1. Sustaining Performance While Reducing Energy Consumption: A Control Theory Approach
- Author
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Raphaël Bleuse, Eric Rutten, Valentin Reis, Sophie Cerf, Swann Perarnau, Control for Autonomic computing systems (CTRL-A ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Argonne National Laboratory [Lemont] (ANL), Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr)., Argonne National Laboratory's work was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computer Research, under Contract DE-AC02-06CH11357. This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration., This research is partially supported by the NCSA-Inria-ANL-BSC-JSC-Riken-UTK Joint-Laboratory for Extreme Scale Computing (JLESC, https://jlesc.github.io/)., Grid'5000, GRID5000, JLESC - Joint Laboratory for Extreme Scale Computing, and JLESC
- Subjects
FOS: Computer and information sciences ,010302 applied physics ,Computer science ,Node (networking) ,Power regulation ,02 engineering and technology ,Energy consumption ,01 natural sciences ,020202 computer hardware & architecture ,Resource (project management) ,Computer Science - Distributed, Parallel, and Cluster Computing ,Control theory ,0103 physical sciences ,Dynamic demand ,HPC ,0202 electrical engineering, electronic engineering, information engineering ,Benchmark (computing) ,[INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY] ,Resource management ,Distributed, Parallel, and Cluster Computing (cs.DC) ,[INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC] ,Performance per watt - Abstract
Production high-performance computing systems continue to grow in complexity and size. As applications struggle to make use of increasingly heterogeneous compute nodes, maintaining high efficiency (performance per watt) for the whole platform becomes a challenge. Alongside the growing complexity of scientific workloads, this extreme heterogeneity is also an opportunity: as applications dynamically undergo variations in workload, due to phases or data/compute movement between devices, one can dynamically adjust power across compute elements to save energy without impacting performance. With an aim toward an autonomous and dynamic power management strategy for current and future HPC architectures, this paper explores the use of control theory for the design of a dynamic power regulation method. Structured as a feedback loop, our approach-which is novel in computing resource management-consists of periodically monitoring application progress and choosing at runtime a suitable power cap for processors. Thanks to a preliminary offline identification process, we derive a model of the dynamics of the system and a proportional-integral (PI) controller. We evaluate our approach on top of an existing resource management framework, the Argo Node Resource Manager, deployed on several clusters of Grid'5000, using a standard memory-bound HPC benchmark., The datasets and code generated and analyzed during the current studyare available in the Figshare repository: https://doi.org/10.6084/m9.figshare.14754468[5]
- Published
- 2021