Collaborative Research: Petascale Algorithms for Particulate Flows

项目提出了一个千万亿次可扩展的计算基础设施，用于直接模拟颗粒流动，特别是模拟血小板聚集的时空动态。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、抗凝治疗和脑卒中研究的临床需要。该方法包括两个算法部分：(1)具有动态界面的Stokesian流的积分方程求解；(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？一微升的血液含有数百万红细胞和几十万个血小板。O（100个点/单元）和O（1000）个时间步的离散化导致超过一万亿的时空未知数。解决这种规模的问题需要50k核的机器。一旦拥有百万核计算平台，实现这种可扩展性的计算工具将能够直接对几微升血液进行数值模拟。

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.