- By:
- Sattar, Naw Safrin ; Lu, Hao ; Wang, Feiyi ; Halappananvar, Mahantesh
- Page Number:
- 815-824
- Book Title:
- 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
- Publication Date:
- March 12, 2026
- Publisher Location:
- IEEE Computer Society, San Francisco, California, United States of America
- Conference Name:
- 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
- Conference Location:
- San Francisco, California, United States of America
- Conference Sponsor:
- IEEE
Abstract
Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (Distributed Parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.
