DeepFlame 2.0: A new version for fully GPU-native machine learning accelerated reacting flow simulations under low-Mach conditions

IF 3.4 2区物理与天体物理 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer Physics Communications Pub Date : 2025-03-26 DOI:10.1016/j.cpc.2025.109595

Runze Mao , Xinyu Dong , Xuan Bai , Ziheng Wu , Guanlin Dang , Han Li , Zhi X. Chen

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Abstract

This paper presents DeepFlame v2.0, a significant computational framework upgrade designed for high-performance combustion simulations on GPU-based heterogeneous architectures. The updated version implements a comprehensive CUDA-accelerated architecture incorporating fundamental combustion modelling components, including: implicit/explicit finite volume method (FVM) discretisation schemes, chemical kinetics integrators, thermophysical property models, and subgrid-scale closures for both fluid dynamics and combustion processes. The redesigned code supports diverse boundary conditions and discretisation schemes for broad applicability across combustion configurations. Key performance optimisations integrate advanced CUDA features including data coalescing techniques, CUDA Graphs for kernel scheduling, and NCCL-based multi-GPU communication. Validation studies employing the fully-implicit low-Mach solver demonstrate two-order-of-magnitude acceleration compared to conventional CPU implementations across canonical test cases, while maintaining numerical accuracy.

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DeepFlame 2.0：一个全新的版本，完全gpu原生机器学习加速低马赫条件下的反应流模拟

本文介绍了DeepFlame v2.0，这是一个重要的计算框架升级，专为基于gpu的异构架构的高性能燃烧模拟而设计。更新后的版本实现了一个全面的cuda加速架构，结合了基本的燃烧建模组件，包括：隐式/显式有限体积法（FVM）离散化方案、化学动力学积分器、热物理性质模型，以及流体动力学和燃烧过程的亚网格尺度闭包。重新设计的代码支持不同的边界条件和离散方案，广泛适用于燃烧配置。关键的性能优化集成了先进的CUDA功能，包括数据合并技术、用于内核调度的CUDA图形和基于nccl的多gpu通信。采用全隐式低马赫求解器的验证研究表明，在保持数值精度的同时，与传统CPU实现相比，在规范测试用例中实现了两个数量级的加速。

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来源期刊

Computer Physics Communications 物理-计算机：跨学科应用

CiteScore

12.10

自引率

3.20%

发文量

287

审稿时长

5.3 months

期刊介绍： The focus of CPC is on contemporary computational methods and techniques and their implementation, the effectiveness of which will normally be evidenced by the author(s) within the context of a substantive problem in physics. Within this setting CPC publishes two types of paper. Computer Programs in Physics (CPiP) These papers describe significant computer programs to be archived in the CPC Program Library which is held in the Mendeley Data repository. The submitted software must be covered by an approved open source licence. Papers and associated computer programs that address a problem of contemporary interest in physics that cannot be solved by current software are particularly encouraged. Computational Physics Papers (CP) These are research papers in, but are not limited to, the following themes across computational physics and related disciplines. mathematical and numerical methods and algorithms; computational models including those associated with the design, control and analysis of experiments; and algebraic computation. Each will normally include software implementation and performance details. The software implementation should, ideally, be available via GitHub, Zenodo or an institutional repository.In addition, research papers on the impact of advanced computer architecture and special purpose computers on computing in the physical sciences and software topics related to, and of importance in, the physical sciences may be considered.