SCALE-TRACK: Asynchronous Euler-Lagrange particle tracking on heterogeneous computing architecture
This enables high-fidelity simulations on local workstations and pushes limits on HPC systems for researchers in computational fluid dynamics.
The paper tackles the computational expense of Euler-Lagrange simulations for multiphase flows by introducing SCALE-TRACK, a scalable particle tracking algorithm that achieved tracking of up to 256 billion particles on 256 GPUs with excellent scaling.
Euler-Lagrange (EL) simulations provide a direct and robust framework for modeling disperse multiphase flows. However, they are computationally expensive. While various approaches have attempted to leverage heterogeneous computing architectures, they have encountered scalability limitations. We present SCALE-TRACK, a scalable two-way coupled EL particle tracking algorithm, designed to exploit heterogeneous exascale computing environments. With asynchronous coupling, cache-friendly data structures, and chunk-based partitioning, we address key limitations of existing EL implementations. Validations against an analytical solution and a conventional EL implementation demonstrate the accuracy of the proposed algorithms. On a local workstation, we simulated 1.4 billion particles in a test case featuring a single graphics processing unit (GPU). Scaling runs on an HPC (high-performance computing) cluster show excellent strong and weak scaling, with up to 256 billion particles being tracked on up to 256 GPUs. This represents a significant advancement for EL simulations, enabling high-fidelity simulations on local workstations and pushing the limits on HPC systems. The software is released as open source and is publicly available.