Presentation
Enhancing Scalability and Performance in Influence Maximization with Optimized Parallel Processing
DescriptionInfluence Maximization (IM) is vital in viral marketing and biological network analysis for identifying key influencers. Given its NP-hard nature, approximate solutions are employed. This paper addresses scalability challenges in a scale-out shared memory system, by focusing on the state-of-the-art Influence Maximization via Martingales (IMM) benchmark. To enhance the work efficiency of the current IMM implementation, we propose EFFICIENTIMM with key strategies, including new parallelization scheme, NUMA-aware memory usage, dynamic load balancing and fine-grained adaptive data structures. Benchmarking on a 128-core CPU system with 8 NUMA nodes, EFFICIENTIMM demonstrated significant performance improvements, achieving an average 5.9x speedup over Ripples across 8 diverse SNAP datasets, when compared to the best execution times of the original Ripples framework. Also, on graph Youtube, EFFICIENTIMM is shown to have better memory access pattern with 357.4x reduction in L1+L2 cache misses as compared to
Ripples.
Ripples.
Author/Presenters
Event Type
Workshop
TimeSunday, 17 November 202411:20am - 11:45am EST
LocationB310
Graph Algorithms
Heterogeneous Computing
Programming Frameworks and System Software
W
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