Energy efficiency: a Lattice Boltzmann study

TL;DR

This study investigates how different parallelization, precision, and clock speed adjustments in a Lattice Boltzmann fluid dynamics code can reduce energy consumption by 20% with minimal performance loss on high-end GPUs.

Contribution

It provides empirical data on energy savings and performance trade-offs for a GPU-accelerated Lattice Boltzmann code, highlighting optimization strategies for energy efficiency.

Findings

20% energy savings achieved

5% increase in computation time

Reduced thermal stress on GPUs

Abstract

The energy consumption and the compute performance of a fluid dynamic code have been investigated varying parallelization approach, arithmetic precision and clock speed. The code is based on a Lattice Boltzmann approximation, is written in Fortran and was executed on high-end GPUs of Leonardo Booster supercomputer. Tests were conducted on single server nodes (up to 4 GPUs in parallel). Performance metrics like the number of operations per second and energy consumption are reported, to quantify how smart coding approach and system adjustment can contribute to reduction of energy footprint while keeping the scientific throughput almost unaltered or with acceptable level of degradation. Results indicate that this application can be executed with 20% of energy saving and reduced thermal stress, at the cost of 5% more computing time. The paper presents preliminary conclusions, as it is a first step of a larger study dedicated to energy efficiency at scale.