Entry Kandemir:1999:GCO from toplas.bib

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BibTeX entry

@Article{Kandemir:1999:GCO,
  author =       "M. Kandemir and P. Banerjee and A. Choudhary and J.
                 Ramanujam and N. Shenoy",
  title =        "A global communication optimization technique based on
                 data-flow analysis and linear algebra",
  journal =      j-TOPLAS,
  volume =       "21",
  number =       "6",
  pages =        "1251--1297",
  month =        nov,
  year =         "1999",
  CODEN =        "ATPSDT",
  ISSN =         "0164-0925 (print), 1558-4593 (electronic)",
  ISSN-L =       "0164-0925",
  bibdate =      "Tue Sep 26 10:12:58 MDT 2000",
  bibsource =    "http://www.acm.org/pubs/contents/journals/toplas/;
                 http://www.math.utah.edu/pub/tex/bib/toplas.bib",
  URL =          "http://www.acm.org/pubs/citations/journals/toplas/1999-21-6/p1251-kandemir/",
  abstract =     "Reducing communication overhead is extremely important
                 in distributed-memory message-passing architectures. In
                 this article, we present a technique to improve
                 communication that considers data access patterns of
                 the entire program. Our approach is based on a
                 combination of traditional data-flow analysis and a
                 linear algebra framework, and it works on structured
                 programs with conditional statements and nested loops
                 but without arbitrary goto statements.The distinctive
                 features of the solution are the accuracy in keeping
                 communication set information, support for general
                 alignments and distributions including block-cyclic
                 distributions, and the ability to simulate some of the
                 previous approaches with suitable modifications. We
                 also show how optimizations such as message
                 vectorization, message coalescing, and redundancy
                 elimination are supported by our framework.
                 Experimental results on several benchmarks show that
                 our technique is effective in reducing the number of
                 messages (an average of 32\% reduction), the volume of
                 the data communicated (an average of 37\% reduction),
                 and the execution time (an average of 26\%
                 reduction).",
  acknowledgement = ack-nhfb,
  fjournal =     "ACM Transactions on Programming Languages and
                 Systems",
  keywords =     "communication optimizations; data-flow analysis;
                 distributed-memory machines; global optimizations;
                 message vectorization; parallelism",
  subject =      "Software --- Software Engineering --- Programming
                 Environments (D.2.6): {\bf Integrated environments};
                 Software --- Programming Languages --- Language
                 Constructs and Features (D.3.3): {\bf Frameworks};
                 Software --- Programming Languages --- Processors
                 (D.3.4): {\bf Compilers}",
}

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