publication . Conference object . Other literature type . 2018

The D.A.V.I.D.E. big-data-powered fine-grain power and performance monitoring support

Bartolini, Andrea; Borghesi, Andrea; Libri, Antonio; Beneventi, Francesco; Gregori, Daniele; Tinti, Simone; Gianfreda, Cosimo; Altoè, Piero;
Open Access
  • Published: 08 May 2018
  • Publisher: ACM
Abstract
On the race toward exascale supercomputing systems are facing important challenges which limit the efficiency of the system. Among all, power and energy consumption fueled by the end of Dennard's scaling start to show their impact on limiting supercomputers peak performance and cost effectiveness. In this paper we present and describe a new methodology based on a set of HW and SW extensions for fine-grain monitoring of power and aggregation of them for fast analysis and visualization. We propose a turn-key system which uses MQTT communication layer, NoSQL database, fine grain monitoring and in future AI technology to measure and control power and performance. Th...
Subjects
free text keywords: Big Data; High Performance Computing; Fine-Grain Power and Performance Monitoring; AMESTER; BeagleBone Black, Big Data, High Performance Computing, Fine-Grain Power and Performance Monitoring, AMESTER, BeagleBone Black, Performance monitoring, Scaling, MQTT, Visualization, Computer science, Energy consumption, Embedded system, business.industry, business, Big data, Real-time computing, NoSQL, computer.software_genre, computer, Supercomputer, High Performance Computing, Fine-Grain Power and Performance Monitoring, AMESTER, BeagleBone Black
Funded by
EC| MULTITHERMAN
Project
MULTITHERMAN
Multiscale Thermal Management of Computing Systems
  • Funder: European Commission (EC)
  • Project Code: 291125
  • Funding stream: FP7 | SP2 | ERC
,
EC| ExaNoDe
Project
ExaNoDe
European Exascale Processor Memory Node Design
  • Funder: European Commission (EC)
  • Project Code: 671578
  • Funding stream: H2020 | RIA
,
EC| ANTAREX
Project
ANTAREX
AutoTuning and Adaptivity appRoach for Energy efficient eXascale HPC systems
  • Funder: European Commission (EC)
  • Project Code: 671623
  • Funding stream: H2020 | RIA
Communities
FET H2020FET HPC: HPC Core Technologies, Programming Environments and Algorithms for Extreme Parallelism and Extreme Data Applications
FET H2020FET HPC: AutoTuning and Adaptivity appRoach for Energy efficient eXascale HPC systems
FET H2020FET HPC: HPC Core Technologies, Programming Environments and Algorithms for Extreme Parallelism and Extreme Data Applications
FET H2020FET HPC: European Exascale Processor Memory Node Design
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Research Collection
Conference object . 2018
ETH Zürich Research Collection
Other literature type . 2018
Provider: Datacite
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Conference object . 2018
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publication . Conference object . Other literature type . 2018

The D.A.V.I.D.E. big-data-powered fine-grain power and performance monitoring support

Bartolini, Andrea; Borghesi, Andrea; Libri, Antonio; Beneventi, Francesco; Gregori, Daniele; Tinti, Simone; Gianfreda, Cosimo; Altoè, Piero;