Performance modeling of computation and communication tradeoffs in vertex-centric graph processing clusters
Performance modeling of computation and communication tradeoffs in vertex-centric graph processing clusters
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DOI：
10.4108/icst.collaboratecom.2014.257474
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
Amir Abdolrashidi;Lakshmish Ramaswamy;David S. Narron
中科院分区：
文献类型：
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作者：
Amir Abdolrashidi;Lakshmish Ramaswamy;David S. Narron
最近已经提出了分布式以顶点为中心的图处理系统，以在大型图上执行不同类型的分析。这些系统利用了无共享集群的并行性。在这项工作中，我们提出了一个新的模型，这样的集群的性能成本。我们还定义了新的度量相关的工作负载平衡和集群处理大量的真实的图数据集的网络通信成本。我们实证研究不同的图形划分机制的影响和他们的权衡两个不同类别的图形处理算法。
Distributed vertex-centric graph processing systems have been recently proposed to perform different types of analytics on large graphs. These systems utilize the parallelism of shared nothing clusters. In this work we propose a novel model for the performance cost of such clusters.We also define novel metrics related to the workload balance and network communication cost of clusters processing massive real graph datasets. We empirically investigate the effects of different graph partitioning mechanisms and their tradeoff for two different categories of graph processing algorithms.