HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures

TL;DR

HBMax significantly reduces memory usage in parallel influence maximization algorithms on large social networks by employing compression techniques, enabling larger problem sizes and faster processing without sacrificing accuracy.

Contribution

Introduces HBMax, a memory-efficient optimization for influence maximization that leverages compression of reverse reachability data on multicore architectures.

Findings

Achieves up to 82.1% memory reduction.

Provides 6.3% average speedup.

Handles large graphs like Twitter7 with 1.4 billion edges efficiently.

Abstract

Influence maximization aims to select k most-influential vertices or seeds in a network, where influence is defined by a given diffusion process. Although computing optimal seed set is NP-Hard, efficient approximation algorithms exist. However, even state-of-the-art parallel implementations are limited by a sampling step that incurs large memory footprints. This in turn limits the problem size reach and approximation quality. In this work, we study the memory footprint of the sampling process collecting reverse reachability information in the IMM (Influence Maximization via Martingales) algorithm over large real-world social networks. We present a memory-efficient optimization approach (called HBMax) based on Ripples, a state-of-the-art multi-threaded parallel influence maximization solution. Our approach, HBMax, uses a portion of the reverse reachable (RR) sets collected by the algorithm to learn the characteristics of the graph. Then, it compresses the intermediate reverse reachability information with Huffman coding or bitmap coding, and queries on the partially decoded data, or directly on the compressed data to preserve the memory savings obtained through compression. Considering a NUMA architecture, we scale up our solution on 64 CPU cores and reduce the memory footprint by up to 82.1% with average 6.3% speedup (encoding overhead is offset by performance gain from memory reduction) without loss of accuracy. For the largest tested graph Twitter7 (with 1.4 billion edges), HBMax achieves 5.9X compression ratio and 2.2X speedup.