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

TL;DR

This paper details a GPU-accelerated version of the Palabos lattice Boltzmann solver using modern C++, enabling seamless CPU-GPU hybrid execution, modular development, and demonstrating high performance and scalability on multiple GPUs.

Contribution

Introduces a hybrid CPU-GPU architecture for Palabos with modern C++ techniques, enhancing performance, modularity, and ease of porting existing features to GPU.

Findings

Single-GPU performance comparable to CUDA-native solvers

Good weak and strong scaling across multiple GPUs

Successful validation with multiphysics benchmarks

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.