Analysis of High Performance Applications Using Workload Requirements

Ferro, Mariza; Evoy, Giacomo Mc; Schulze, Bruno

doi:10.1007/978-3-319-61982-8_2

Cited by 3 publications

(1 citation statement)

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“…Based on these initial information, the characterization of the application and architectures available for the job execution will be defined, like an signature of it. This relationship leads to very specific conditions of performance and energy consumption, as demonstrated in [Ferro 2015, Ferro et al 2017a] and could figure out of the knowledge base (KB) of the framework. This KB would be used by the scheduler to predict the runtime and energy consumption in order to choose the best architecture for the job and also the feasibility to select the best frequency, using Dynamic and Frequency Voltage Scaling (DVFS) techniques.…”

Section: Proposed Autonomous Frameworkmentioning

confidence: 99%

Towards an Autonomous Framework for HPC Optimization: Using Machine Learning for Energy and Performance Modeling

Klôh¹,

Gritz²,

Schulze³

et al. 2019

Anais Do XX Simpósio Em Sistemas Computacionais De Alto Desempenho (SSCAD 2019)

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Performance and energy efficiency are now critical concerns in high performance scientific computing. It is expected that requirements of the scientific problem should guide the orchestration of different techniques of energy saving, in order to improve the balance between energy consumption and application performance. To enable this balance, we propose the development of an autonomous framework to make this orchestration and present the ongoing research to this development, more specifically, focusing in the characterization of the scientific applications and the performance modeling tasks using Machine Learning.

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Section: Proposed Autonomous Frameworkmentioning

confidence: 99%