Software Development Environments for Scientific and Engineering Software: A Series of Case Studies

The need for high performance computing applications for computational science and engineering projects is growing rapidly, yet there have been few detailed studies of the software engineering process used for these applications. The DARPA High Productivity Computing Systems Program has sponsored a series of case studies of representative computational science and engineering projects to identify the steps involved in developing such applications (i.e. the life cycle, the workflows, technical challenges, and organizational challenges). Secondary goals were to characterize tool usage and identify enhancements that would increase the programmers' productivity. Finally, these studies were designed to develop a set of lessons learned that can be transferred to the general computational science and engineering community to improve the software engineering process used for their applications. Nine lessons learned from five representative projects are presented, along with their software engineering implications, to provide insight into the software development environments in this domain.

1. Introduction

Software written to solve scientific and engineering problems that require the use of high performance (massively parallel) supercomputers is an increasingly important class of applications. Software engineering processes and methods used to develop these applications varies from those used to develop other types of applications, such as in commercial IT. Some of these differences are highlighted in Section 2.