可扩展的基于云的视网膜毛细血管中血液流动和靶向药物递送的模拟
Scalable, Cloud-Based Simulations of Blood Flow and Targeted Drug Delivery in Retinal Capillaries
AI总结：
该研究利用云计算实现了视网膜毛细血管中血液流动和靶向药物递送的高效模拟，展示了Mirheo和LAMMPS在大规模模拟中的强可扩展性。
摘要：
我们研究了云计算在复杂几何结构中进行大规模紧密耦合生物流体模拟的能力，这传统上是在超级计算机中心进行的。我们展示了在公共云上进行可扩展和高效的模拟。我们对图像重建的毛细血管中进行介观尺度的血液流动模拟，并通过人工细菌鞭毛（ABFs）研究靶向药物递送。模拟使用了两种软件框架，Mirheo（由我们的团队开发）和LAMMPS。Mirheo在多达512块GPU上表现出显著的弱可扩展性。同样，LAMMPS在纯溶剂以及重建的视网膜毛细血管中的血悬浮液和ABFs上也表现出色的弱可扩展性。特别是，LAMMPS在云上维持超过90%的弱可扩展性，最多支持2000个核心。我们的发现表明，云计算可以支持紧密耦合、大规模的科学模拟，并具有竞争力的性能。
英文摘要：
We investigate the capabilities of cloud computing for large-scale,tightly-coupled simulations of biological fluids in complex geometries, traditionally performed in supercomputing centers. We demonstrate scalable and efficient simulations in the public cloud. We perform meso-scale simulations of blood flow in image-reconstructed capillaries, and examine targeted drug delivery by artificial bacterial flagella (ABFs). The simulations deploy dissipative particle dynamics (DPD) with two software frameworks, Mirheo (developed by our team) and LAMMPS. Mirheo exhibits remarkable weak scalability for up to 512 GPUs. Similarly, LAMMPS demonstrated excellent weak scalability for pure solvent as well as for blood suspensions and ABFs in reconstructed retinal capillaries. In particular, LAMMPS maintained weak scaling above 90% on the cloud for up to 2,000 cores. Our findings demonstrate that cloud computing can support tightly coupled, large-scale scientific simulations with competitive performance.
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