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.
@article{arxiv.2103.04185,
title = {EVEREST: A design environment for extreme-scale big data analytics on heterogeneous platforms},
author = {Christian Pilato and Stanislav Bohm and Fabien Brocheton and Jeronimo Castrillon and Riccardo Cevasco and Vojtech Cima and Radim Cmar and Dionysios Diamantopoulos and Fabrizio Ferrandi and Jan Martinovic and Gianluca Palermo and Michele Paolino and Antonio Parodi and Lorenzo Pittaluga and Daniel Raho and Francesco Regazzoni and Katerina Slaninova and Christoph Hagleitner},
journal= {arXiv preprint arXiv:2103.04185},
year = {2021}
}
Comments
Paper accepted for presentation at the IEEE/EDAC/ACM Design, Automation and Test in Europe Conference and Exhibition (DATE 2021)