Anti-Section Transitive Closure

The transitive closure of a graph is a new graph where every vertex is directly connected to all vertices to which it had a path in the original graph. Transitive closures are useful for reachability and relationship querying. Finding the transitive closure can be computationally expensive and requi...

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Bibliographic Details
Published in:Proceedings - International Conference on High Performance Computing pp. 192 - 201
Main Authors: Green, Oded, Du, Zhihui, Patel, Sanyamee, Xie, Zehui, Liu, Hang, Bader, David A.
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2021
Subjects:
Conferences
dynamic graph
GPU
Graphics processing units
Heuristic algorithms
High performance computing
Instruction sets
Memory management
parallel graph algorithm
Parallel processing
Transitive closure
ISSN:2640-0316
Online Access:Get full text
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Summary:The transitive closure of a graph is a new graph where every vertex is directly connected to all vertices to which it had a path in the original graph. Transitive closures are useful for reachability and relationship querying. Finding the transitive closure can be computationally expensive and requires a large memory footprint as the output is typically larger than the input. Some of the original research on transitive closures assumed that graphs were dense and used dense adjacency matrices. We have since learned that many real-world networks are extremely sparse, and the existing methods do not scale. In this work, we introduce a new algorithm called Anti-section Transitive Closure (ATC) for finding the transitive closure of a graph. We present a new parallel edges operation - anti-sections - for finding new edges to reachable vertices. ATC scales to massively multi-threaded systems such as NVIDIA's GPU with tens of thousands of threads. We show that the anti-section operation shares some traits with the triangle counting intersection operation in graph analysis. Lastly, we view the transitive closure problem as a dynamic graph problem requiring edge insertions. By doing this, our memory footprint is smaller. We also show a method for creating the batches in parallel using two different techniques: dual-round and hash. Using these techniques and the Hornet dynamic graph data structure, we show our new algorithm on an NVIDIA Titan V GPU. We compare with other packages such as NetworkX, SEI-GBTL, SuiteSparse, and cuSparse.
ISSN:2640-0316
DOI:10.1109/HiPC53243.2021.00033