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An Approach for Code Generation in the Sparse Polyhedral Framework
Parallel Computing (2016)
  • Michelle Mills Strout, University of Arizona
  • Alan LaMielle, Colorado State University
  • Larry Carter, University of California, San Diego
  • Jeanne Ferrante, University of California, San Diego
  • Barbara Kreaseck, La Sierra University
  • Catherine Olschanowsky, Colorado State University
Applications that manipulate sparse data structures contain memory reference patterns that are unknown at compile time due to indirect accesses such as A[B[i]]. To exploit parallelism and improve locality in such applications, prior work has developed a number of Run-Time Reordering Transformations (RTRTs). This paper presents the Sparse Polyhedral Framework (SPF) for specifying RTRTs and compositions thereof and algorithms for automatically generating efficient inspector and executor code to implement such transformations. Experimental results indicate that the performance of automatically generated inspectors and executors competes with the performance of hand-written ones when further optimization is done.
  • inspector/executor strategies,
  • runtime reordering transformations,
  • Sparse Polyhedral Framework
Publication Date
April, 2016
Citation Information
Michelle Mills Strout, Alan LaMielle, Larry Carter, Jeanne Ferrante, et al.. "An Approach for Code Generation in the Sparse Polyhedral Framework" Parallel Computing Vol. 53 (2016) p. 32 - 57
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