Special issue: Euro‐Par 2016

Abstract

Euro-Par-the European Conference on Parallel Computing-is an annual series of international conferences dedicated to the promotion and advancement of all aspects of parallel and distributed computing. Euro-Par covers a wide spectrum of topics from algorithms and theory to software technology and hardware-related issues, with application areas ranging from scientific to mobile and cloud computing. The major part of the Euro-Par audience consists of researchers in academic institutions, government laboratories, and industrial organisations.Euro-Par 2016, the 22nd conference in the Euro-Par series, was held in Grenoble, France. It was organised by Inria, Université Grenoble-Alpes, and IUT 2 Grenoble.Twelve broad topics were defined and advertised, covering a large variety of aspects of parallel and distributed computing. The call for papers attracted a total of 176 submissions. The submitted papers were reviewed at least 3 and, in most cases, 4 or even more times (4 reviews on average).A total of 47 papers were finally accepted for publication. This makes a global acceptance rate of 26.7 %. The authors of accepted papers came from 20 countries, with the 4 main contributing countries-France, the United States, Germany, and Spain-accounting for a bit more than half of them.Based on the results of the reviews and a majority opinion of the respective topic programme committees, a number of papers were recommended for this special issue. The authors who gave a convincing talk were contacted at the conference and invited to submit revised and extended versions of their papers. These new versions were given to 3 reviewers; 2 had previously reviewed the conference version, the third had not. Eventually, five papers were accepted for publication. This year, four Euro-Par topics are represented. Topic 3 on Scheduling and Load Balancing is represented by the paper Controlling the correlation of cost matrices to assess scheduling algorithm perfor-mance on heterogeneous platforms. 1 The authors Louis-Claude Canon, Pierre-Cyrille Héam, and Laurent Philippe consider the problem of allocating independent tasks to a collection of different machines in an optimizing manner. Costs are assessed via the generation of matrices that advise on the cost of a specific task on a specific machine. The authors propose a new assessment metric comparing the uniform cost matrix and a cost matrix using task and machine correlations. Two generation methods are proposed, one of them new, the other a modification of an existing one, and their effect on performance evaluation heuristics from the literature is studied. One reviewer pointed out that, despite the numerous required mathematical proofs, the authors do an admirable job of making each one accessible. Since the code-bones have an increased flexibility, the results show improvements over APOLLO's predecessor VMAD. Topic 5 on Parallel and Distributed Data Management and Analytics is represented by the paper A flexible I/O arbitration framework for netCDF-based BigData processing workflows on hi...