Implementing Directed Acyclic Graphs with the Heterogeneous System Architecture

Achieving optimal performance on heterogeneous computing systems requires a programming model that supports the execution of asynchronous, multi-stream, and out-of-order tasks in a shared memory environment. Asynchronous dependency-driven tasking is one such programming model that allows the computation to be expressed as a directed acyclic graph (DAG) and exposes fine-grain task management to the programmer. The use of DAGs to extract parallelism also enables runtimes to perform dynamic load-balancing, thereby achieving higher throughput when compared to the traditional bulk-synchronous execution. However, efficient DAG implementations require features such as user-level task dispatch, hardware signalling and local barriers to achieve low-overhead task dispatch and dependency resolution. In this paper, we demonstrate that the Heterogeneous System Architecture (HSA) exposes the above capabilities, and we validate their benefits by implementing three well-referenced applications using fine-grain tasks: Cholesky factorization, Lower Upper Decomposition (LUD), and Needleman-Wunsch (NW). HSA's userlevel task dispatch and signalling capability allow work to be launched and dependencies to be managed directly by the hardware, avoiding inefficient bulk-synchronization. Our results show the HSA task-based implementations of Cholesky, LUD, and NW are representative of this emerging class of workloads and using hardware-managed tasks achieve a speedup of 3.8x, 1.6x, and 1.5x, respectively, compared to bulk-synchronous implementations.