| dc.identifier.uri | http://hdl.handle.net/11401/76516 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | This work presents multiscale models and efficient numerical algorithms for analyzing the activation mechanisms of platelets under blood flow conditions at disparate spatiotemporal scales on supercomputers, with applications in initial thrombogenicity study and medical device optimization. Modeling the multiscale structures of platelets and the dynamics of their motion in viscous blood plasma require multiple time stepping (MTS) algorithm to optimally utilize the computing resources. This MTS algorithm improves the computational efficiency while maintaining stability and prescribed precisions. Our study of the dynamic properties of flipping platelets adapts the hybridized dissipative particle dynamics and coarse-grained molecular dynamics methods, which resolve the appropriate spatial scales of the platelet and the blood flow, respectively. In addition to the algorithmic strategies, general-purpose graphics processing units are also introduced to speed up the computationally intensive force field evaluations. Examinations of the implementation of the double-punch speedup strategy, i.e., algorithmic MTS and hardware acceleration, reveal significant speedups over single time stepping algorithms and CPU-only solutions. Detailed performance analysis on three representative supercomputers affords the possibility of simulating the millisecond-scale hematology at resolutions of nanoscale platelet and mesoscale bio-flow using millions of particles, the state-of-the-art for the field at the present time. | |
| dcterms.available | 2017-09-20T16:50:31Z | |
| dcterms.contributor | Deng, Yuefan | en_US |
| dcterms.contributor | Glimm, James | en_US |
| dcterms.contributor | Harrison, Robert | en_US |
| dcterms.contributor | Bluestein, Danny. | en_US |
| dcterms.creator | Zhang, Na | |
| dcterms.dateAccepted | 2017-09-20T16:50:31Z | |
| dcterms.dateSubmitted | 2017-09-20T16:50:31Z | |
| dcterms.description | Department of Applied Mathematics and Statistics. | en_US |
| dcterms.extent | 112 pg. | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | http://hdl.handle.net/11401/76516 | |
| dcterms.issued | 2015-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:50:31Z (GMT). No. of bitstreams: 1
Zhang_grad.sunysb_0771E_12601.pdf: 3576713 bytes, checksum: ab2f51ffb2ad41f30e306d673d40fa4d (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | GPGPU acceleration, multiple time stepping, multiscale modeling, performance, platelet simulation | |
| dcterms.subject | Applied mathematics | |
| dcterms.title | Design and Analysis of Parallel Algorithms for Multiscale Modeling of Platelets | |
| dcterms.type | Dissertation | |
