EVEREST: A design environment for extreme-scale big data analytics on heterogeneous platforms
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
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.
Details
Original language | English |
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Title of host publication | 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) |
Publisher | IEEE, New York [u. a.] |
Pages | 1320-1325 |
Number of pages | 6 |
ISBN (electronic) | 978-3-9819263-5-4 |
ISBN (print) | 978-1-7281-6336-9 |
Publication status | Published - 1 Feb 2021 |
Peer-reviewed | Yes |
Publication series
Series | Design, Automation and Test in Europe Conference and Exhibition (DATE) |
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ISSN | 1530-1591 |
Conference
Title | 2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021 |
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Duration | 1 - 5 February 2021 |
City | Virtual, Online |
External IDs
ORCID | /0000-0002-5007-445X/work/141545522 |
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