Automatic Software Tuning of Parallel Programs for Energy-Aware Executions

  • Sébastien VarretteEmail author
  • Frédéric PinelEmail author
  • Emmanuel KiefferEmail author
  • Grégoire DanoyEmail author
  • Pascal BouvryEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12044)


For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to the type and characteristics of workload that the device is running. For this reason, this paper presents an automatic software tuning method for parallel program generation able to adapt and exploit the hardware features available on a target computing system such as an HPC facility or a cloud system in a better way than traditional compiler infrastructures. We propose a search based approach combining both exact methods and approximated heuristics evolving programs in order to find optimized configurations relying on an ever-increasing number of tunable knobs i.e., code transformation and execution options (such as the number of OpenMP threads and/or the CPU frequency settings). The main objective is to outperform the configurations generated by traditional compiling infrastructures for selected KPIs i.e., performance, energy and power usage (for both for the CPU and DRAM), as well as the runtime. First experimental results tied to the local optimization phase of the proposed framework are encouraging, demonstrating between 8% and 41% improvement for all considered metrics on a reference benchmarking application (i.e., Linpack). This brings novel perspectives for the global optimization step currently under investigation within the presented framework, with the ambition to pave the way toward automatic tuning of energy-aware applications beyond the performance of the current state-of-the-art compiler infrastructures.


HPC Performance evaluation Energy efficiency Compiler infrastructure Automatic tuning MOEA Hyper-parameter optimization 



The experiments presented in this paper were carried out using the HPC facilities of the University of Luxembourg [11] – see


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science (DCS)University of LuxembourgLuxembourg CityLuxembourg
  2. 2.Interdisciplinary Centre for Security Reliability and Trust (SnT)University of LuxembourgLuxembourg CityLuxembourg

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