EVEREST OVERVIEW

The distributed and heterogeneous nature of the data sources in High Performance Big Data Analytics (HPDA) applications pushes towards novel computing systems that combine HPC, Cloud, and IoT solutions (for efficient and distributed computation closer to the data) with Artificial Intelligence (AI) algorithms (for knowledge extraction and decision making). The creation of future Big Data systems will be data-driven, but will also feature complex heterogeneous and reconfigurable architectures that must be customized depending on the nature and locality of the data, and the type of learning/decisions to be performed.

The EVEREST project aims at developing a holistic approach for co-designing computation and communication in a heterogeneous, distributed, scalable, and secure system for HPDA. This is achieved by simplifying the programmability of heterogeneous and distributed architectures through a “data-driven” design approach, the use of hardware-accelerated AI, and an efficient monitoring of the execution with a unified hardware/software paradigm. EVEREST proposes a design environment that combines state-of-the-art, stable programming models, and emerging communication standards with novel and dedicated domain-specific extensions.