  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.
