SOURCE-LINKED INTELLIGENCE

Scalable Multi-GPU Simulation of 3D Multicellular Growth with RNN-Based Workload Balancing

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

Detailed multicellular growth simulations based on subcellular element models (SEMs) can capture complex tissue development, but their element-level interactions impose substantial computational cost. This work presents a scalable multi-GPU framework for 3D multicellular growth simulation that combines GPU acceleration, spatial binning, domain decomposition, and workload-aware partitioning. Cell movement, growth, and division continuously reshape the spatial workload distribution, causing initially balanced partitions to become inefficient over time. To address this, we introduce an RNN-based

Read original source ↗ Open in workspace

recordType

paper

region

Global