Global Task Data-Dependencies in PGAS Applications

Global Task Data-Dependencies in PGAS Applications
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PGAS 应用程序中的全局任务数据依赖性

DOI:
10.1007/978-3-030-20656-7_16
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发表时间:
2019
期刊:
2015 IEEE International Parallel and Distributed Processing Symposium
影响因子:
--
通讯作者:
J. Gracia
J. Gracia
中科院分区:
--
文献类型:
--
作者:
Joseph Schuchart;J. Gracia

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近年来,出现了两种独立的编程模型,挑战了传统的消息传递和线程级工作共享的两层组合:分区全局地址空间(PGAS)和基于任务的并发。在PGAS编程模型中,进程之间的同步和通信是解耦的,这为减少通信开销提供了巨大的潜力。同时,基于任务的编程允许利用大量的共享内存并发性。PGAS中固有的细粒度同步的缺乏可以通过跨进程边界的细粒度任务同步来解决。在这项工作中,我们建议使用的任务数据依赖关系描述的数据流在全局地址空间中同步执行的任务,在多个进程上并行创建。我们提出了一个描述的全局数据依赖关系,描述了必要的分布式调度程序实例之间的相互作用,需要处理它们,并讨论我们的实现在新的窗口PGAS框架中的DASH开放图像的上下文中。我们使用Blocked Cholesky Factorization和LULESH代理应用程序评估我们的方法,证明了我们方法的可行性和可扩展性。
Recent years have seen the emergence of two independent programming models challenging the traditional two-tier combination of message passing and thread-level work-sharing: partitioned global address space (PGAS) and task-based concurrency. In the PGAS programming model, synchronization and communication between processes are decoupled, providing significant potential for reducing communication overhead. At the same time, task-based programming allows to exploit a large degree of shared-memory concurrency. The inherent lack of fine-grained synchronization in PGAS can be addressed through fine-grained task synchronization across process boundaries. In this work, we propose the use of task data dependencies describing the data-flow in the global address space to synchronize the execution of tasks created in parallel on multiple processes. We present a description of the global data dependencies, describe the necessary interactions between the distributed scheduler instances required to handle them, and discuss our implementation in the context of the DASH Open image in new window PGAS framework. We evaluate our approach using the Blocked Cholesky Factorization and the LULESH proxy app, demonstrating the feasibility and scalability of our approach.
实践中的等效率:配置和了解基于任务的应用程序的性能
DOI: 10.1145/3018743.3018770
发表时间: 2017
期刊: Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
影响因子: --
作者:
Shudler;Sergei;Calotoiu;Alexandru;Hoefler;Torsten
通讯作者: Torsten