Collective Framework and Performance Optimizations to Open MPI for Cray XT Platforms
Joshua S. Ladd, Manjunath Gorentla Venkata, Pavel Shamis, Richard L. Graham · 2011
The performance and scalability of collective operations plays a key role in the performance and scalability of many scientific applications. Within the Open MPI code base we have developed a general purpose hierarchical collec-tive operations framework called Cheetah, and applied it at large scale on the Oak Ridge Leadership Computing Fa-cility’s Jaguar (OLCF) platform, obtaining better perfor-mance and scalability than the native MPI implementation. This paper discuss Cheetah’s design and implementation, and optimizations to the framework for Cray XT 5 plat-forms. Our results show that the Cheetah’s Broadcast and Barrier perform better than the native MPI implementation. For medium data, the Cheetah’s Broadcast outperforms the native MPI implementation by 93 % for 49,152 processes problem size. For small and large data, it out performs the native MPI implementation by 10 % and 9%, respectively, at 24,576 processes problem size. The Cheetah’s Barrier per-forms 10 % better than the native MPI implementation for 12,288 processes problem size. 1