Predictive Modeling for Job Power Consumption in HPC Systems

Borghesi, Andrea; Bartolini, Andrea; Lombardi, Michele; Milano, Michela; Benini, Luca

doi:10.1007/978-3-319-41321-1_10

Cited by 37 publications

(25 citation statements)

References 19 publications

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“…Tools for measuring and modeling energy-efficiency of existing systems are important. Borghesi et al [26] use machine-learning to predict the power consumption of typical HPC workloads and show promising results. Mair et al [120] extensively analyze trends in the TOP500.…”

Section: Modeling and Toolsmentioning

confidence: 99%

The Landscape of Exascale Research

et al. 2020

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The next generation of supercomputers will break the exascale barrier. Soon we will have systems capable of at least one quintillion (billion billion) floating-point operations per second (10 18 FLOPS). Tremendous amounts of work have been invested into identifying and overcoming the challenges of the exascale era. In this work, we present an overview of these efforts and provide insight into the important trends, developments, and exciting research opportunities in exascale computing. We use a three-stage approach in which we (1) discuss various exascale landmark studies, (2) use data-driven techniques to analyze the large collection of related literature, and (3) discuss eight research areas in depth based on influential articles. Overall, we observe that great advancements have been made in tackling the two primary exascale challenges: energy efficiency and fault tolerance. However, as we look forward, we still foresee two major concerns: the lack of suitable programming tools and the growing gap between processor performance and data bandwidth (i.e., memory, storage, networks). Although we will certainly reach exascale soon, without additional research, these issues could potentially limit the applicability of exascale computing.

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Section: Modeling and Toolsmentioning

confidence: 99%

The Landscape of Exascale Research

et al. 2020

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“…In this work, the SVR method was employed to predict power of computing components, from which we then obtained system‐level power. Recently, another method for predicting power solely from workload data was introduced; however, the authors concentrate on predicting mean power for jobs and not full power profiles like we do.…”

Section: Related Workmentioning

confidence: 99%

A data‐driven approach to modeling power consumption for a hybrid supercomputer

Sîrbu

Babaoğlu

2018

Concurrency and Computation

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Summary Power consumption of current High Performance Computing systems has to be reduced by at least one order of magnitude before they can be scaled up towards ExaFLOP performance. While we can expect novel hardware technologies and architectures to contribute towards this goal, significant advances have to come also from software technologies such as proactive and power‐aware scheduling, resource allocation, and fault‐tolerant computing. Development of these software technologies in turn relies heavily on our ability to model and accurately predict power consumption in large computing systems. In this paper, we present a data‐driven model of power consumption for a hybrid supercomputer (which held the top spot in the Green500 ranking in June 2013) that combines CPU, GPU, and MIC technologies to achieve high levels of energy efficiency. Our model takes as input workload characteristics—the number and location of resources that are used by each job at a certain time—and calculates a predicted power consumption at the system level. The model is application‐code‐agnostic and is based solely on a data‐driven predictive approach, where log data describing the past jobs in the system are employed to estimate future power consumption. For this, three different model components are developed and integrated. The first employs support vector regression to predict power usage for jobs before these are started. The second uses a simple heuristic to predict the length of jobs, again before they start. The two predictions are then combined to estimate power consumption due to the job at all computational elements in the system. The third component is a linear model that takes as input the power consumption at the computing units and predicts system‐wide power consumption. Our method achieves highly‐accurate predictions starting solely from workload information and user histories. The model can be applied to power‐aware scheduling and power capping: alternative workload dispatching configurations can be evaluated from a power perspective and more efficient ones can be selected. The methodology outlined here can be easily adapted to other HPC systems where the same types of log data are available.

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“…Neural Network has been used to minimise the cooling energy consumption [1] of a server. Borghesi et al [8] have proposed a machine learning approach, which relies on resource requests from a user and an application to estimate power consumption of HPC workloads. Their model was able to handle cases when CPUs were not fully utilised.…”

Section: Related Workmentioning

confidence: 99%

A Study on Cross-Architectural Modelling of Power Consumption Using Neural Networks

2018

JSFI

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On the path to Exascale, the goal of High Performance Computing (HPC) to achieve maximum performance becomes the goal of achieving maximum performance under strict power constraint. Novel approaches to hardware and software co-design of modern HPC systems have to be developed to address such challenges. In this paper, we study prediction of power consumption of HPC systems using metrics obtained from hardware performance counters. We argue that this methodology is portable across different micro-architecture implementations and compare results obtained on Intel R 64, IBM R POWER TM and Cavium ThunderX R ARMv8 microarchitectures. We discuss optimal number and type of hardware performance counters required to accurately predict power consumption. We compare accuracy of power predictions provided by models based on Linear Regression (LR) and Neural Networks (NN). We find that the NN-based model provides better accuracy of predictions than the LR model. We also find, that presently it is not yet possible to predict power consumption on a given microarchitecture using data obtained on a different microarchitecture. Results of our work can be used as a starting point for developing unified, cross-architectural models for predicting power consumption.

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Predictive Modeling for Job Power Consumption in HPC Systems

Cited by 37 publications

References 19 publications

The Landscape of Exascale Research

The Landscape of Exascale Research

A data‐driven approach to modeling power consumption for a hybrid supercomputer

A Study on Cross-Architectural Modelling of Power Consumption Using Neural Networks

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