Implementing the Broadcast Operation in a Distributed Task-based Runtime

Scientific applications that require high performance rely on multi-node and multi-core systems equipped with accelerators. Code for these heterogeneous architectures often mixes different programming paradigms and is hard to read and maintain. Task-based distributed runtimes can improve portability and readability by allowing programmers to write tasks that are automatically scheduled and offloaded for execution. Nevertheless, in large systems, communication can dominate the time spent during execution. To mitigate this, these systems usually implement collective operation algorithms that efficiently execute common data movement patterns across a group of processes. This work studies the usage of different broadcast strategies on the OpenMP Cluster (OMPC) task-based runtime. In addition to OMPC default behavior of on-demand data delivery, we introduce a routine to automatically detect data movement that is equivalent to a broadcast in the task graph and actively send it through a specialized algorithm. Our largest test setup using 64 worker nodes and broadcasting 64GB of data on the Santos Dumont cluster, using an extended version of Task Bench, showed a 2.02x speedup using the Dynamic Broadcast algorithm and 2.49x speedup when using the MPI broadcast routine, when compared to the default on-demand delivery.