Distributed Multi-GPU Community Detection on Exascale Computing Platforms

Conference · Fri Jul 26 04:00:00 EDT 2024

DOI:https://doi.org/10.1109/IPDPSW63119.2024.00147· OSTI ID:2482258

Sattar, Naw Safrin ^[1]; Lu, Hao ^[1]; Wang, Feiyi ^[1]; Halappanavar, Mahantesh ^[2]

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1. Oak Ridge National Laboratory
2. BATTELLE (PACIFIC NW LAB)

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 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 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.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2482258
• Report Number(s): PNNL-SA-195868
• Country of Publication: United States
• Language: English

Related Subjects

graph algorithms
high performance comptuing