Alleviating Irregularity in Graph Analytics Acceleration

Abstract

Graph analytics is an emerging application which extracts insights by processing large volumes of highly connected data, namely graphs. The parallel processing of graphs has been exploited at the algorithm level, which in turn incurs three irregularities onto computing and memory patterns that significantly hinder an efficient architecture design. Certain irregularities can be partially tackled by the prior domain-specific accelerator designs with well-designed scheduling of data access, while others remain unsolved.