Scaling Behavior of Linear Solvers on Large Linux Clusters
Scaling Behavior of Linear Solvers on Large Linux Clusters
复制标题
大型 Linux 集群上线性求解器的缩放行为
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
F. Saied
中科院分区：
文献类型：
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作者：
John W. Fettig;W. Kwok;F. Saied
随着高性能Linux集群进入主流超级计算领域，了解这些架构如何扩展计算科学应用中的重要内核（如线性求解器、特征值求解器、多维FFT）非常重要。本文试图描述最先进的线性求解器在大型Linux集群上的可扩展性。我们的研究集中在两个家庭的算法，Krylov子空间方法和多重网格方法。我们在一个Linux集群上包括了多达256个处理器的结果，问题大小高达6400万个未知数。
As high performance Linux clusters enter mainstream supercomputing, it is important to understand how well these architecture scale for important kernels in computational science applications, such as linear solvers, eigenvalue solvers, multidimensional FFT’s. This paper attempts to characterize the scalability of state of the art linear solvers on large Linux clusters. Our studies focus on two families of algorithms, Krylov subspace methods and multigrid methods. We include results up to 256 processors on a Linux cluster, with problem sizes up to 64 million unknowns.