EVEREST: A Design Environment For Extreme-Scale Big Data Analytics on Heterogeneous Platforms
Christian Pilato2, Stanislav Bohm6, Fabien Brocheton10, Jeronimo Castrillon4, Riccardo Cevasco9, Vojtech Cima6, Radim Cmar11, Dionysios Diamantopoulos1, Fabrizio Ferrandi2, Jan Martinovic4, Gianluca Palermo2, Michele Paolino8, Antonio Parodi5, Lorenzo Pittaluga9, Daniel Raho8, Francesco Regazzoni3, Katerina Slaninova6 and Christoph Hagleitner1
1IBM Research Europe, Switzerland
2Politecnico di Milano, Italy,
3Università della Svizzera italiana, Switzerland
4Technische Universität Dresden, Germany
5Centro Internazionale di Monitoraggio Ambientale, Italy,
6IT4Innovations, VSB – Technical University of Ostrava
7Czech Republic
8Virtual Open System, France
9Duferco Energia, Italy,
10NUMTECH, France
11Sygic, Slovakia
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 hardwareaccelerated 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.
