Performance evaluation of programming models for SMP-based clusters

Myungho Lee, Neungsoo Park, Won W. Ro, Kuan Ching Li

Research output: Contribution to journalArticlepeer-review


Recently, computing clusters based on shared-memory multiprocessors (SMP's) is becoming popular for high performance computing (HPC) applications. With the recent prevalence of CPU's, which are small-scale SMP's themselves, multi-core CPU's SMP clusters will become increasingly popular in the near future. SMP clusters have characteristics of both SMP's and MPP's. Therefore, developing parallel programs which can efficiently exploits characteristics of both SMP and MPP in SMP clusters is a challenging task. Standard parallel programming models such as MPI, OpenMP, or Hybrid (a combination of the two former models) are commonly used for SMP clusters. Depending on the characteristics of applications, however, some programming models are better than others. To identify and select a suitable programming model for an application on SMP clusters needs a quantity of analysis of the application behavior and its performance. In this paper, we conduct experimental studies to evaluate the benefits and limits of MPI and OpenMP on three SMP-based systems using standard HPC applications parallelized using MPI, OpenMP, and Hybrid model. The performance results and final analysis may lead to an optimal programming model for the applications.

Original languageEnglish
Pages (from-to)1181-1188
Number of pages8
JournalJournal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an
Issue number7
Publication statusPublished - 2008

Bibliographical note

Funding Information:
This paper is based upon work supported in part by Korea Research Foundation (KRF) under MOEHRD grant no. KRF-2007-521-D00406 and Taiwan National Science Council (NSC) grants no. NSC95-2221-E-126-006-MY3 and NSC96-2221-E-126-004-MY3. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the KRF or NSC.

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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