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Speculative Parallelization of a Randomized Incremental Convex Hull Algorithm

  • Marcelo Cintra
  • Diego R. Llanos
  • Belén Palop
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3045)

Abstract

Finding the fastest algorithm to solve a problem is one of the main issues in Computational Geometry. Focusing only on worst case analysis or asymptotic computations leads to the development of complex data structures or hard to implement algorithms. Randomized algorithms appear in this scenario as a very useful tool in order to obtain easier implementations within a good expected time bound. However, parallel implementations of these algorithms are hard to develop and require an in-depth understanding of the language, the compiler and the underlying parallel computer architecture. In this paper we show how we can use speculative parallelization techniques to execute in parallel iterative algorithms such as randomized incremental constructions. In this paper we focus on the convex hull problem, and show that, using our speculative parallelization engine, the sequential algorithm can be automatically executed in parallel, obtaining speedups with as little as four processors, and reaching 5.15x speedup with 28 processors.

Keywords

Convex Hull Incremental Algorithm Shared Structure Complex Data Structure Incremental Construction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Marcelo Cintra
    • 1
  • Diego R. Llanos
    • 2
  • Belén Palop
    • 2
  1. 1.School of InformaticsUniversity of EdinburghEdinburghUK
  2. 2.Departamento de InformáticaUniversidad de ValladolidValladolidSpain

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