Energy and Power Aware Job Scheduling and Resource Management: Global Survey — Initial Analysis

Maiterth, Matthias; Koenig, Gregory A.; Pedretti, Kevin; Jana, Siddhartha; Bates, Natalie; Borghesi, Andrea; Montoya, Dave; Bartolini, Andrea; Puzovic, Milos

doi:10.1109/ipdpsw.2018.00111

Cited by 31 publications

(20 citation statements)

References 25 publications

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“…Managing power has become an important issue in HPC. A survey on the techniques developed in nine of the TOP500 HPC centers for improving energy efficient is presented by Maiterth et al [38]. They identify several emerging techniques, some with common characteristics.…”

Section: Power-aware Budgeting and Schedulingmentioning

confidence: 99%

Power efficient job scheduling by predicting the impact of processor manufacturing variability

Chasapis

Moretó

Schulz

et al. 2019

Proceedings of the ACM International Conference on Supercomputing

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Modern CPUs suffer from performance and power consumption variability due to the manufacturing process. As a result, systems that do not consider such variability caused by manufacturing issues lead to performance degradations and wasted power. In order to avoid such negative impact, users and system administrators must actively counteract any manufacturing variability. In this work we show that parallel systems benefit from taking into account the consequences of manufacturing variability when making scheduling decisions at the job scheduler level. We also show that it is possible to predict the impact of this variability on specific applications by using variabilityaware power prediction models. Based on these power models, we propose two job scheduling policies that consider the effects of manufacturing variability for each application and that ensure that power consumption stays under a systemwide power budget. We evaluate our policies under different power budgets and traffic scenarios, consisting of both singleand multi-node parallel applications, utilizing up to 4096 cores in total. We demonstrate that they decrease job turnaround time, compared to contemporary scheduling policies used on production clusters, up to 31% while saving up to 5.5% energy. CCS CONCEPTS • Computer systems organization → Parallel architectures; • Hardware → Power estimation and optimization;

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Section: Power-aware Budgeting and Schedulingmentioning

confidence: 99%

Power efficient job scheduling by predicting the impact of processor manufacturing variability

Chasapis

Moretó

Schulz

et al. 2019

Proceedings of the ACM International Conference on Supercomputing

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“…The importance of resource management in HPC, HPC in the Cloud and Cloud Computing and the relevant solutions were discussed in several surveys. The Energy-Efficient High-Performance-Computing Working-Group (EE HPC WG) Energy and Power Aware Job Scheduling and Resource Management (EPA JSRM) team conducted comprehensive interviews and identified the energy-and power-aware scheduling and resource management solutions that are used on the major HPC sites [12]. This report showed different trends in power consumption minimisation, such as the job-killing when the power limit is exceeded, the analysis of information obtained from the power system monitoring and the forecast of energy consumption for each job.…”

Section: Relevant Surveysmentioning

confidence: 99%

Survey of Memory Management Techniques for HPC and Cloud Computing

Pupykina

Agosta

2019

IEEE Access

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The emergence of new classes of HPC applications and usage models, such as real-time HPC and cloud HPC, coupled with the increasingly heterogeneous nature of HPC architectures, requires a renewed investigation of memory management solutions. Traditionally, memory is shared by an operating system using segmentation and paging techniques. At the same time, new classes of applications require Quality of Service (QoS) guarantees. As such, the typical practice of reserving a subset of the supercomputer to a single application becomes less attractive, leading to the exploration of cloud technologies. In this context, a viable scenario is that of multiple applications, with different QoS levels, coexisting on the same deeply heterogeneous HPC infrastructure and sharing resources. However, for this scenario to succeed in practice, resources, including memory, need to be allocated with a vision that includes both the application requirements and the current and future state of the overall system. In this survey, challenges of memory management in HPC and Cloud Computing, different memory management systems and optimisation techniques to increase memory utilisation are discussed in detail. INDEX TERMS Clouds, high performance computing, memory management, resource management. ANNA PUPYKINA received the higher education degree in data processing and control systems engineering from Samara State Aerospace University, Russia, in 2009. She is currently pursuing the Ph.D. degree with the Politecnico di Milano, under the supervision of Prof. G. Agosta. She was with Russian State Humanitarian University (RSUH), Togliatti, and also with the Department of Applied Mathematics and Informatics, Togliatti State University (TSU). Her main research interests include runtime management algorithms and programming model extensions for deeply heterogeneous systems. GIOVANNI AGOSTA received the Laurea degree in computer engineering and the Ph.D. degree in information technology from the Politecnico di Milano, Italy, in 2000 and 2004, respectively. He is currently a tenured Researcher. He has authored more than 80 articles in international journals, conferences, and workshops. His main research interest includes compiler technology. He was a recipient of four HiPEAC Awards and three Best Paper Awards. He has been involved in eight European funded projects, since 2010, holding workpackage and task leader responsibilities. He is a Full Member of the HiPEAC Network and the Organizer of two workshops colocated with the HiPEAC Conference.

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“…With the end of Dennard's scaling [1], [2], the last decade has seen a progressive increase of the power density required to operate each new processor generation at its maximum performance. Supercomputing installations have suffered from this power density increase, which over the years has pushed up the energy provisioning and cooling costs [3]- [5]. To mitigate these effects processors in this market segment, embed dedicated HW resources to control the power consumption dynamically, prevent thermal hazards, and increase the energy efficiency of the computation.…”

Section: Introductionmentioning

confidence: 99%

“…More information can be found on the PULP platform website (https://pulp-platform.org//implementation.html).3 https://greenwaves-technologies.com /ai processor gap8/ 4 https://content.riscv.org/wp-content /uploads/2018/05 /16.10-16.25-Seiji-Munetoh-IBM-Japan.pdf5 https://content.riscv.org /wp-content/uploads/2018 /05/13.15-13.30-matt-Cockrell.pdf6 https://content.riscv.org /wp-content /uploads/2018/05 /11.20-11.45-Rob-Oshana-NXP.pdf7 https://www.ceva-dsp.com/wp-content /uploads/2018/05 /Ceva-First-to-Launch-802.11ax-IP-CEVA.pdf…”

mentioning

confidence: 99%