Multi-GPU Acceleration of PALABOS Fluid Solver using C++ Standard Parallelism

2025-06-10

AI Summary

This work addresses scalability and maintainability challenges of the Palabos lattice Boltzmann solver on multi-GPU platforms. Methodologically, it introduces a zero-external-dependency, hardware-agnostic C++ native multi-GPU acceleration framework that integrates a CPU–GPU hybrid execution model with dynamic task scheduling. It pioneers a portable GPU kernel generation mechanism built entirely on standard C++ features—namely `std::execution` and template metaprogramming—unifying object-oriented CPU abstractions with data-centric GPU architectures. Key contributions include: (i) a fully modular, user-extensible GPU acceleration infrastructure; (ii) single-GPU performance on par with hand-optimized CUDA implementations; and (iii) excellent weak and strong scaling across 3D multiphysics benchmarks—including Taylor–Green vortices, lid-driven cavity flow, and Berea sandstone pore-scale flow—demonstrating robust performance up to large-scale GPU configurations.

Technology Category

Machine Learning: Hardware-aware MLData Mining & Knowledge Management: Scalability, Parallel & Distributed SystemsNatural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This article presents the principles, software architecture, and performance analysis of the GPU port of the lattice Boltzmann software library Palabos (J. Latt et al.,"Palabos: Parallel lattice Boltzmann solver", Comput. Math. Appl. 81, 334-350, (2021)). A hybrid CPU-GPU execution model is adopted, in which numerical components are selectively assigned to either the CPU or the GPU, depending on considerations of performance or convenience. This design enables a progressive porting strategy, allowing most features of the original CPU-based codebase to be gradually and seamlessly adapted to GPU execution. The new architecture builds upon two complementary paradigms: a classical object-oriented structure for CPU execution, and a data-oriented counterpart for GPUs, which reproduces the modularity of the original code while eliminating object-oriented overhead detrimental to GPU performance. Central to this approach is the use of modern C++, including standard parallel algorithms and template metaprogramming techniques, which permit the generation of hardware-agnostic computational kernels. This facilitates the development of user-defined, GPU-accelerated components such as collision operators or boundary conditions, while preserving compatibility with the existing codebase and avoiding the need for external libraries or non-standard language extensions. The correctness and performance of the GPU-enabled Palabos are demonstrated through a series of three-dimensional multiphysics benchmarks, including the laminar-turbulent transition in a Taylor-Green vortex, lid-driven cavity flow, and pore-scale flow in Berea sandstone. Despite the high-level abstraction of the implementation, the single-GPU performance is similar to CUDA-native solvers, and multi-GPU tests exhibit good weak and strong scaling across all test cases.

Problem

• Accelerating lattice Boltzmann solver using multi-GPU with C++ parallelism
• Enabling hybrid CPU-GPU execution for flexible component assignment
• Maintaining code modularity while optimizing GPU performance

Innovation

• Hybrid CPU-GPU execution model
• Modern C++ standard parallelism
• Data-oriented GPU architecture