A data-driven paradigm for mapping problems

Peng Zhang, Ling Liu, Yuefan Deng

Research output: Contribution to journalArticlepeer-review


We present a new data-driven paradigm for solving mapping problems on parallel computers. This paradigm targets at mapping data modules, instead of task modules, onto multiple processing cores. By dependency analysis of data modules, we devise a data movement matrix to reduce the need of manipulating task program modules at the expenses of handling data modules. To visualize and quantify the complex maneuver, we adopt the parallel activities trace graphs introduced earlier. To demonstrate the procedure and algorithmic values of our paradigm, we test it on the Strassen matrix multiplication and Cholesky matrix inversion algorithms. Mapping tasks has been more widely studied while mapping data is a new approach that appears to be more efficient for data-intensive applications that are becoming prevalent for today's parallel computers with millions of cores.

Original languageEnglish (US)
Pages (from-to)108-124
Number of pages17
JournalParallel Computing
StatePublished - Jul 9 2015


  • Data mapping
  • Data movement matrix
  • Parallel computing
  • Task mapping

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence


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