EVEREST: A design environment for extreme-scale big data analytics on heterogeneous platforms
| Autor: | Pilato, Christian, Bohm, Stanislav, Brocheton, Fabien, Castrillon, Jeronimo, Cevasco, Riccardo, Cima, Vojtech, Cmar, Radim, Diamantopoulos, Dionysios, Ferrandi, Fabrizio, Martinovic, Jan, Palermo, Gianluca, Paolino, Michele, Parodi, Antonio, Pittaluga, Lorenzo, Raho, Daniel, Regazzoni, Francesco, Slaninova, Katerina, Hagleitner, Christoph |
|---|---|
| Rok vydání: | 2021 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.23919/DATE51398.2021.9473940 |
| Popis: | 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 1st, 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. Comment: Paper accepted for presentation at the IEEE/EDAC/ACM Design, Automation and Test in Europe Conference and Exhibition (DATE 2021) |
| Databáze: | arXiv |
| Externí odkaz: |
|