Towards a Predictive Energy Model for HPC Runtime Systems Using Supervised Learning

Ozer, Gence; Garg, Sarthak; Davoudi, Neda; Poerwawinata, Gabrielle; Maiterth, Matthias; Netti, Alessio; Tafani, Daniele

doi:10.1007/978-3-030-48340-1_48

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…3) Power: This segment was collected from a single compute node in the CooLMUC-3 5 production HPC system at LRZ, while running several single-node OpenMP applications each under two possible input configurations. The segment contains both node-level and CPU core-level data and is used to perform power consumption prediction: this enables system tuning (e.g., via changes in CPU frequency) to optimize performance based on predicted workloads [7].…”

Section: B Structure Of the Dataset Collectionmentioning

confidence: 99%

“…As shown in Figure 1, ODA can be applied at any level of a data center, from the building infrastructure down to the compute node level [6], with different requirements in terms of data sources, time scales, resource footprints and modes of operation: an ODA algorithm tuning CPU frequencies with the aim of optimizing energy efficiency [7] will run in compute nodes (in-band) and will act at very fine time scales (i.e., milliseconds), with strict requirements in terms of resource footprint and overhead on applications [8]. On the other hand, an ODA algorithm optimizing a cooling system's inlet water temperature [9] will operate on a system-wide basis (out-ofband) and at a coarse scale (i.e., minutes), but will need to cope with the large volume of sensor data from the full breadth of the system.…”

Section: Introductionmentioning

confidence: 99%

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Correlation-wise Smoothing: Lightweight Knowledge Extraction for HPC Monitoring Data

Netti

Tafani

Ott

et al. 2021

2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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Modern High-Performance Computing (HPC) and data center operators rely more and more on data analytics techniques to improve the efficiency and reliability of their operations. They employ models that ingest time-series monitoring sensor data and transform it into actionable knowledge for system tuning: a process known as Operational Data Analytics (ODA). However, monitoring data has a high dimensionality, is hardware-dependent and difficult to interpret. This, coupled with the strict requirements of ODA, makes most traditional data mining methods impractical and in turn renders this type of data cumbersome to process. Most current ODA solutions use ad-hoc processing methods that are not generic, are sensible to the sensors' features and are not fit for visualization.In this paper we propose a novel method, called Correlationwise Smoothing (CS), to extract descriptive signatures from timeseries monitoring data in a generic and lightweight way. Our CS method exploits correlations between data dimensions to form groups and produces image-like signatures that can be easily manipulated, visualized and compared. We evaluate the CS method on HPC-ODA, a collection of datasets that we release with this work, and show that it leads to the same performance as most state-of-the-art methods while producing signatures that are up to ten times smaller and up to ten times faster, while gaining visualizability, portability across systems and clear scaling properties.

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Section: B Structure Of the Dataset Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correlation-wise Smoothing: Lightweight Knowledge Extraction for HPC Monitoring Data

Netti

Tafani

Ott

et al. 2021

2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Self Cite

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“…This case study serves to show the effectiveness of Wintermute in such a scenario, where data is collected in-band, at a fine time scale, and is immediately re-used for control purposes. The model represents an online implementation of the one proposed by Ozer et al [43].…”

Section: B Case Study 1 -Power Consumption Predictionmentioning

confidence: 99%

DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems

Netti,

Mueller,

Guillen

et al. 2019

Preprint

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The complexity of today's HPC systems increases as we move closer to the exascale goal, raising concerns about their sustainability. In an effort to improve their efficiency and effectiveness, more and more HPC installations are experimenting with fine-grained monitoring coupled with Operational Data Analytics (ODA) to drive resource management decisions. However, while monitoring is an established reality in HPC, no generic framework exists to enable holistic and online operational data analytics, leading to insular ad-hoc solutions each addressing only specific aspects of the problem.In this paper we propose Wintermute, a novel operational data analytics framework for HPC installations, built upon the holistic DCDB monitoring system. Wintermute is designed following a survey of common operational requirements, and as such offers a large variety of configuration options to accommodate these varying requirements. Moreover, Wintermute is based on a set of logical abstractions to ease the configuration of models at a large scale and maximize code re-use. We highlight Wintermute's flexibility through a series of case studies, each targeting a different aspect of the management of HPC systems, and demonstrate its small resource footprint.

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AI-Driven Holistic Approach to Energy Efficient HPC

Tracey

Hoang

Subelet

et al. 2020

Lecture Notes in Computer Science

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Towards a Predictive Energy Model for HPC Runtime Systems Using Supervised Learning

Cited by 9 publications

References 13 publications

Correlation-wise Smoothing: Lightweight Knowledge Extraction for HPC Monitoring Data

Correlation-wise Smoothing: Lightweight Knowledge Extraction for HPC Monitoring Data

DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems

AI-Driven Holistic Approach to Energy Efficient HPC

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