This workshop is concerned with identifying and understanding the unique aspects of Software Engineering (SE) for the development of scientific software that exploits High Performance Computing (HPC) architectures. This class of software applications includes software developed to support various scientific endeavors which would be difficult or impossible to perform experimentally. This type of software development has not received enough attention from the SE community. Specifically, we are interested in: Scientific software applications that solve complex software- or data-intensive research problems. These applications range from large, parallel models/simulations of the physical world using HPC systems to smaller scale simulations developed by a single scientist or engineer on a desktop machine or on a small cluster, which may be later scaled to an HPC environment. Applications that support scientific research and experiments. Such applications include, but are not limited to, systems for managing and/or manipulating large amounts of data and systems that provide HPC infrastructure for research applications. The process for building, reusing, and publishing HPC software and the data used in scientific experiments or engineering innovations. Among others, these process include agile approaches, open source/open data issues, testing HPC software, and managing software or data repositories for publishing goals. Despite its importance, the development of scientific HPC software historically has attracted less attention from the SE community than other subdomains have. Indeed, the development of this type of software is significantly different than the development of business information systems, from which many of the SE best practices, tools and techniques have been drawn. Therefore, in order to identify and develop appropriate methods, tools and techniques for scientific HPC software, members of the SE community must interact with members of the HPC and science communities to understand the differences and determine the most appropriate SE tools, methods and techniques. In addition, we hope to identify aspects of SE practice that are relevant for the education of future developers in various research domains, which can be provided as suggestions for inclusion in Software Engineering and Computer Science curricula.