具有约束并行性的任务并行规划

Tsung-Wei Huang, L. Hwang
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引用次数: 0

摘要

任务图编程模型(Task graph programming model, TGPM)已经成为许多科学计算应用程序的核心,因为它支持对管理宏观性能的并行性进行自顶向下的优化。现有tgpm关注于表达工作负载的任务和依赖关系,而将调度细节留给库运行时。虽然最大化任务并发性是一个典型的调度目标,但许多应用程序需要在图执行期间约束任务并行性。例如限制子图中工作线程的数量或将两个任务之间的冲突关联起来。然而,主流tgpm在很大程度上忽略了任务图中约束并行性这一重要特性。用户别无选择,只能实现一个单独的、通常是复杂的调度解决方案,这个解决方案既不能通用化,也不能扩展。在本文中,我们提出了一种信号量规划模型和调度方法,这两种方法都可以很容易地集成到现有的TGPM中以支持约束并行。我们已经在实际应用中证明了我们的方法的有效性和效率。例如,我们的信号量模型将工业电路放置工作量加快了28%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Task-Parallel Programming with Constrained Parallelism
Task graph programming model (TGPM) has become central to a wide range of scientific computing applications because it enables top-down optimization of parallelism that governs the macro-scale performance. Existing TGPMs focus on expressing tasks and dependencies of a workload and leave the scheduling details to a library runtime. While maximizing the task concurrency is a typical scheduling goal, many applications require task parallelism to be constrained during the graph execution. Examples are limiting the number of worker threads in a subgraph or relating a conflict between two tasks. However, mainstream TGPMs have largely ignored this important feature of constrained parallelism in a task graph. Users have no choice but to implement a separate and often sophisticated scheduling solution that is neither generalizable nor scalable. In this paper, we propose a semaphore programming model and a scheduling method both of which can be easily integrated into an existing TGPM to support constrained parallelism. We have demonstrated the effectiveness and efficiency of our approach in real applications. As an example, our semaphore model speeds up an industrial circuit placement workload up to 28%.
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