A methodology for generating data distributions to optimize communication

  • Authors:

  • Gupta; Kaushik; Huang; Johnson; Johnson; Sadayappan

  • Affiliations:

  • Dept. of Comput. & Inf. Sci., Ohio State Univ., Columbus, OH, USA;Dept. of Comput. & Inf. Sci., Ohio State Univ., Columbus, OH, USA;Dept. of Comput. & Inf. Sci., Ohio State Univ., Columbus, OH, USA;-;-;-

  • Venue:
  • SPDP '92 Proceedings of the 1992 Fourth IEEE Symposium on Parallel and Distributed Processing
  • Year:
  • 1992

Quantified Score

Hi-index 0.00

Visualization

Abstract

The authors present an algebraic theory, based on the tensor product for describing the semantics of regular data distributions such as block, cyclic, and block-cyclic distributions. These distributions have been proposed in high performance Fortran, an ongoing effort for developing a Fortran extension for massively parallel computing. This algebraic theory has been used for designing and implementing block recursive algorithms on shared-memory and vector multiprocessors. In the present work, the authors extend this theory to generate programs with explicit data distribution commands from tensor product formulas. A methodology to generate data distributions that optimize communication is described. This methodology is demonstrated by generating efficient programs with data distribution for the fast Fourier transform.