A Parallel PARAFAC Implementation & Scalability Testing for Large-Scale Dense Tensor Decomposition
A Parallel PARAFAC Implementation & Scalability Testing for Large-Scale Dense Tensor Decomposition
复制标题
并行 PARAFAC 实施
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
K. Aggour
中科院分区：
文献类型：
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作者：
K. Aggour
并行因子分析（PARAFAC）用于许多科学学科，将多维数据集分解为主因子，以揭示数据中的关系。虽然相当流行，但PARAFAC的常见实现是单服务器解决方案，无法很好地扩展到非常大的数据集。为了解决这个问题，并行PARAFAC算法已被设计和实现在C使用MPI。端到端流水线包括从文件并行读取输入数据、执行并行算法，并以将结果并行写入文件结束。的实施已被评估使用IBM蓝色基因/Q超级计算机上的强大的缩放研究。计算时间以及通信、文件读取和文件写入带宽都是跨多个场景捕获的，以评估整体系统性能和可伸缩性。结果表明，该实现可扩展性良好，并行进程数量增加了128倍，系统执行速度提高了200倍。此外，峰值时的通信时间仅占总处理时间的12%，这表明该实现目前受到CPU的限制，因此应该继续在越来越多的节点上扩展。
Parallel Factor Analysis (PARAFAC) is used in many scientific disciplines to decompose multidimensional datasets into principal factors in order to uncover relationships in the data. While quite popular, the common implementations of PARAFAC are single server solutions that do not scale well to very large datasets. To address this limitation, a Parallel PARAFAC algorithm has been designed and implemented in C using MPI. The end-to-end pipeline includes a parallel read of the input data from a file, the execution of the parallel algorithm, and concludes with a parallel write of the results to a file. The implementation has been evaluated using a strong scaling study on an IBM Blue Gene/Q supercomputer. The compute time, as well as the communication, file read, and file write bandwidths were each captured across multiple scenarios to evaluate the overall system performance and scalability. Results indicate the implementation scales well—with a 128x increase in the number of parallel processes, the system executed 200x faster. Further, the communication time at its peak accounted for only 12% of the total processing time, indicating the implementation is currently CPU bound and thus should continue to scale well across more and more nodes.
影响因子：
20.6
作者：
Fan J;Han F;Liu H
通讯作者：
Liu H