Interval Arithmetic and Automatic Differentiation on GPU Using OpenCL

  • Grzegorz Kozikowski
  • Bartłomiej Jacek Kubica
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7782)

Abstract

This paper investigates efficient and powerful approach to the Gradient and the Hessian evaluation for complex functions. The idea is to apply the parallel GPU architecture and the Automatic Differentiation methods. In order to achieve better accuracy, the interval arithmetic is used. Considerations are based on sequential and parallel authors’ implementation. In this solution, both the AD methods: Forward and Reverse modes are employed. Computational experiments include analysis of performance and are studied on the generated test functions with a given complexity.

Keywords

interval computations automatic differentiation GPGPU OpenCL 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Grzegorz Kozikowski
    • 1
  • Bartłomiej Jacek Kubica
    • 1
  1. 1.Institute of Control and Computation EngineeringWarsaw University of TechnologyPoland

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