Despite an impressive body of research, parallel and distributed computing remains a complex task prone to subtle software issues that can affect both the correctness and the performance of the computation. The increasing demand to distribute computing over large-scale parallel and distributed platforms, such as grids and large clusters, often combined with the use of hardware accelerators, overlaps with an increasing pressure to make computing more dependable. To address these challenges, the parallel and distributed computing community continuously requires better tools and environments to design, program, debug, test, tune, and monitor parallel programs. This topic aims to bring together tool designers, developers, and users to share their concerns, ideas, solutions, and products covering a wide range of platforms, including homogeneous and heterogeneous multi-core architectures. Contributions with solid theoretical foundations and experimental validations on production-level parallel and distributed systems were particularly valued. This year, we encouraged submissions proposing intelligent monitoring and diagnosis tools and environments, which can exploit behavior knowledge to detect programming bugs or performance bottlenecks and help ensure correct and efficient parallel program execution.