Keynote, PELGA workshop at Euro-Par
Graphs model social networks, human knowledge, and other vital information for business, governance, and academic practice. Although both industry and academia are developing and tuning many graph-processing algorithms and platforms, the performance of graph-processing platforms has never been explored or compared in-depth. Moreover, graph processing exposes new bottlenecks in traditional HPC systems (see differences in Top500 and Graph500 rankings).
In this talk, we introduce the LDBC Graphalytics benchmark, which focuses on batch full-graph analytics. Attendees will learn about methods and tools for performance evaluation and optimization for graph processing platforms, and hear about our view that the performance forms a dependency triangle Platform-Algorithm-Dataset. We will present real-world experiences with commonly used systems, in particular with graph-processing platforms such as Giraph, Graphmat, OpenG, PGX.D, and PowerGraph. Moving towards performance engineering, and in particular bottleneck analysis and performance diagnosis, we further focus on Granula, a component of LDBC Graphalytics for in-depth performance analysis for graph-processing systems with various data processing models.