A case study in high-performance mixed-language programming

  • Authors:

  • Hans Petter Langtangen

  • Affiliations:

  • Simula Research Laboratory, Oslo, Norway and Dept. of Informatics, University of Oslo, Norway

  • Venue:
  • PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
  • Year:
  • 2006

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Abstract

Several widely used and promising programming tools and styles for computational science software are reviewed and compared. In particular, we discuss function/subroutine libraries, object-based programming, object-oriented programming, generic (template) programming, and iterators in the context of a specific example involving sparse matrix-vector products. A key issue in the discussion is to hide the storage structure of the sparse matrix in application code. The role of different languages, such as Fortran, C, C++, and Python, is an integral part of the discussion. Finally, we present performance measures of the various designs and implementations. These results show that high-level Python programming, with loops migrated to compiled languages, maintains the performance of traditional implementations, while offering the programmer a more convenient and efficient tool for experimenting with designs and user-friendly interfaces.