Journal ArticleDOI
Parallelizing a Black-Scholes solver based on finite elements and sparse grids
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TLDR
The algorithmical structure of efficient algorithms operating on sparse grids are introduced, and it is demonstrated how they can be used to derive an efficient parallelization with OpenMP of the Black-Scholes solver.Abstract:
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 efficient algorithms operating on sparse grids and demonstrate how they can be used to derive an efficient parallelization with OpenMP of the Black-Scholes solver. We show results on different commodity hardware systems based on multi-core architectures with up to 24 cores and discuss the parallel performance using Intel and Advanced Micro Devices AMD CPUs. Copyright © 2012 John Wiley & Sons, Ltd.read more
Citations
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Journal ArticleDOI
Option pricing with a direct adaptive sparse grid approach
TL;DR: This paper introduces the spatially adaptive discretization of the Black-Scholes equation with sparse grids and describes the algorithmic structure of the numerical solver, and presents several strategies for adaptive refinement.
Proceedings ArticleDOI
Multi- and many-core data mining with adaptive sparse grids
Alexander Heinecke,Dirk Pflüger +1 more
TL;DR: This paper presents the parallelization on several current task- and data-parallel platforms, covering multi-core CPUs with vector units, GPUs, and hybrid systems, and analyzes the suitability of parallel programming languages for the implementation.
Journal ArticleDOI
Emerging Architectures Enable to Boost Massively Parallel Data Mining Using Adaptive Sparse Grids
Alexander Heinecke,Dirk Pflüger +1 more
TL;DR: This paper presents the parallelization on several current task- and data-parallel platforms, covering multi-core CPUs with vector units, GPUs, and hybrid systems, and analyzes the suitability of parallel programming languages for the implementation.
Proceedings ArticleDOI
Hybrid parallel solutions of the Black-Scholes PDE with the truncated combination technique
Janos Benk,Dirk Pflüger +1 more
TL;DR: The hybrid parallel approach to parallel pricing of multi-dimensional financial derivatives based on the Black-Scholes Partial Differential Equation is presented, and a shared memory parallel multigrid solver for the BS-PDE is developed.
Proceedings ArticleDOI
A scalable design approach for stencil computation on reconfigurable clusters
TL;DR: A scalable communication model to schedule communication operations based on available resources and algorithm properties is proposed to solve the problem of scalability of stencil algorithms in large-scale clusters.
References
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Journal ArticleDOI
Sparse grid collocation schemes for stochastic natural convection problems
TL;DR: The sparse grid collocation method based on the Smolyak algorithm offers a viable alternate method for solving high-dimensional stochastic partial differential equations and an extension of the collocation approach to include adaptive refinement in important stochastically dimensions is utilized to further reduce the numerical effort necessary for simulation.
Journal ArticleDOI
Data mining with sparse grids
TL;DR: It turns out that the new method achieves correctness rates which are competitive to that of the best existing methods, i.e. the amount of data to be classified.
Book
Sparse Grid Quadrature in High Dimensions with Applications in Finance and Insurance
TL;DR: Numerical experiments show that the approaches presented in this book can be faster and more accurate than (quasi-) Monte Carlo methods, even for integrands with hundreds of dimensions.
Journal ArticleDOI
Efficient Hierarchical Approximation of High-Dimensional Option Pricing Problems
TL;DR: The objective of this article is to show how an efficient discretization can be achieved by hierarchical approximation as well as asymptotic expansions of the underlying continuous problem.
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