Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Qualityof-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers the challenges and opportunities that arise out of this new direction in the computing landscape.

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FastFlow is a programming framework specifically targeting cache-coherent shared-memory multicores. It is implemented as a stack of C++ template libraries built on top of lock-free (and memory fence free) synchronization mechanisms. Its philosophy is to combine programmability with performance. In this paper a new FastFlow programming methodology aimed at supporting parallelization of existing sequential code via offloading onto a dynamically created software accelerator is presented. The new methodology has been validated using a set of simple micro-benchmarks and some real applications.

The use of efficient synchronization mechanisms is crucial for implementing fine grained parallel programs on modern shared cache multicore architectures. In this paper we study this problem by considering Single-Producer/Single-Consumer (SPSC) coordination using unbounded queues. A novel unbounded SPSC algorithm capable of reducing the row synchronization latency and speeding up Producer-Consumer coordination is presented. The algorithm has been extensively tested on a sharedcache multi-core platform and a sketch proof of correctness is presented. The queues proposed have been used as basic building blocks to implement the FastFlow parallel framework, which has been demonstrated to offer very good performance for fine-grain parallel applications.

This paper presents a new programming methodology for introducing and tuning parallelism in Erlang programs, using source-level code refactoring from sequential source programs to parallel programs written using our skeleton library, Skel. High-level cost models allow us to predict with reasonable accuracy the parallel performance of the refactored program, enabling programmers to make informed decisions about which refactorings to apply. Using our approach, we demonstrate easily obtainable, significant and scalable speedups of up to 21 on a 24-core machine over the sequential cod

Abstract. FastFlow is a structured parallel programming framework targeting shared memory multi-core architectures. In this paper we introduce a FastFlow extension aimed at supporting a network of multi-core workstation as well. The extension supports the execution of FastFlow programs by coordinating-in a structured way-the fine grain parallel activities running on a single workstation. We discuss the design and the implementation of this extension presenting preliminary experimental results validating it on state-of-the-art networked multi-core nodes.

We present the results of a systematic literature review that examines the main paradigms and properties of programming languages developed for and used in High Performance Computing for Big Data processing. The systematic literature review is based on a combination of automated keyword-based search in the Elsevier Science Direct database and further digital databases for articles published in international peer-reviewed journals and conferences, leading to an initial sample of 420 articles, which was then narrowed down in a second phase to 152 articles found relevant and published 2006-2018. The manual analysis of these articles allowed us to identify 26 languages used in 33 of these articles for HPC for Big Data processing. We analyzed the languages and their usage in these articles by 22 criteria and summarize the results in this article. We evaluate the outcomes of the literature review by comparing them with opinions of domain experts. Our results indicate that, for instance, the majority of the used HPC languages in the context of Big Data are text-based general-purpose programming languages and target the end-user community.

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