# Enabling Simulation of High-Dimensional Micro-Macro Biophysical Models through Hybrid CPU and Multi-GPU Parallelism

## TL;DR

This paper introduces a hybrid CPU and multi-GPU parallel algorithm that significantly accelerates high-dimensional micro-macro biophysical model simulations, enabling higher complexity and resolution in studies of microscale to macroscale biological systems.

## Contribution

The paper presents a novel hybrid CPU and multi-GPU parallelization approach with algorithmic and hardware optimizations for efficient high-dimensional micro-macro model simulations.

## Key findings

- Achieved up to 27x speedup over previous hybrid implementation.
- Achieved up to 540x speedup over single-threaded implementation.
- Enabled higher complexity and resolution in micro-macro simulations.

## Abstract

Micro-macro models provide a powerful tool to study the relationship between microscale mechanisms and emergent macroscopic behavior. However, the detailed microscopic modeling may require tracking and evolving a high-dimensional configuration space at high computational cost. In this work, we present a parallel algorithm for simulation a high-dimensional micro-macro model of a gliding motility assay. We utilize a holistic approach aligning the data residency and simulation scales with the hybrid CPU and multi-GPU hardware. With a combination of algorithmic modifications, GPU optimizations, and scaling to multiple GPUs, we achieve speedup factors of up to 27 over our previous hybrid CPU-GPU implementation and up to 540 over our single-threaded implementation. This approach enables micro-macro simulations of higher complexity and resolution than would otherwise be feasible.