Collaborative Research: Petascale Algorithms for Particulate Flows

该项目提出了一个petaflop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。对血液微循环和血小板流变学的更好理解将影响血栓形成风险评估、抗凝治疗和卒中研究的临床需求。所提出的方法包括两个算法组件：（1）积分方程求解器与动态接口的斯托克斯流;（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）和几十万个血小板。O（100点/细胞和O（1000）时间步长的离散化导致超过万亿个时空未知数。解决这样规模的问题需要50 K核的机器。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直接数值模拟几微升的血液。

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.