A Brief Survey of Algorithms, Architectures, and Challenges toward Extreme-scale Graph Analytics

The notion of networks is inherent in the structure, function and behavior of the natural and engineered world that surround us. Consequently, graph models and methods have assumed a prominent role to play in this modern era of Big Data, and are taking a center stage in the discovery pipelines of various data-driven scientific domains. In this paper, we present a brief review of the state-of-the-art in parallel graph analytics, particularly focusing on iterative graph algorithms and their implementation on modern day multicore/manycore architectures. The class of iterative graph algorithms covers a broad class of graph operations of varying complexities, from simpler routines such as Breadth-First Search (BFS), to polynomially-solvable problems such as shortest path computations, to NP-Hard problems such as community detection and graph coloring. We cover a set of common algorithmic abstractions used in implementing such iterative graph algorithms, state the challenges around parallelization on contemporary parallel platforms (including commodity multicores and emerging manycore platforms), and describe a set of approaches that have led to efficient implementations. We also report on advances in manycore architectural frameworks that have found application in parallel graph analytics. We conclude the paper identifying potential research directions, opportunities, and challenges that lay ahead in the path toward enabling graph analytics at exascale.