An adaptive control model for parallel program scheduling

Explores the role of learning techniques motivated by the approaches in artificial intelligence in the context of improving parallel program performance. The authors present an adaptive control model to improve the parallel program performance through dynamic modification of scheduling parameters under various run-time environments. Optimal or improved scheduling strategies learned from previous program executions provide feedback to further program executions