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Parallel programming in an abstract enough form has been advocated to be easy and intuitive [3]. Productive programming development, and software engineering, relies in programming abstractions with efficient implementations. Abstractions that hide the low-level details and decisions. However, a trade-off between abstraction and performance is constantly appearing in the complex environments of parallel computing. The complexity of new platforms for high-performance computing is continuously increasing. This trend affects all kind of systems at any scale level. The list of the more powerful computational systemsworldwide is regularly published on the Top500 list [4]. The observation of the trends and evolution of these systems shows that both the amount of processing units and their heterogeneity is growing at a fast pace, with exascale computing on mind. On the other hand, more parallel, heterogeneous, and complex systems-on-chip, containing a diversity of core types, are being delivered for low-end systems. Exploiting the performance power of all these systems is becoming more and more difficult. The parallel programming languages and tools commonly used as de-facto standards (such as OpenMP, message-passing, CUDA, or OpenCL), or even more generic approaches [1], are not yet providing a really intuitive and interoperable parallel programming experience. In the quest for high-performance, the generality is overwhelmed by the details. Both the system and the applications programmers still face many challenges. For example, those related to learning different programmingmodels

Guest Editorial: High-Level Parallel Programming and the Road to High Performance