These differences appear at various phases of the software lifecycle as described below:

Requirements

• Risks due to the exploration of relatively unknown scientific/engineering phenomena

• Supporting reproducible science, particularly on non-deterministic systems

• Constant change as new information is gathered

Design

• Data dependencies within the software

• The need to identify the most appropriate parallelization strategy for CoDeSE algorithms

• The presence of complex communication among HPC nodes that could degrade performance

• Challenges in designing unit and system tests at appropriate scales

• The need for fault tolerance and task migration mechanisms to mitigate the need to restart time-consuming computations due to software or hardware errors

V&V

• Results are often unknown when exploring novel science or engineering areas, algorithms, and datasets

• Challenges in applying unit and system tests at appropriate scales

• Challenges in retrospectively designing and implementing tests for legacy code

• Popular tools often do not work on the latest HPC architectures; they need to be tuned to handle many threads executing at the same time

Deployment

• Failure of components within running systems is expected due to system size

• Continuous integration on platforms with high available and infrequent downtimes

• Long system lifespans necessitate porting across multiple platforms