Multi-GPU thermal lattice Boltzmann simulations using OpenACC and MPI

TL;DR

This paper evaluates a hybrid OpenACC and MPI approach for multi-GPU thermal lattice Boltzmann simulations, demonstrating high performance and scalability up to 16 GPUs.

Contribution

It introduces an optimized hybrid OpenACC and MPI method for efficient multi-GPU thermal lattice Boltzmann simulations, achieving near-maximum performance and excellent scalability.

Findings

Single GPU 2D simulation: 1.93 GLUPS

Single GPU 3D simulation: 1.04 GLUPS

Strong scaling with 16 GPUs: 30.42 GLUPS (2D), 14.52 GLUPS (3D)

Abstract

We assess the performance of the hybrid Open Accelerator (OpenACC) and Message Passing Interface (MPI) approach for multi-graphics processing units (GPUs) accelerated thermal lattice Boltzmann (LB) simulation. The OpenACC accelerates computation on a single GPU, and the MPI synchronizes the information between multiple GPUs. With a single GPU, the two-dimension (2D) simulation achieved 1.93 billion lattice updates per second (GLUPS) with a grid number of $8193^{2}$, and the three-dimension (3D) simulation achieved 1.04 GLUPS with a grid number of $385^{3}$, which is more than 76% of the theoretical maximum performance. On multi-GPUs, we adopt block partitioning, overlapping communications with computations, and concurrent computation to optimize parallel efficiency. We show that in the strong scaling test, using 16 GPUs, the 2D simulation achieved 30.42 GLUPS and the 3D simulation achieved 14.52 GLUPS. In the weak scaling test, the parallel efficiency remains above 99% up to 16 GPUs. Our results demonstrated that, with improved data and task management, the hybrid OpenACC and MPI technique is promising for thermal LB simulation on multi-GPUs.