AccaSim: a customizable workload management simulator for job dispatching research in HPC systems

Galleguillos, Cristian; Kızıltan, Zeynep; Netti, Alessio; Soto, Ricardo

doi:10.1007/s10586-019-02905-5

Cited by 10 publications

(8 citation statements)

References 37 publications

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“…16,17 In relation to system hardware: collection of performance counters and sensors, 18 node level anomaly detection, [19][20][21] hardware failure predictions, 22 CPU frequency tuning. [23][24][25] Software side includes efforts related to system software and middleware, like jitter sources identification, 26 various simulators for schedulers, 27,28 energy aware scheduling, 25,29 application fingerprinting, 30,31 jobs resource usage predictions 32,33 and auto-tuning of applications. 34,35 Examples of the holistic approach, when the above mentioned components are considered together to be the PowerStack project.…”

Section: Related Workmentioning

confidence: 99%

Towards bespoke optimizations of energy efficiency in HPC environments

Tracey,

Elisseev,

Smyrnakis

et al. 2023

Applied AI Letters

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We present bespoke energy efficiency optimizations in high performance computing (HPC) environments using holistic approach to data collection, analysis and proactive management of resources and workloads. Our solution has three major components: (i) platform for collecting, storing and processing data from multiple sources across hardware and software stacks, (ii) collections of regression machine learning (ML) algorithms for making workloads classifications and energy usage predictions, (iii) agent‐based decision‐making framework for delivering control decisions to middle‐ware and infrastructure thus supporting real time or near real energy efficiency optimizations. We will provide some concrete examples of using our proposed approach in HPC environment.

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Section: Related Workmentioning

confidence: 99%

Towards bespoke optimizations of energy efficiency in HPC environments

Tracey,

Elisseev,

Smyrnakis

et al. 2023

Applied AI Letters

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“…Those include, e.g., the recent AccaSim or Qsim. AccaSim is freely available library for Python, thus compatible with any major operating system, and executable on a wide range of computers thanks to its lightweight installation and light memory footprint [5]. Qsim is an event-driven scheduling simulator for Cobalt, which is an HPC job management suite supporting compute clusters of the IBM BlueGene series [19].…”

Section: Related Workmentioning

confidence: 99%

“…The aforementioned list is not exhaustive, containing only widely used/recent toolkits and simulators. More detailed survey can be found in [5,9].…”

Section: Related Workmentioning

confidence: 99%

Alea – Complex Job Scheduling Simulator

Klusáček¹,

Soysal²,

Suter³

2020

Lecture Notes in Computer Science

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Using large computer systems such as HPC clusters up to their full potential can be hard. Many problems and inefficiencies relate to the interactions of user workloads and system-level policies. These policies enable various setup choices of the resource management system (RMS) as well as the applied scheduling policy. While expert's assessment and well known best practices do their job when tuning the performance, there is usually plenty of room for further improvements, e.g., by considering more efficient system setups or even radically new scheduling policies. For such potentially damaging modifications it is very suitable to use some form of a simulator first, which allows for repeated evaluations of various setups in a fully controlled manner. This paper presents the latest improvements and advanced simulation capabilities of the Alea job scheduling simulator that has been actively developed for over 10 years now. We present both recently added advanced simulation capabilities as well as a set of real-life based case studies where Alea has been used to evaluate major modifications of real HPC and HTC systems.

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“…Paper [25] presents an AccaSim model of general JMS, which is compared to Batsim and Alea. Comparison with ScSF is not done due to high system requirements and complex configuration.…”

Section: Related Workmentioning

confidence: 99%

Simulator of a Supercomputer Job Management System as a Scientific Service

Savin

Shabanov

Lyakhovets

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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Job management system (JMS) is an important part of any supercomputer. JMS creates a schedule for launching jobs of different users. Actual job management systems are complex software systems with a number of settings. These settings have a significant impact on various JMS metrics, such as supercomputer resources utilization, mean waiting time of a job in queue, and others. Various JMS simulators are widely used to study the influence of JMS settings or modifications, new scheduling algorithms, jobs input stream parameters or available computing resources for JMS efficiency metrics. The article presents the comparative analysis results of the actual JMS simulators (Alea, ScSF, Batsim, AccaSim, Slurm simulator) and their application areas. The authors consider new ways to use the JMS simulator as a scientific service for researchers. With such a service, the researchers are able to study various hypotheses about JMS efficiency, algorithms or parameters. This gives the folowing: (1) research is performed on the service side around the clock, (2) the simulator accuracy or adequacy is provided by the service, (3) the research results reproducibility is ensured, and the simulator-as-a-service becomes a single entry point for the researchers.

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AccaSim: a customizable workload management simulator for job dispatching research in HPC systems

Cited by 10 publications

References 37 publications

Towards bespoke optimizations of energy efficiency in HPC environments

Towards bespoke optimizations of energy efficiency in HPC environments

Alea – Complex Job Scheduling Simulator

Simulator of a Supercomputer Job Management System as a Scientific Service

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