Cooperative Execution of Parallel Tasks with Synchronization Constraints - Imam, Shams Mahmood (ProQuest Dissertations & Theses, 2016)
The topic of this thesis is the effective execution of parallel applications on emerging multicore and manycore systems in the presence of modern synchronization and coordination constraints. Synchronization and coordination can contribute significant productivity and performance overheads to the development and execution of parallel programs. Higher-level programming models, such as the Task Parallel Model and Actor Model, provide abstractions that can be used to simplify writing parallel programs, in contrast to lower-level programming models that directly expose locks, threads and processes. However, these higher-level models often lack efficient support for general synchronization patterns that are necessary for a wide range of applications. Many modern synchronization and coordination constructs in parallel programs can incur significant performance overheads on current runtime systems, or significant productivity overheads when the programmer is forced to complicate their code to mitigate these performance overheads. We believe that a cooperation between the programmer and the runtime system is necessary to reduce the parallel overhead and to execute the available parallelism efficiently in the presence of synchronization constraints. In a cooperative approach, an executing entity yields control to other entities at well-defined points during its execution. This thesis shows that the use of cooperative techniques is critical to performance and scalability of certain parallel programming models, especially in the presence of modern synchronization and coordination constraints such as asynchronous tasks, futures, phasers, data-driven tasks, and actors. In particular, we focus on cooperative extensions and runtimes for the async-finish Task Parallel Model and the Actor Model in this thesis. Our work shows that cooperative techniques simplify programmability and deliver significant performance improvements by reducing the overhead in modern parallel programming models.