| Parallel computing offers immense potential to solve large, complex scientific problems. Load imbalance is a major impediment in obtaining high performance by a parallel system. One principal form of parallelism found in scientific applications is data parallelism. Loops without dependencies are data parallel. During the execution of large parallel loops, computational requirements vary due to problem, algorithmic and systemic characteristics. These factors lead to load imbalance which in turn degrades the performance of an application. Over the years, a number of dynamic loop scheduling techniques have been proposed to address one or more of these factors. However, there is no single strategy that works well for different problem domains and system characteristics. Moreover, load balancing during runtime is complicated because of its need for dynamic data redistribution. Therefore, there is a distinct need to integrate the dynamic loop scheduling techniques into a single package and provide them as an application programming interface (API) to the application developer. (Abstract shortened by UMI.)... |
