SLOWER: A performance model for Exascale computing

A performance framework is introduced to facilitate the development and optimization of extreme-scale abstract execution models and the future systems derived from them. SLOWER defines a six-dimensional design trade-off space based on sources of performance degradation that are invariant across system classes. Exemplar previous generation execution models e.g., vector are examined in terms of the SLOWER parameters to illustrate their alternative responses to changing enabling technologies. New technology trends leading to nano-scale and the end of Moore's Law demand future innovations to address these same performance factors. An experimental execution model, ParalleX, is described to postulate one possible advanced abstraction upon which to base next generation hardware and software systems. A detailed examination is presented of how this class of dynamic adaptive execution model addresses SLOWER for advances in efficiency and scalability. To represent the SLOWER trade-off space, a queue model has been developed and is described. A set of simulation experiments spanning ranges of key parameters is presented to expose some initial properties of the SLOWER framework.