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.

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.

With the emergence of multicore embedded System on Chip (SoC), the integration of several applications with different levels of criticality on the same platform is becoming increasingly popular. These platforms, known as mixed-criticality systems, need to meet numerous requirements such as real-time constraints, Operating System (OS) scheduling, memory and OSes isolation. To construct mixed-criticality systems, various solutions, based on virtualization extensions, have been presented where OSes are contained in a Virtual Machine (VM) through the use of a hypervisor. However, such implementations usually lack hardware features to ensure a full isolation of other bus masters (e.g., Direct Memory Access (DMA) peripherals, Graphics Processing Unit (GPU)) between OSes. Furthermore on multicore implementation, one core is usually dedicated to one OS, causing CPU underutilization. To address these issues, this paper presents VOSYSmonitor, a multi-core software layer, which allows the co-execution of a safety-critical Real-Time Operating System (RTOS) and a non-critical General Purpose Operating System (GPOS) on the same hardware ARMv8-A platform. VOSYSmonitor main differentiation factors with the known solutions is the possibility for a processor to switch between secure and non-secure code execution at runtime. The partitioning is ensured by the ARM TrustZone technology, thus allowing to preserve the usage of virtualization features for the GPOS. VOSYSmonitor architecture will be detailed in this paper, while benchmarking its performance versus other known solutions.