Sensitivity analysis of biomolecular simulations using symbolic models

Sadaf R. Alam, Nikhil Bhatia, Jeffrey S. Vetter

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Performance and scaling of biomolecular simulations frameworks largely depends on not only the workload characteristics of the simulations but also the design of underlying processor architecture and interconnection networks. Because construction of Teraflops and Petaflops scale prototype systems for evaluation alone is impractical and cost-prohibitive, architects use analytical models of workloads and architecture simulators to guide their design decisions and tradeoffs. To address the problem of providing scalable yet precise input for network simulators, we have developed a technique to model symbolically the communication patterns of production-level scientific applications to study workload growth rates and to carry out sensitivity analysis. We apply our symbolic modeling scheme to the Particle Mesh Ewald (PME) implementation in the sander package of the AMBER framework and demonstrate how the increase in computation, memory and communication requirements impact the performance and scaling of the PME method on the next-generation massively-parallel systems.

Original languageEnglish
Title of host publicationProceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Pages294-300
Number of pages7
DOIs
StatePublished - 2007
Event7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, United States
Duration: Jan 14 2007Jan 17 2007

Publication series

NameProceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

Conference

Conference7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Country/TerritoryUnited States
CityBoston, MA
Period01/14/0701/17/07

Keywords

  • Biomolecular simulations
  • High performance computing
  • Performance analysis
  • Performance modeling and prediction
  • Scalability

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