A methodology for generating data distributions to optimize communication
Sandeep Kumar Gupta, S. D. Kaushik, Chien‐Hung Huang, Jeremy Russell Johnson, R. W. Johnson, Ponnuswamy Sadayappan · 2003
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.>