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MPI Reduction Operations for Sparse Floating-point Data

机译:稀疏浮点数据的MPI缩小操作

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This paper presents a pipeline algorithm for MPI_Reduce that uses a Run Length Encoding (RLE) scheme to improve the global reduction of sparse floating-point data. The RLE scheme is directly incorporated into the reduction process and causes only low overheads in the worst case. The high throughput of the RLE scheme allows performance improvements when using high performance interconnects, too. Random sample data and sparse vector data from a parallel FEM application is used to demonstrate the performance of the new reduction algorithm for an HPC Cluster with InfiniBand interconnects.
机译:本文介绍了用于MPI_Reduce的管道算法,它使用运行长度编码(RLE)方案来提高全局稀疏浮点数据的减少。 RLE方案直接纳入还原过程,并在最坏情况下仅导致低开销。 RLE方案的高吞吐量允许在使用高性能互连时进行性能改进。来自并行有限元件应用程序的随机样本数据和稀疏向量数据用于展示与Infiniband互连的HPC群集的新减速算法的性能。

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