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.
Georgiy Levchuk and John Colonna-Romano, "Optimizing collaborative computations for scalable distributed inference in large graphs," Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460O (Presented at SPIE Defense + Security: April 17, 2018; Published: 7 June 2018); https://doi.org/10.1117/12.2305872.
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