Compile-time composition of run-time data and iteration reorderings

  • Authors:

  • Michelle Mills Strout;Larry Carter;Jeanne Ferrante

  • Affiliations:

  • UC, San Diego, La Jolla, CA;UC, San Diego, La Jolla, CA;UC, San Diego, La Jolla, CA

  • Venue:
  • PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
  • Year:
  • 2003

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Abstract

Many important applications, such as those using sparse data structures, have memory reference patterns that are unknown at compile-time. Prior work has developed run-time reorderings of data and computation that enhance locality in such applications.This paper presents a compile-time framework that allows the explicit composition of run-time data and iteration-reordering transformations. Our framework builds on the iteration-reordering framework of Kelly and Pugh to represent the effects of a given composition. To motivate our extension, we show that new compositions of run-time reordering transformations can result in better performance on three benchmarks.We show how to express a number of run-time data and iteration-reordering transformations that focus on improving data locality. We also describe the space of possible run-time reordering transformations and how existing transformations fit within it. Since sparse tiling techniques are included in our framework, they become more generally applicable, both to a larger class of applications, and in their composition with other reordering transformations. Finally, within the presented framework data need be remapped only once at runtime for a given composition thus exhibiting one example of overhead reductions the framework can express.