2019 · 59 citations · 48 references
ComputingComputer EngineeringComputer ScienceComputer GraphicsParallel AlgorithmsParallel ComputingGraphics Processing Unit ArchitectureGraphics Processing Unit ComputingGraphics Processing UnitsHardware AccelerationParallel ArchitecturesParallel Computing SystemsAsynchronous ExecutionComparative EvaluationComparative Performance EvaluationExecution Time AnalysisGpu ArchitectureGraph ProcessingParallel Graph AlgorithmsParallel Processing
In general, the performance of parallel graph processing is determined by three pairs of critical parameters, namely synchronous or asynchronous execution mode (Sync or Async), Push or Pull communication mechanism (Push or Pull), and Data-driven or Topology-driven traversing scheme (DD or TD), which increases the complexity and sophistication of programming and system implementation of GPU. Existing graph-processing frameworks mainly use a single combination in the entire execution for a given application, but we have observed their variable and suboptimal performance.
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A faster algorithm for betweenness centrality*
Ulrik Brandes · Journal of Mathematical Sociology · 2001 · 4.9K citations · Full text
A bridging model for parallel computation
Leslie G. Valiant · Communications of the ACM · 1990 · 3.7K citations · Full text
Grzegorz Malewicz, Matthew H. Austern, Aart J. C. Bik et al. · 2010 · 3.5K citations