Sensitivity Analysis of Biomolecular Simulations using Symbolic Models
- Resource Type
- Conference
- Authors
- Alam, Sadaf R.; Bhatia, Nikhil; Vetter, Jeffrey S.
- Source
- 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering Bioinformatics and Bioengineering, 2007. BIBE 2007. Proceedings of the 7th IEEE International Conference on. :294-300 Oct, 2007
- Subject
- Bioengineering
Computing and Processing
Sensitivity analysis
Analytical models
Computational modeling
Predictive models
Packaging
Large-scale systems
Virtual prototyping
High performance computing
Performance analysis
Laboratories
biomolecular simulations
high performance computing
performance modeling and prediction
performance analysis
scalability
- Language
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.