Effective use of communication networks is critical to the performance and scalability of parallel applications. Partitioned Global Address Space languages like UPC bring the promise of performance and programmer productivity. Studies of well-tuned programs have suggested that PGAS languages are effective at utilizing modern networks because their one-sided communication is a good match to the underlying network hardware. An open question is whether the manual optimizations required to achieve good performance can be performed automatically by the compiler in a performance portable manner. In this paper we present a compiler and runtime optimization framework for loops containing communication operations. Our framework performs compile time message vectorization and strip-mining and defers until runtime the selection of the actual communication operations. At runtime, the communication requirements of the program are analyzed, and communication is instantiated and scheduled based on highly tuned network and application performance models. The runtime analysis takes into account network flow control and quality-of-service restrictions, and it is able to select from a large class of available communication primitives the communication schedule best suited for the dynamic combination of input size and system parameters. The results indicate that our framework produces code that scales and performs better than that of manually optimized implementations. Our approach not only improves performance, but increases programmer productivity as well.

International Conference on Supercomputing, Island of Kos, Greece