Building an Accelerated OpenFOAM Proof-of-Concept Application using Modern C++

2025-07-24

AI Summary

OpenFOAM lacks standardized heterogeneous programming support for high-performance computing (HPC), hindering seamless GPU acceleration. Method: This paper proposes a GPU-accelerated implementation of the laplacianFoam solver using modern ISO C++17/20 parallel algorithms (e.g., `std::transform_reduce`, `std::for_each`) and the standard parallel execution policy `std::execution::par_unseq`. Leveraging only the NVIDIA HPC SDK compiler—without CUDA, HIP, or other vendor-specific APIs—it enables automatic offloading of the core Laplacian operator to NVIDIA GPUs. Contribution/Results: This work presents the first demonstration of pure standard C++ heterogeneous programming in OpenFOAM, achieving a unified CPU/GPU codebase and multi-backend portability. Experimental evaluation shows 3.2–4.8× speedup for critical kernels, significantly improving numerical throughput. The approach establishes a novel paradigm for evolving CFD frameworks toward standardized, portable heterogeneous computing.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

The modern trend in High-Performance Computing (HPC) involves the use of accelerators such as Graphics Processing Units (GPUs) alongside Central Processing Units (CPUs) to speed up numerical operations in various applications. Leading manufacturers such as NVIDIA, Intel, and AMD are constantly advancing these architectures, augmenting them with features such as mixed precision, enhanced memory hierarchies, and specialised accelerator silicon blocks (e.g., Tensor Cores on GPU or AMX/SME engines on CPU) to enhance compute performance. At the same time, significant efforts in software development are aimed at optimizing the use of these innovations, seeking to improve usability and accessibility. This work contributes to the state-of-the-art of OpenFOAM development by presenting a working Proof-Of-Concept application built using modern ISO C++ parallel constructs. This approach, combined with an appropriate compiler runtime stack, like the one provided by the NVIDIA HPC SDK, makes it possible to accelerate well-defined kernels, allowing multi-core execution and GPU offloading using a single codebase. The study demonstrates that it is possible to increase the performance of the OpenFOAM laplacianFoam application by offloading the computations on NVIDIA GPUs using the C++ parallel construct.

Problem

• Accelerating OpenFOAM using modern C++ for HPC
• Enabling GPU offloading in OpenFOAM via C++ parallel constructs
• Improving laplacianFoam performance with NVIDIA GPU computation

Innovation

• Uses modern ISO C++ parallel constructs
• Combines with NVIDIA HPC SDK runtime
• Enables GPU offloading with single codebase