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

Source: [A GPU-based Compressible Combustion Solver for Applications Exhibiting Disparate Space and Time Scales](<https://www.sota2.com/research/paper/69019f6c3ef7ccd74e5e38bb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-05T05:03:08.928Z

Authors: Anthony Carreon, Jagmohan Singh, Shivank Sharma, Shuzhi Zhang, Venkat Raman

Published: 2025-10-28T01:50:28.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.23993>)

[PDF](<https://arxiv.org/pdf/2510.23993.pdf>)