Scalable, Cloud-Based Simulations of Blood Flow and Targeted Drug Delivery in Retinal Capillaries

TL;DR

This paper demonstrates that cloud computing can effectively support large-scale, tightly-coupled biological fluid simulations, including blood flow and drug delivery in retinal capillaries, with high scalability and performance.

Contribution

It shows scalable, efficient cloud-based simulations of blood flow and drug delivery using DPD with Mirheo and LAMMPS, achieving high scalability on GPUs and cloud resources.

Findings

Mirheo scales efficiently up to 512 GPUs.

LAMMPS maintains over 90% weak scaling up to 2000 cores.

Cloud computing supports large-scale biological fluid simulations.

Abstract

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.