Improving strategies for writing scientific software that is efficient, scalable, and portable—from laptops to emerging extreme-scale architectures—while preserving other code quality metrics such as correctness and usability.
The practice of aggregating computing power in a way that delivers much higher performance than one could achieve with a typical desktop computer or workstation in order to solve large problems, often in science and engineering.
Factors that must be considered when developing software for the leadership-class supercomputers, among the fastest computers in the world.
Software exhibiting similar performance across multiple platforms, with the time to solution reflecting efficient utilization of computational resources on each platform. Resources address porting code to new architectures.