After more than eight years since the launch of Bitcoin, the decentralized transaction ledger functionality implemented through the blockchain technology is being used not only for cryptocurrencies, but to register, confirm and transfer any kind of contract and property. In this work, we analyze the most relevant functionalities and known issues of this technology, with the intent of pointing out the possible behaviours that are not as efficient and reliable as they should be when thinking with a broader outlook.

The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer decision-making processes to the periphery of sensor networks without the involvement of central data servers. For this reason, we recently witnessed an impetuous development of devices that integrate sensors and computing resources in a single board to process data directly on the collection place. Due to the particular context where they are used, the main feature of these boards is the reduced energy consumption, even if they do not exhibit absolute computing powers comparable to modern high-end CPUs. Among the most popular Artificial Intelligence techniques, clustering algorithms are practical tools for discovering correlations or affinities within data collected in large datasets, but a parallel implementation is an essential requirement because of their high computational cost. Therefore, in the present work, we investigate how to implement clustering algorithms on parallel and low-energy devices for edge computing environments. In particular, we present the experiments related to two devices with different features: the quad-core UDOO X86 Advanced+ board and the GPU-based NVIDIA Jetson Nano board, evaluating them from the performance and the energy consumption points of view. The experiments show that they realize a more favorable trade-off between these two requirements than other high-end computing devices.

Modern graphics processing units (GPUs) have been at the leading edge of increasing parallelism over the last 10 years. This fact has encouraged the use of GPUs in a broader range of applications, where developers are required to lever age this technology with new programming models which ease the task of writing programs to run efficiently on GPUs. In this paper, we discuss the main guidelines to assist the developer when porting sequential scientific code on modern GPUs. These guidelines were carried out by porting the L-BFGS, the (Limited memory-) BFGS algorithm for large scale optimization, available as Harwell routine VA15. The specific interest in the L-BFGS algorithm arises from the fact that this is the computational module with the longest running time of a Oceanographic Data Assimilation application software, on which some of the authors are working.

In the last four years, the evolution and adoption of blockchain and, more generally, distributed ledger systems have shown the affirmation of many concepts and models with significant differences in system governance and suitable applications. This work aims to analyze distributed ledger technology (DLT) critically. Starting from the topical idea of decentralization, we introduce concepts and building blocks currently adopted in the available systems centering on their functional aspects and impact on possible applications. We present some conceptual framing tools helpful in the application context: a DLT reference architecture, off-chain and on-chain governance models, and classification of consensus protocols. Finally, we introduce the concept of process authenticity, reviewing tools and strategies for integrating DLT with the physical world and proposing a constructive scheme for the authentication of a physical resource through alphanumeric data.