A GPU-based Compressible Combustion Solver for Applications Exhibiting Disparate Space and Time Scales

TL;DR

This paper introduces a high-performance GPU-based compressible combustion solver optimized for multi-GPU systems, addressing memory, workload, and localization challenges to enable efficient simulations of chemically reactive flows with disparate scales.

Contribution

It presents a novel GPU-optimized solver built on AMReX that significantly improves performance and scalability for complex combustion simulations involving stiff chemistry and adaptive meshes.

Findings

Achieved 2-5x performance improvements over initial GPU implementations.

Demonstrated near-ideal weak scaling across 1-96 GPUs.

Significant increase in arithmetic intensity for convection and chemistry routines.

Abstract

High-speed chemically active flows present significant computational challenges due to their disparate space and time scales, where stiff chemistry often dominates simulation time. While modern supercomputing scientific codes achieve exascale performance by leveraging graphics processing units (GPUs), existing GPU-based compressible combustion solvers face critical limitations in memory management, load balancing, and handling the highly localized nature of chemical reactions. To this end, we present a high-performance compressible reacting flow solver built on the AMReX framework and optimized for multi-GPU settings. Our approach addresses three GPU performance bottlenecks: memory access patterns through column-major storage optimization, computational workload variability via a bulk-sparse integration strategy for chemical kinetics, and multi-GPU load distribution for adaptive mesh refinement applications. The solver adapts existing matrix-based chemical kinetics formulations to multigrid contexts. Using representative combustion applications including hydrogen-air detonations and jet in supersonic crossflow configurations, we demonstrate $2-5\times$ performance improvements over initial GPU implementations with near-ideal weak scaling across $1-96$ NVIDIA H100 GPUs. Roofline analysis reveals substantial improvements in arithmetic intensity for both convection ($\sim 10 \times$) and chemistry ($\sim 4 \times$) routines, confirming efficient utilization of GPU memory bandwidth and computational resources.