How to Scale Dynamic Tuning to Large Parallel Applications

Abstract

Current performance analysis and tuning tools must be able to improve the performance of large-scale parallel applications. To be effective, such analysis and tuning tools must be scalable and be able to manage the dynamic behaviour of parallel applications. This work presents a scalable solution for dynamic tuning. This approach is based on a hierarchical performance analysis architecture that uses a novel information abstraction mechanism to solve local and global performance problems. We have developed a prototype implementation of the proposed analysis architecture making use of the MRNet framework. Scalability experiments have been performed using this prototype with up to 6400 application tasks. The results obtained show that the proposed analysis architecture will provide the scalability required to carry out dynamic tuning of large-scale parallel applications.