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An object-oriented method for out-of-core parallel computations on cluster of workstations

机译:面向对象的工作站集群上核外并行计算方法

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Many applications in different fields need to access large amounts of data. The difficulty of handling out-of-core data limits the performance of supercomputers. We first describe an out-of-core computation model on cluster of workstations. Then an object-oriented method is offered, which makes the out-of-core programs easier to write. Some optimal strategies, such as distribution of local array files, data sieving and prefetching, also make an improvement in the performance of out-of-core parallel computations.
机译:不同字段中的许多应用程序需要访问大量数据。处理外核数据的难度限制了超级计算机的性能。我们首先描述了在工作站集群上描述了一个核心计算模型。然后提供面向对象的方法,这使得核心外的程序更容易写入。一些最佳策略,例如本地阵列文件的分发,数据筛选和预取,还提高了核心外并行计算的性能。

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