Cross-platform programming model for many-core lattice Boltzmann simulations

TL;DR

This paper introduces a hardware-agnostic, high-performance implementation strategy for lattice Boltzmann simulations using C++17 Parallel Algorithms, enabling efficient execution on both CPUs and GPUs without vendor-specific code.

Contribution

It presents a novel, portable approach for LB simulations that achieves state-of-the-art performance across diverse many-core platforms using standard C++17 features.

Findings

Achieves comparable performance on CPUs and GPUs with a single codebase

Demonstrates versatility across six implementation schemes and nine collision models

Shows modern CPUs can narrow the performance gap with GPUs

Abstract

We present a novel, hardware-agnostic implementation strategy for lattice Boltzmann (LB) simulations, which yields massive performance on homogeneous and heterogeneous many-core platforms. Based solely on C++17 Parallel Algorithms, our approach does not rely on any language extensions, external libraries, vendor-specific code annotations, or pre-compilation steps. Thanks in particular to a recently proposed GPU back-end to C++17 Parallel Algorithms, it is shown that a single code can compile and reach state-of-the-art performance on both many-core CPU and GPU environments for the solution of a given non trivial fluid dynamics problem. The proposed strategy is tested with six different, commonly used implementation schemes to test the performance impact of memory access patterns on different platforms. Nine different LB collision models are included in the tests and exhibit good performance, demonstrating the versatility of our parallel approach. This work shows that it is less than ever necessary to draw a distinction between research and production software, as a concise and generic LB implementation yields performances comparable to those achievable in a hardware specific programming language. The results also highlight the gains of performance achieved by modern many-core CPUs and their apparent capability to narrow the gap with the traditionally massively faster GPU platforms. All code is made available to the community in form of the open-source project "stlbm", which serves both as a stand-alone simulation software and as a collection of reusable patterns for the acceleration of pre-existing LB codes.