Modern big data workflows are characterized by computationally intensive kernels. The simulated results are often combined with knowledge extracted from AI models to ultimately support decision-making. These energy-hungry workflows are increasingly executed in data centers with energy-efficient hard-ware accelerators since FPG As are well-suited for this task due to their inherent parallelism. We present the H2020 project EVEREST, which has developed a system development kit (SDK) to simplify the creation of FPGA-accelerated kernels and manage the execution at runtime through a virtualization environment. This paper describes the main components of the EVEREST SDK and the benefits that can be achieved in our use cases.

Big data analytics largely rely on data. Because of their central role, it is fundamental to ensure the security and correctness of data used in these applications. Anomaly detection could help to increase the security of big data analytics applications. However, these applications are very diverse both for the properties of the data analyzed and for the computations to be carried out on them. As a result, the selection of the most appropriate anomaly detection method is a challenging and time consuming task for designers. Hierarchical Temporal Memory (HTM) is as an anomaly detection technique sufficiently generic to achieve satisfactory performance on a wide range of applications, thus suitable to ease the burden of selecting the anomaly detection method. To confirm this, in this paper we explore the performance of HTM on a dataset used for air quality prediction. Our preliminary results show that HTM achieves excellent performance when compared to other popular anomaly detection methods.

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 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.