Optimizing collaborative computations for scalable distributed inference in large graphs
Optimizing collaborative computations for scalable distributed inference in large graphs
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DOI：
10.1117/12.2305872
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
G. Levchuk;John Colonna-Romano
中科院分区：
文献类型：
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作者：
G. Levchuk;John Colonna-Romano
在本文中，我们研究了两种优化分布式协作计算的方法：（a）数据分区，利用局部性来减少本地计算之间的数据依赖性；（b）计算聚合，减少本地分区之间的通信负载。我们分析了此类优化的好处及其对消息传递处理模型的效用。这是一类广泛用于一系列领域和应用的通用图分析，包括计算机视觉、活动识别、社交网络分析、知识挖掘和半监督推理。所描述的优化方法将提高在分布式环境中实现关系数据分析的性能，包括云计算、图形处理单元、协作多代理系统或专用芯片板。
In this paper, we study two methods to optimize distributed collaborative computations: (a) data partitioning, which exploits locality to reduce data dependencies between local computations, and (b) computation aggregation, which reduces communication load between local partitions. We analyze the benefits of such optimizations and their utility for message-passing processing model. This is a class of general-purpose graph analytics widely used in a range of domains and applications, including computer vision, activity recognition, social network analysis, knowledge mining, and semi-supervised inference. Described optimization methods will improve performance of implementing relational data analytics in distributed environments, including cloud computing, graphical processing units, collaborative multi-agent systems, or specialized chip-boards.