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

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.

本工作介绍了DeepFlame v2.0——一款面向基于图形处理器（GPU）的异构架构的高性能燃烧模拟专用计算框架的重大升级版本。该更新版本搭建了一套完整的CUDA加速架构，集成了燃烧模拟的核心建模组件，涵盖：隐式/显式有限体积法（Finite Volume Method, FVM）离散格式、化学动力学积分器、热物理性质模型，以及适配流体动力学与燃烧过程的亚网格尺度封闭模型。该重新设计的代码框架支持多种边界条件与离散格式，可广泛适配各类燃烧工况。核心性能优化集成了多项先进CUDA特性，包括数据合并技术、用于核函数调度的CUDA图，以及基于NCCL的多GPU通信机制。采用全隐式低马赫数求解器的验证研究表明，在各类标准基准测试算例中，该框架相较传统CPU实现方案可实现两个数量级的性能加速，同时保持数值精度不变。

搜集汇总

背景与挑战

背景概述

DeepFlame 2.0是一个专为GPU异构架构设计的高性能燃烧模拟计算框架，采用CUDA加速技术整合了多种燃烧模型组件和性能优化方案，相比传统CPU实现可获得两个数量级的加速效果。该数据集属于计算物理领域，涉及高性能计算、机器学习和计算流体动力学等多个交叉学科。