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In scientific computing, scaling issues frequently occur as more processors are utilized and/or the problem size is increased. Applications that run well on small problems and small machines, don't necessarily work well, or at all when executed at larger scale. It is desirable to avoid using expensive supercomputer time for discovering and correcting problems; instead, we would like to do this beforehand...
Climate simulation and weather forecasting codes are among the most complex examples of scientific software. Moreover, many of them are written in Fortran, making them some of the largest and most complex Fortran codes ever developed. For companies and researchers creating Fortran development tools -- IDEs, static analyzers, refactoring tools, etc. -- it is helpful to study these codes to understand...
Developing a distributed system is a complex and error-prone task. Properly handling the interaction of a potentially large number of distributed components while keeping resource usage low and performance high is challenging. The state-of-the-practice on performance evaluation focuses on employing profilers to detect and fix potential performance problems, but lacks decision support information about...
Developers working in Computational Science & Engineering (CSE)/High Performance Computing (HPC) must contend with constant change due to advances in computing technology and science. Test Driven Development (TDD) is a methodology that mitigates software development risks due to change at the cost of adding comprehensive and continuous testing to the development process. Testing frameworks...
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