Energy Efficient Frequency Scaling on GPUs in Heterogeneous HPC Systems

Kraljic, Karlo; Kerger, Daniel; Schulz, Martin

doi:10.1007/978-3-031-21867-5_1

Cited by 2 publications

(1 citation statement)

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“…A survey of GPU energy-efficiency analysis and optimization techniques [25] refers to various approaches to identify the optimal configuration of the GPU frequency to obtain energy savings, but in each listed case, the paper presents a single configuration for the whole execution of the application, which we refer to as static tuning. Similarly, Kraljic et al [26] identified the execution phases of an analyzed application by sampling GPU energy consumption. Ghazanfar et al [27] trained a neural network model to identify the optimal GPU SM frequency.…”

Section: Energy-aware Hardware Tuning-theoretical Backgroundmentioning

confidence: 99%

Energy-Efficient Implementation of the Lattice Boltzmann Method

Vysocky,

Holzer,

Staffelbach

et al. 2024

Energies

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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 calledwaLBerla. 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.

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Section: Energy-aware Hardware Tuning-theoretical Backgroundmentioning

confidence: 99%