EVEREST: A design environment for extreme-scale big data analytics on heterogeneous platforms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F21%3A10248959" target="_blank" >RIV/61989100:27740/21:10248959 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9473940" target="_blank" >https://ieeexplore.ieee.org/document/9473940</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/DATE51398.2021.9473940" target="_blank" >10.23919/DATE51398.2021.9473940</a>

Jazyk výsledku

angličtina

Název v původním jazyce

EVEREST: A design environment for extreme-scale big data analytics on heterogeneous platforms

Popis výsledku v původním jazyce

High-Performance Big Data Analytics (HPDA) applications are characterized by huge volumes of distributed and heterogeneous data that require efficient computation for knowledge extraction and decision making. Designers are moving towards a tight integration of computing systems combining HPC, Cloud, and IoT solutions with artificial intelligence (AI). Matching the application and data requirements with the characteristics of the underlying hardware is a key element to improve the predictions thanks to high performance and better use of resources. We present EVEREST, a novel H2020 project started on October 1, 2020, that aims at developing a holistic environment for the co-design of HPDA applications on heterogeneous, distributed, and secure platforms. EVEREST focuses on programmability issues through a data-driven design approach, the use of hardware accelerated AI, and an efficient runtime monitoring with virtualization support. In the different stages, EVEREST combines state-of-the-art programming models, emerging communication standards, and novel domain-specific extensions. We describe the EVEREST approach and the use cases that drive our research.

Název v anglickém jazyce

EVEREST: A design environment for extreme-scale big data analytics on heterogeneous platforms

Popis výsledku anglicky

High-Performance Big Data Analytics (HPDA) applications are characterized by huge volumes of distributed and heterogeneous data that require efficient computation for knowledge extraction and decision making. Designers are moving towards a tight integration of computing systems combining HPC, Cloud, and IoT solutions with artificial intelligence (AI). Matching the application and data requirements with the characteristics of the underlying hardware is a key element to improve the predictions thanks to high performance and better use of resources. We present EVEREST, a novel H2020 project started on October 1, 2020, that aims at developing a holistic environment for the co-design of HPDA applications on heterogeneous, distributed, and secure platforms. EVEREST focuses on programmability issues through a data-driven design approach, the use of hardware accelerated AI, and an efficient runtime monitoring with virtualization support. In the different stages, EVEREST combines state-of-the-art programming models, emerging communication standards, and novel domain-specific extensions. We describe the EVEREST approach and the use cases that drive our research.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2021 Design, Automation &amp; Test in Europe (DATE 2021) : 01-05 February 2021, Virtual Conference

ISBN

978-1-72816-336-9

ISSN

1530-1591

e-ISSN

1558-1101

Počet stran výsledku

6

Strana od-do

1320-1325

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Grenoble

Datum konání akce

1. 2. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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