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A design approach for numerical libraries in large scale distributed systems

机译:大规模分布式系统中数值库的设计方法

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Nowadays, large scale distributed systems gather thousands of nodes with hierarchical memory models. They are heterogeneous, volatile and geographically distributed. The efficient exploitation of such systems requires the conception and adaptation of appropriate numerical methods, the definition of new programming paradigms, new metrics for performance prediction, etc. The modern hybrid numerical methods are well adapted to this kind of systems. This is particularly because of their multi-level parallelism and fault tolerance property. However the programming of these methods for these architectures requires concurrent reuse of sequential and parallel code. But the currently existing numerical libraries aren't able to exploit the multi-level parallelism offered by theses methods. A few linear algebra numerical libraries make use of object oriented approach allowing modularity and extensibility. Nevertheless, those which offer modularity,sequential and parallel code reuse are almost non-existent. In this paper, we analyze the lacks in existing libraries and propose a design based on a component approach and the strict separation between computation operations, data management and communication control of an application. We present then an application of this design using YML scientific workflow environment (http://yml.prism.uvsq.fr/) jointly with the object oriented LAKe (Linear Algebra Kernel) library. Some numerical experiments on GRID5000 platform validate our approach and show its efficiency.
机译:如今,大规模的分布式系统使用分层的内存模型收集了数千个节点。它们是异构的,易变的并且在地理上分布。对此类系统的有效利用需要适当的数值方法的概念和适应,新的编程范例的定义,性能预测的新指标等。现代的混合数值方法非常适合此类系统。尤其是由于它们的多级并行性和容错特性。但是,针对这些体系结构的这些方法的编程需要同时重用顺序和并行代码。但是当前现有的数值库无法利用这些方法提供的多级并行性。一些线性代数数值库利用面向对象的方法来实现模块化和可扩展性。但是,提供模块化,顺序和并行代码重用的功能几乎不存在。在本文中,我们分析了现有库的不足,并提出了一种基于组件方法的设计,并在应用程序的计算操作,数据管理和通信控制之间严格分开。然后,我们将使用YML科学工作流环境(http://yml.prism.uvsq.fr/)与面向对象的LAKe(线性代数内核)库一起提出该设计的应用程序。在GRID5000平台上进行的一些数值实验验证了我们的方法并显示了其有效性。

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