A comparison of three programming models for adaptive applications on the origin2000

Hongzhang Shan, Jaswinder Pal Singh, Leonid Oliker, Rupak Biswas

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

9 Scopus citations


Adaptive applications have computational workloads and communication patterns which change unpredictably at runtime, requiring dynamic load balancing to achieve scalable performance on parallel machines. Efficient parallel implementations of such adaptive applications is therefore a challenging task. In this paper, we compare the performance of and the programming effort required for two major classes of adaptive applications under three leading parallel programming models on an SGI Origin2000 system, a machine which supports all three models efficiently. Results indicate that the three models deliver comparable performance; however, the implementations differ significantly beyond merely using explicit messages versus implicit loads/stores even though the basic parallel algorithms are similar. Compared with the message-passing (using MPI) and SHMEM programming models, the cache-coherent shared address space (CC-SAS) model provides substantial ease of programming at both the conceptual and program orchestration levels, often accompanied by performance gains. However, CC-SAS currently has portability limitations and may suffer from poor spatial locality of physically distributed shared data on large numbers of processors.

Original languageEnglish (US)
Title of host publicationSC 2000 - Proceedings of the 2000 ACM/IEEE Conference on Supercomputing
PublisherAssociation for Computing Machinery
ISBN (Electronic)0780398025
StatePublished - 2000
Externally publishedYes
Event2000 ACM/IEEE Conference on Supercomputing, SC 2000 - Dallas, United States
Duration: Nov 4 2000Nov 10 2000

Publication series

NameProceedings of the International Conference on Supercomputing


Conference2000 ACM/IEEE Conference on Supercomputing, SC 2000
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • General Computer Science


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