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首页> 外文期刊>International Journal of Foundations of Computer Science >Scheduling on large scale distributed platforms from models to implementations
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Scheduling on large scale distributed platforms from models to implementations

机译：从模型到实现在大型分布式平台上调度

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Today, large scale parallel systems are available at low cost. Many powerful such systems have been installed all over the world and the number of users is always increasing. The difficulty of using them efficiently is growing with the complexity of the interactions between more and more architectural constraints and the diversity of the applications. The design of efficient parallel algorithms has to be reconsidered under the influence of new parameters of such platforms (namely, cluster, grid and global computing) which are characterized by a larger number of heterogeneous processors, often organized in several hierarchical sub-systems. At each step of the evolution of the parallel processing field, researchers designed adequate computational models whose objective was to abstract the real world in order to be able to analyze the behavior of algorithms. In this paper, we will investigate two complementary computational models that have been proposed recently: Parallel Task (PT) and Divisible Load (DL). The Parallel Task (i.e. tasks that require more than one processor for their execution) model is a promising alternative for scheduling parallel applications, especially in the case of slow communication media. The basic idea is to consider the application at a coarse level of granularity. Another way of looking at the problem (which is somehow a dual view) is the Divisible Load model where an application is considered as a collection of a large number of elementary - sequential - computing units that will be distributed among the available resources. Unlike the PT model, the DL model corresponds to a fine level of granularity. We will focus on the PT model, and discuss how to mix it with simple Divisible Load scheduling. As the main difficulty for distributing the load among the processors (usually known as the scheduling problem) in actual systems comes from handling efficiently the communications, these two models of the problem allow us to consider them implicitly or to mask them, thus leading to more tractable problems. We will show that in spite of the enormous complexity of the general scheduling problem on new platforms, it is still useful to study theoretical models. We will focus on the links between models and actual implementations on a regional grid with more than 500 processors.
机译：如今，低成本的大规模并行系统可用。全世界已经安装了许多功能强大的此类系统，并且用户数量一直在增加。随着越来越多的体系结构约束和应用程序多样性之间交互的复杂性，有效使用它们的难度越来越大。在此类平台的新参数（即集群，网格和全局计算）的新参数的影响下，必须重新考虑高效并行算法的设计，这些参数的特征是数量众多的异构处理器，通常组织在多个分层子系统中。在并行处理领域发展的每个步骤中，研究人员设计了足够的计算模型，其目的是抽象现实世界，以便能够分析算法的行为。在本文中，我们将研究最近提出的两个互补的计算模型：并行任务（PT）和可分负荷（DL）。并行任务（即，执行需要多个处理器的任务）模型是调度并行应用程序的有希望的替代方法，尤其是在通信媒体速度较慢的情况下。基本思想是在粗粒度上考虑应用程序。解决问题的另一种方法（某种程度上是双重观点）是可分割负载模型，在该模型中，应用程序被视为将在可用资源之间分配的大量基本-顺序-计算单元的集合。与PT模型不同，DL模型对应于精细级别的粒度。我们将专注于PT模型，并讨论如何将其与简单的Divisible Load调度结合使用。由于在实际系统中在处理器之间分配负载的主要困难（通常称为调度问题）来自有效地处理通信，因此问题的这两种模型使我们可以隐式考虑或掩盖它们，从而导致更多问题。棘手的问题。我们将证明，尽管新平台上的一般调度问题非常复杂，但是研究理论模型仍然有用。我们将重点关注具有500多个处理器的区域网格上模型与实际实现之间的联系。

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