[en] High Performance Computing (HPC) is nowadays a strategic asset required to sustain the surging demands for massive processing and data-analytic capabilities. In practice, the effective management of such large scale and distributed computing infrastructures is left to a Resource and Job Management System (RJMS). This essential middleware component is responsible for managing the computing resources, handling user requests to allocate resources while providing an optimized framework for starting, executing and monitoring jobs on the allocated resources. The University of Luxembourg has been operating for 15 years a large academic HPC facility which relies since 2017 on the Slurm RJMS introduced on top of the flagship cluster Iris. The acquisition of a new liquid-cooled supercomputer named Aion which was released in 2021 was the occasion to deeply review and optimize the seminal Slurm configuration, the resource limits defined and the sustaining fairsharing algorithm.
This paper presents the outcomes of this study and details the implemented RJMS policy. The impact of the decisions made over the supercomputers workloads is also described. In particular, the performance evaluation conducted highlights that when compared to the seminal configuration, the described and implemented environment brought concrete and measurable improvements with regards the platform utilization (+12.64%), the jobs efficiency (as measured by the average Wall-time Request Accuracy, improved by 110.81%) or the management and funding (increased by 10%). The systems demonstrated sustainable and scalable HPC performances, and this effort has led to a negligible penalty on the average slowdown metric (response time normalized by runtime), which was increased by 0.59% for job workloads covering a complete year of exercise. Overall, this new setup has been in production for 18 months on both supercomputers and the updated model proves to bring a fairer and more satisfying experience to the end users. The proposed configurations and policies may help other HPC centres when designing or improving the RJMS sustaining the job scheduling strategy at the advent of computing capacity expansions.
Research center :
ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Computer science
Author, co-author :
Varrette, Sébastien ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Kieffer, Emmanuel ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Pinel, Frederic
External co-authors :
yes
Language :
English
Title :
Optimizing the Resource and Job Management System of an Academic HPC and Research Computing Facility
Publication date :
July 2022
Event name :
21st IEEE Intl. Symp. on Parallel and Distributed Computing (ISPDC'22)
Event place :
Basel, Switzerland
Event date :
July 11-13, 2022
Audience :
International
Main work title :
21st IEEE Intl. Symp. on Parallel and Distributed Computing (ISPDC'22)
S. Varrette, H. Cartiaux, S. Peter, E. Kieffer, T. Valette, and A. Olloh, “Management of an Academic HPC & Research Computing Facility: The ULHPC Experience 2.0,” in Proc. of the 6th ACM High Performance Computing and Cluster Technologies Conf. (HPCCT 2022). Fuzhou, China: Association for Computing Machinery (ACM), Jul. 2022.
M. A. Jette, A. B. Yoo, and M. Grondona, “SLURM: Simple Linux Utility for Resource Management,” in Proc. of Job Scheduling Strategies for Parallel Processing (JSSPP’03). Springer-Verlag, 2002, pp. 44–60.
Y. Georgiou and M. Hautreux, “Evaluating scalability and efficiency of the resource and job management system on large HPC clusters,” in Workshop on Job Scheduling Strategies for Parallel Processing. Springer, 2012, pp. 134–156.
M. Rocklin, “Dask: Parallel computation with blocked algorithms and task scheduling,” in Proceedings of the 14th Python in Science Conference, K. Huff and J. Bergstra, Eds., 2015, pp. 130 – 136.
N. Capit, G. Da Costa, Y. Georgiou, G. Huard, C. Martin, G. Mounié, P. Neyron, and O. Richard, “A batch scheduler with high level components,” in CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005., vol. 2.
S. Varrette, P. Bouvry, H. Cartiaux, and F. Georgatos, “Management of an Academic HPC Cluster: The UL Experience,” in Proc. of the 2014 Intl. Conf. on High Performance Computing & Simulation (HPCS 2014). Bologna, Italy: IEEE, July 2014, pp. 959–967.
U. Lublin and D. G. Feitelson, “The Workload on Parallel Supercomputers: Modeling the Characteristics of Rigid Jobs,” J. Parallel Distrib. Comput., vol. 63, no. 11, p. 1105–1122, nov 2003.
D. G. Feitelson, Workload Modeling for Computer Systems Performance Evaluation, 1st ed. USA: Cambridge University Press, 2015.
“LLNL slurm tutorial and configuration,” [online] hpc.llnl.gov/banks-jobs/running-jobs/slurm.
“Slurm configuration on the nilfheim cluster,” [online] wiki.fysik.dtu.dk/niflheim/Slurm_configuration.
N. A. Simakov, R. L. DeLeon, M. D. Innus, M. D. Jones, J. P. White, S. M. Gallo, A. K. Patra, and T. R. Furlani, “Slurm Simulator: Improving Slurm Scheduler Performance on Large HPC Systems by Utilization of Multiple Controllers and Node Sharing,” in Proc. of the ACM Practice and Experience on Advanced Research Computing (PEARC’18), 2018.
A. Jokanovic, M. D’Amico, and J. Corbalan, “Evaluating SLURM Simulator with Real-Machine SLURM and Vice Versa,” in Intl. Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS’18). Los Alamitos, CA, USA: IEEE Computer Society, Nov 2018, pp. 72–82.
M. Martinasso, M. Gila, M. Bianco, S. R. Alam, C. McMurtrie, and T. Schulthess, “RM-Replay: A High-Fidelity Tuning, Optimization and Exploration Tool for Resource Management,” in Proc. of the Intl. Conf. for High Performance Computing, Networking, Storage, and Analysis (SC’18). IEEE Press, 2018.