This paper presents a framework for multi-core implementations of divide and conquer algorithms and shows its efficiency and
ease of use by applying it to some fundamental problems in computational geometry. The framework supports automatic parallelization
of any D&C algorithm. It is only required that the algorithm is implemented by a C++ class implementing a so-called job-interface.
We also report on experimental results and discuss some aspects of the automatic parallelization of randomized incremental
algorithms. Some results of this paper have been presented in the 20th Annual Canadian Conference on Computational Geometry
[Show abstract][Hide abstract] ABSTRACT: We present a framework for multi-core implementations of divide and conquer algorithms and show its efficiency and ease of use by applying it to the fundamental geo- metric problem of computing the convex hull of a point set. We concentrate on the Quickhull algorithm intro- duced in (2). In general the framework can easily be used for any D&C-algorithm. It is only required that the algorithm is implemented by a C++ class imple- menting the job-interface introduced in section 3 of this paper.
Proceedings of the 20th Annual Canadian Conference on Computational Geometry, Montreal, Canada, August 13-15, 2008; 01/2008
[Show abstract][Hide abstract] ABSTRACT: We give an overview of the LEDA platform for combinatorial and geometric computing and an account of its development. We discuss our motivation for building LEDA and to what extent we have reached our goals. We also discuss some recent theoretical developments. This paper contains no new technical material. It is intended as a guide to existing publications about the system. We refer the reader also to our web-pages for more information.
04/2006: pages 7-16; Cambridge University Press.
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