Ligra: a lightweight graph processing framework for shared memory

By Julian Shun, Guy E. Blelloch. ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming, 2013

1,034 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5355687e1dab8b770e7c79c8a626d8a4052eff43

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression (2022, background): “Ligra [21] A parallel singlemachine Reducing the memory consumption O(log3 M), M: sum of the sizes of the sets 7 3 3 3 3”
The GAP Benchmark Suite (2015, methodology): “To create our benchmark, we learned from the best practices of the community, and we co-developed this benchmark with our graph workload characterization [5].”
LASAGNE: Locality and Structure Aware Graph Node Embedding (2017, methodology): “PageRank implemented in the Ligra framework [26] for our implementation.”
Processing Data Where It Makes Sense: Enabling In-Memory Computation (2019, background): “A popular modern application is large-scale graph processing [87, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193].”
X-Stream: edge-centric graph processing using streaming partitions (2013, background): “For Pagerank, X-Stream is faster than Ligra at all thread counts.”
GridGraph: Large-Scale Graph Processing on a Single Machine Using 2-Level Hierarchical Partitioning (2015, background): “Ligra [23] is a shared-memory graph processing framework which provides two very simple routines for mapping over vertices and edges, inspiring GridGraph for the streaming interface.”
PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs (2019, background): “…graph processing systems: There are also several efforts aiming at leveraging multicore platforms for graph processing [39, 46, 53, 57, 65, 66, 92, 101] which focus on such issues as improving out-of-core accesses [39], selecting appropriate execution modes [65], supporting a…”
Gemini: A Computation-Centric Distributed Graph Processing System (2016, neutral): “While these systems are able to take advantage of multiple machines to achieve scalability, their performance is often unsatisfactory compared with state-of-the-art shared-memory counterparts [36, 47, 49, 57].”
EmptyHeaded: A Relational Engine for Graph Processing (2015, neutral): “H.2 [Information Systems]: Database Management System Engines”
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs (2020, background): “Previous distributed graph analytical systems [7]–[9] are designed for full graph computation expressed in the vertex-centric program paradigm, which is not suitable for GNN mini-batch training.”