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.

该项目提出了一种千万亿次可扩展的计算基础设施，用于直接模拟颗粒流，特别是模拟血小板聚集的时空动力学。更好地了解血液微循环和血小板流变学将影响血栓形成风险评估、抗凝治疗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）具有动态接口的斯托克斯流积分方程求解器； (2)可扩展的快速多极算法。为什么我们需要千万亿次的计算能力来解决这个问题？一微升血液含有数百万个红细胞（RBC）和数十万个血小板。 O(100 点/单元和 O(1000) 时间步长的离散化会导致超过一万亿个时空未知数。解决如此规模的问题将需要 5 万核机器。一旦百万核计算平台可用，实现这种可扩展性的计算工具将能够对几微升血液进行直接数值模拟。