Energy-Efficient Implementation of the Lattice Boltzmann Method

Abstract

Energy costs are now one of the leading criteria when procuring new computing hardware. Until recently, developers and users focused only on pure performance in terms of time-to-solution. Recent advances in energy-aware runtime systems render the optimization of both runtime and energy-to-solution possible by including hardware tuning depending on the application’s workload. This work presents the impact that energy-sensitive tuning strategies have on a state-of-the-art high-performance computing code based on the lattice Boltzmann approach called waLBerla. We evaluate both CPU-only and GPU-accelerated supercomputers. This paper demonstrates that, with little user intervention, when using the energy-efficient runtime system called MERIC, it is possible to save a significant amount of energy while maintaining performance.

Keywords

• HPC
• GPU accelerators
• DVFS
• MERIC
• energy-aware runtime system
• dynamic resource management