These workshops were organized by members of the Nesus Cost Action IC 1305: Network for Sustainable Ultrascale Computing, which is a follow-up of COST Actions IC0804 and IC0805 1. The goal of the NESUS Action is to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large-scale distributed systems, and big data management. This network aims at contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience. The selected papers cover very important scientific issues encountered nowadays such as the following: CPU/GPU execution, system-on-chip programming, parallel algorithms taking into account various constraints (energy, communication, etc.), programming models, and so on. We really hope that the reader will enjoy this high-quality issue, and we are sure that she/he will find it highly relevant to the state-of-the-art of today's heterogeneous and parallel computing.

Visualization of multidimensional data by means of Multidimensional Scaling (MDS) is a popular technique of exploratory data analysis widely usable, e.g. in analysis of bio-medical data, behavioral science, marketing research, etc. Implementations of MDS methods include a subroutine for an auxiliary global optimization problem. The latter is difficult because of high dimensionality, absence of overall smoothness, and a large number of local minima. In such a situation application of a genetic algorithm (GA) seems reasonable. A favorable assessment of application of GAs in MDS in previous publications is based on heuristic arguments without estimating quantitatively the precision of GA while applied to the solution of corresponding global optimization problems. Indeed, the estimation of precision is difficult because of complexity to find the actual global minimum not only in routine use but also in unique research experiments. Quantitatively the precision of GA was estimated, at least in the experimental problems of modest dimensionality, using global minima found by means of the developed parallel version of explicit enumeration algorithm. To cope with high complexity of the minimization problem a parallel version of GA is developed, and its efficiency for problem of higher dimensionality is investigated.