Task Fusion in Distributed Runtimes

Abstract

We present distributed task fusion, a run-time optimization for task-based runtimes operating on parallel and heterogeneous systems. Distributed task fusion dynamically performs an efficient buffering, analysis, and fusion of asynchronously-evaluated distributed operations, reducing the overheads inherent to scheduling distributed tasks in implicitly parallel frameworks and runtimes. We identify the constraints under which distributed task fusion is permissible and describe an implementation in Legate, a domain-agnostic library for constructing portable and scalable task-based libraries. We present performance results using cuNumeric, a Legate library that enables scalable execution of NumPy pipelines on parallel and heterogeneous systems. We realize speedups up to 1.5x with task fusion enabled on up to 32 P100 GPUs, thus demonstrating efficient execution of pipelines involving many successive fine-grained tasks. Finally, we discuss potential future work, including complementary optimizations that could result in additional performance improvements.