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Toward the reusability for iterative linear algebra software in distributed environment

机译:分布式环境中迭代线性代数软件的可重用性

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LAKe (Linear Algebra Kernel) is a linear algebra class library developed using an object oriented approach in order to enable a good reuse code for sequential or parallel linear algebra applications. An application implemented using LAKe can be run either in sequential or in parallel mode using the same code. This paper proposes an extension to the LAKe library, which enables an implemented method to run in sequential and parallel mode simultaneously. This extension of LAKe allows a concurrent reuse of sequential and parallel components inside the same linear algebra application. To take advantage of such concurrent reusability, the sequential and parallel components have to be able to collaborate with each other. The hybrid methods need such properties to run. These methods are defined by a combination of several numerical techniques, or several copies of the same method parameterized differently in order to accelerate the convergence and/or to improve the accuracy of the solution of a given large linear algebra problem. In order to validate our approach, we present some numerical experiments, making use of a hybrid method with the proposed extension of LAKe.
机译:LAKe(线性代数内核)是使用面向对象方法开发的线性代数类库,以便为顺序或并行线性代数应用程序提供良好的重用代码。使用LAKe实现的应用程序可以使用相同的代码以顺序或并行模式运行。本文提出了对LAKe库的扩展,该扩展使实现的方法可以同时在顺序和并行模式下运行。 LAKe的这种扩展允许在同一线性代数应用程序内同时重用顺序和并行分量。为了利用这种并发的可重用性,顺序和并行组件必须能够彼此协作。混合方法需要此类属性才能运行。这些方法是通过几种数值技术的组合或以不同方式参数化的同一方法的多个副本定义的,以加快收敛速度​​和/或提高给定大线性代数问题的求解精度。为了验证我们的方法,我们提出了一些数值实验,并使用了混合方法和LAKe的扩展建议。

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