Efficient Multiscale Platelets Modeling Using Supercomputers
Student: Na Zhang (Stony Brook University)
Supervisor: Yuefan Deng (Stony Brook Univerity)
Abstract: This work focuses on developing multiscale models and efficient numerical algorithms for simulating platelets on supercomputers. More specifically, the development of multiple time-stepping algorithm can be applied to optimally use computing resources to model platelet structures at multiple scales, enabling the study of flow-induced platelet-mediated thrombogenicity. In order to achieve this, sophisticated parallel computing algorithms are developed and detailed performance analysis has been conducted. The performance results manifest the possibility of simulating the millisecond-scale hematology at resolutions of nanoscale platelets and mesoscale bio-flows using millions of particles. The computational methodology using multiscale models and algorithms on supercomputers will enable efficient predictive simulations for initial thrombogenicity study and may provide a useful guide for exploring mechanisms of other complex biomedical problems at disparate spatiotemporal scales. This poster will cover multiscale model, a two-fold speedup strategy, i.e., combined algorithmic multiple time-stepping and hardware GPGPU acceleration, and performance analysis.
Two-page extended abstract: pdf