Runtime detection and optimization of collective communication patterns

Parallelism is steadily growing, remote-data access will soon dominate the execution time of large-scale applications. Many large-scale communication patterns expose significant structure that can be used to schedule communications accordingly. In this work, we identify concurrent communication patterns and transform them to semantically equivalent but faster communications. We show a directed acyclic graph formulation for communication schedules and concisely define their synchronization and data movement semantics. Our dataflow solver computes an internal representation (IR) that is amenable to pattern detection. We demonstrate a detection algorithm for our IR that is guaranteed to detect communication kernels on subsets of the graph and replace the subgraph with hardware accelerated or hand-tuned kernels. Those techniques are implemented in an open-source detection and transformation framework to optimize communication patterns. Experiments show that our techniques can improve the performance of representative example codes by several orders of magnitude on two different systems. However, we also show that some collective detection problems on process subsets are NP-hard. The developed analysis techniques are a first important step towards automatic large-scale communication transformations. Our developed techniques open several avenues for additional transformation heuristics and analyses. We expect that such communication analyses and transformations will become as natural as pattern detection, just-in-time compiler optimizations, and autotuning are today for serial codes.