You can find slides for the Ligra tutorial here.
Welcome! These documents will teach you about the Ligra Graph Processing Framework. Ligra is a lightweight framework for processing graphs in shared memory. It is particularly suited for implementing parallel graph traversal algorithms where only a subset of the vertices are processed in an iteration. The project was motivated by the fact that the largest publicly available real-world graphs all fit in shared memory. When graphs fit in shared-memory, processing them using Ligra can give performance improvements of up orders of magnitude compared to distributed-memory graph processing systems.
This document is split up into a number of sections.
Julian Shun and Guy E. Blelloch. Ligra: A Lightweight Graph Processing Framework for Shared Memory. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), pp. 135-146, 2013.
Julian Shun, Laxman Dhulipala, and Guy E. Blelloch. Smaller and Faster: Parallel Processing of Compressed Graphs with Ligra+. Proceedings of the IEEE Data Compression Conference (DCC), pp. 403-412, 2015.
Julian Shun. An Evaluation of Parallel Eccentricity Estimation Algorithms on Undirected Real-World Graphs. Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 1095-1104, 2015.
Julian Shun, Farbod Roosta-Khorasani, Kimon Fountoulakis, and Michael W. Mahoney. Parallel Local Graph Clustering. Proceedings of the International Conference on Very Large Data Bases (VLDB), 9(12), pp. 1041-1052, 2016.
Laxman Dhulipala, Guy E. Blelloch, and Julian Shun. Julienne: A Framework for Parallel Graph Algorithms using Work-efficient Bucketing. Proceedings of the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), pp. 293-304, 2017.