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

2025-12-01

AI Summary

This study investigates the feasibility of public cloud platforms for large-scale, tightly coupled multiscale biophysical fluid simulations—specifically retinal capillary hemodynamics and artificial bacterial flagella–driven targeted drug delivery. Using dissipative particle dynamics (DPD), we implement massively parallel simulations on cloud infrastructure via the GPU-accelerated Mirheo framework and the CPU-based LAMMPS framework, achieving thousand-core–scale GPU/CPU co-simulation. Our method demonstrates, for the first time, cloud performance competitive with supercomputing: Mirheo achieves excellent weak scaling on up to 512 GPUs, while LAMMPS maintains >90% weak scaling efficiency across 2,000 CPU cores. These results establish a scalable, cost-effective, cloud-native paradigm for high-resolution, geometrically complex, tightly coupled biophysical fluid simulations—overcoming traditional reliance on dedicated supercomputing centers.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Computer Vision: Medical and Biological Imaging
• Machine Learning: Scalability of ML Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Simulating blood flow in retinal capillaries using cloud computing
• Investigating targeted drug delivery via artificial bacterial flagella
• Assessing scalability of DPD simulations on cloud platforms

Innovation

• Cloud-based simulations of blood flow and drug delivery
• Dissipative particle dynamics with Mirheo and LAMMPS frameworks
• Scalable GPU and CPU performance in public cloud