<span class="var-sub_title">There Are Trillions of Little Forks in the Road: Choose Wisely! -- Estimating the Cost and Likelihood of Success of Constrained Walks to Optimize a Graph Pruning Pipeline</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

IA^3 2018: 8th Workshop on Irregular Applications: Architectures and Algorithms


There Are Trillions of Little Forks in the Road: Choose Wisely! -- Estimating the Cost and Likelihood of Success of Constrained Walks to Optimize a Graph Pruning Pipeline

Authors: Nicolas Tripoul (University of British Columbia), Tahsin Reza (University of British Columbia), Geoffrey Sanders (Lawrence Livermore National Laboratory), Roger Pearce (Lawrence Livermore National Laboratory), Matei Ripeanu (University of British Columbia)

Abstract: We have developed [Reza et al. SC18] a highly scalable algorithmic pipeline for pattern matching in labeled graphs and demonstrated it on trillion-edge graphs. This pipeline: (i) supports arbitrary search patterns, (ii) identifies all the vertices and edges that participate in matches - offering 100% precision and recall, and (iii) supports realistic data analytics scenarios. This pipeline is based on graph pruning: it decomposes the search template into individual constraints and uses them to repeatedly prune the graph to a final solution.

Our current solution, however, makes a number of ad-hoc intuition-based decisions with impact on performance. In a nutshell these relate to (i) constraint selection - which constraints to generate? (ii) constraint ordering - in which order to use them? and (iii) individual constraint generation - how to best verify them? This position paper makes the observation that by estimating the runtime cost and likelihood of success of a constrained walk in a labeled graph one can inform these optimization decisions. We propose a preliminary solution to make these estimates, and demonstrate - using a prototype shared-memory implementation - that this: (i) is feasible with low overheads, and (ii) offers accurate enough information to optimize our pruning pipeline by a significant margin.


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