Collaborative Research: Petascale Algorithms for Particulate Flows

合作研究：颗粒流的千万亿次算法

• 批准号: 0923710
• 负责人: George Biros
• 金额: --
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0923710&HistoricalAwards=false
• 关键词: Collaborative; Research; Petascale; Algorithms; Particulate

Project proposes a petaflop-scalable computational infrastructure for the direct simulation of particulate flows, in particular the simulation of spatio-temporal dynamics of platelet aggregation. Better understanding of microcirculation of blood and platelet rheology will impact clinical needs in thrombosis risk assessment, anti-coagulation therapy, and stroke research. The proposed method comprises two algorithmic components: (1) integral equation solvers for Stokesian flows with dynamic interfaces; and (2) scalable fast multipole algorithms. Why do we need petaflop-scale computing power to tackle this problem? One microliter of blood contains millions of red blood cells(RBCs) and a few hundred thousand platelets. Discretizations with O(100 points/cell and O(1000) time steps result in more than a trillion space-time unknowns. Solving problems of such size will require 50K-core machines. Computational tools that achieve such scalability, will enable direct numerical simulation of several microliters of blood, once million-core computing platforms are available.

项目提出了一个可直接模拟颗粒流量的Petaflop量表计算基础架构，特别是对血小板聚集的时空动力学的模拟。更好地了解血液和血小板流变性的微循环将影响血栓形成风险评估，抗癌疗法和中风研究的临床需求。所提出的方法包括两个算法组件：（1）具有动态接口的Stokesian流的积分方程求解器； （2）可扩展的快速多极算法。为什么我们需要PETAFLOP规模的计算能力来解决此问题？一毫升的血液含有数百万的红细胞（RBC）和几十万个血小板。 O（100点/单元和O（1000）时间步长的离散性会导致超过万亿个时空未知数。解决此类尺寸的问题将需要50k核心机器。一旦可以使用百万核计算平台，可以直接对几个血液的血液进行直接数值模拟。