scholes equation, which is commonly used for option pricing. Sparse grids allow to handle higher dimensional options than classical approaches on full grids, and can be extended to a fully adaptive discretization method. We introduce the algorithmical structure of(More)
Gaining knowledge out of vast datasets is a main challenge in data-driven applications nowadays. Sparse grids provide a numerical method for both classification and regression in data mining which scales only linearly in the number of data points and is thus well-suited for huge amounts of data. Due to the recursive nature of sparse grid algorithms and(More)
This paper presents an efficient approach to parallel pricing of multi-dimensional financial derivatives based on the Black-Scholes Partial Differential Equation (BS-PDE). One of the main challenges for such multi-dimensional problems is the curse of dimensionality, that is tackled in our approach by the combination technique (CT). This technique consists(More)