Thus, the focus of this workshop is on methodologies and experiences of scientific and engineering applications and algorithms to achieve sustainable code development for better productivity, application performance and reliability. In particular, we will focus on the following topics in parallel and distributed scientific and engineering applications, but not limited to:

• Code modernization methodologies and experiences for adapting the changes in future computing systems such as porting of legacy simulation code and libraries/tools to facilitate code refactoring and porting.

• Application and algorithm development of various parallel and distributed programming models/framework such as CAF, UPC, Chapel, X10, Charm++, HPX, Uintah, Legion, and/or the interoperation of multiple models within single applications (e.g. MPI+X where X is OpenMP, OpenCL, CUDA etc). We appreciate the experiences of early adopters of new programming models and platforms.

• Experience in new tools and libraries for effective application development, including performance tools, application development frameworks, Domain Specific Languages (DSLs), etc.

• Tools and techniques for improving application reliability and resilience. This includes both performance and correctness issues, with the latter arising from adverse operating conditions (e.g. low power) or very large system scales.

• Use cases of enterprise distributed computing technology (such as MapReduce, Data Analytics and Machine-learning tools) in scientific and engineering applications.

• Large-scale parallel and distributed algorithms supporting science and engineering applications.

• Methodologies and experiences in developing large-scale applications.