Enhancing Scalability and Performance in Influence Maximization with Optimized Parallel Processing

TL;DR

This paper introduces EFFICIENTIMM, an optimized parallel algorithm that significantly improves the scalability and performance of influence maximization methods on shared memory systems, especially on large datasets.

Contribution

The paper proposes EFFICIENTIMM, a novel parallelization and memory optimization approach that enhances the efficiency of influence maximization algorithms like IMM.

Findings

Achieved an average 5.9x speedup over Ripples on 8 datasets.

Reduced cache misses by 357.4x on the Youtube graph.

Demonstrated improved scalability and memory access patterns.

Abstract

Influence 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 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. Additionally, on the Youtube graph, EFFICIENTIMM demonstrates a better memory access pattern with 357.4x reduction in L1+L2 cache misses as compared to Ripples.