SIMCoV-GPU: Accelerating an Agent-Based Model for Exascale

Modern supercomputers rely on graphics processing units (GPUs) to achieve unprecedented computational capabilities. Multi-node computation with GPUs promises to accelerate and scale simulations dramatically across many domains, and many scientific simulations have been adapted to this new paradigm of supercomputing. However, agent-based models (ABMs) are a class of simulations that to date have seen little development for multinode, multi-GPU supercomputers because their computation flow poses unique algorithmic and communication challenges for effective performance on GPU enabled supercomputers. In particular, many ABMs have irregular and dynamic communication patterns, resource competition that causes race conditions, and unpredictable effects on load balancing. We studied the Spatial Immune Model of Coronavirus, or SIMCoV, as a target ABM application for acceleration. SIMCoV is a large-scale ABM which simulates the spread of viral infection through the epithelial tissue of the lungs and models the immune response with diffusing inflammatory signals and mobile T cell agents. Our multinode, multi-GPU implementation of SIMCoV achieves significant speedups over a competitive baseline version, up to 11.9x with a ratio of 32 CPU cores to a single GPU. The paper describes SIMCoV's GPU-specific optimizations, reports empirical results, and demonstrates effective solutions to the challenges of accelerating ABMs on modern supercomputers.