Accelerating pairwise statistical significance estimation using NUMA machine

Yuhong Zhang*, Fan Zhou, Jianping Gou, Hua Xiao, Zhiguang Qin, Ankit Agrawal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Pairwise statistical significance (PSS) has been found as a promising alternative to database statistical significance for homology detection. However, the estimation of PSS is both computationally and data intensive, which poses a big challenge on performance and scalability in terms of distributed computations. In this work, we develop the parallel accelerator for the estimation of PSS using Non-Uniform Memory Acess (NUMA) machine, which is implemented in C++ using OpenMP, MPI and hybrid paradig-ms, respectively. Through distributing the computation kernels of the estimation of PSS procedure across multiple cores, we achieve up to 22.65× speedup using 24 CPU cores compared to sequential implementation.

Original languageEnglish (US)
Pages (from-to)3887-3894
Number of pages8
JournalJournal of Computational Information Systems
Issue number9
StatePublished - May 1 2012


  • Multicore
  • Non-uniform memory acess (NUMA)
  • Pairwise statistical significance

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications


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