| Scientific applications are large, complex, irregular, computationally intensive and typically contain data parallel loops. The presence of loops with independent iterations, makes parallel computing as a natural choice for solving these applications. The computational requirements of these iterations vary due to variations in problem, algorithmic and systemic characteristics, leading to performance degradation during parallel execution. Considerable amount of research has been dedicated to the development of dynamic scheduling techniques based on probabilistic analysis to address these predictable and unpredictable factors that lead to severe load imbalance. The mathematical foundations of these scheduling algorithms have been previously developed and published in the literature. These techniques have successfully been integrated into scientific applications as well as into runtime systems. Recently, efforts have also been directed to integrate these techniques into dynamic load balancing libraries for scientific applications. (Abstract shortened by UMI.)... |
