An approach for enhancing inter-processor data locality on chip multiprocessors

Guilin Chen, Mahmut Kandemir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


The tighter integration on chip multiprocessors exerts a higher pressure on off-chip accesses to the memory system. This makes minimizing the number of off-chip accesses a critical optimization goal. This paper discusses a compiler-based solution to this problem for the embedded applications that perform stencil computations. An important characteristic of this solution is that it distinguishes between the intra-processor data reuse and inter-processor data reuse. The first of these captures the data reuse that occurs across loop iterations assigned to the same processor, whereas the second one represents the data reuse that takes place across the loop iterations assigned to different processors. The proposed approach then optimizes inter-processor reuse by re-organizing the loop iterations of each processor carefully, considering how data elements are shared across processors. The goal is to ensure that the different processors access the shared data within a short period of time, so that the data can be captured in the on-chip memory space at the time of the reuse. This paper also presents an evaluation of the proposed optimization and compares it to an alternate scheme that optimizes data locality for each processor in isolation. The results obtained by applying our implementation to eight loop-intensive benchmark codes from the embedded computing domain show that our approach improves over the mentioned alternate scheme by 15.6% on average.

Original languageEnglish (US)
Title of host publicationTransactions on High-Performance Embedded Architectures and Compilers I
Number of pages20
StatePublished - Dec 1 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4050 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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