Lecture
Graph Pattern Matching: Work-sharing
Description
This lecture covers the optimization of graph pattern matching through work-sharing techniques, including transitioning from sequential to parallel matching, addressing load imbalance, and utilizing work-stealing strategies. It also discusses experimental setups, such as the Social Network Benchmark, and emphasizes the importance of context-aware parallelization and node dependencies in pattern mining at scale.
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