Exécution de notre outil de synthèse sur un nouvel article
Vérifiez l’article ci-dessous, puis lancez sa synthèse.
Graph Colouring as a Challenge Problem for Dynamic Graph Processing on Distributed Systems
Scott Sallinen (Lawrence Livermore National Laboratory, University of British Columbia), Keita Iwabuchi (Tokyo Institute of Technology, Lawrence Livermore National Laboratory), Suraj Poudel (Lawrence Livermore National Laboratory, University of Alabama at Birmingham), Maya B. Gokhale (Lawrence Livermore National Laboratory), Matei Ripeanu (University of British Columbia), Roger Pearce (Lawrence Livermore National Laboratory)
Résumé : An unprecedented growth in data generation is taking place. Data about larger dynamic systems is being accumulated, capturing finer granularity events, and thus processing requirements are increasingly approaching real-time. To keep up, data-analytics pipelines need to be viable at massive scale, and switch away from static, offline scenarios to support fully online analysis of dynamic systems. This paper uses a challenge problem, graph colouring, to explore massive-scale analytics for dynamic graph processing. We present an event-based infrastructure, and a novel, online, distributed graph colouring algorithm. Our implementation for colouring static graphs, used as a performance baseline, is up to an order of magnitude faster than previous results and handles massive graphs with over 257 billion edges. Our framework supports dynamic graph colouring with performance at large scale better than GraphLab's static analysis. Our experience indicates that online solutions are feasible, and can be more efficient than those based on snapshotting.