Publication | Closed Access
Dynamic Graphs on the GPU
28
Citations
6
References
2020
Year
Unknown Venue
Cluster ComputingDynamic GraphsHash TableEngineeringGpu BenchmarkingComputer ArchitectureGraph DatabaseDynamic Graph StructureData StructureGpu ComputingData ScienceData IntegrationParallel ComputingComputational GeometryData ManagementComputer EngineeringComputer ScienceGpu ClusterGpu ArchitectureGraph TheoryCloud ComputingParallel Programming
We present a fast dynamic graph data structure for the GPU. Our dynamic graph structure uses one hash table per vertex to store adjacency lists and achieves 3.4-14.8x faster insertion rates over the state of the art across a diverse set of large datasets, as well as deletion speedups up to 7.8x. The data structure supports queries and dynamic updates through both edge and vertex insertion and deletion. In addition, we define a comprehensive evaluation strategy based on operations, workloads, and applications that we believe better characterize and evaluate dynamic graph data structures.
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