Fused Breadth-First Probabilistic Traversals on Distributed GPU Systems.
CoRR(2023)
摘要
Probabilistic breadth-first traversals (BPTs) are used in many network
science and graph machine learning applications. In this paper, we are
motivated by the application of BPTs in stochastic diffusion-based graph
problems such as influence maximization. These applications heavily rely on
BPTs to implement a Monte-Carlo sampling step for their approximations. Given
the large sampling complexity, stochasticity of the diffusion process, and the
inherent irregularity in real-world graph topologies, efficiently parallelizing
these BPTs remains significantly challenging.
In this paper, we present a new algorithm to fuse massive number of
concurrently executing BPTs with random starts on the input graph. Our
algorithm is designed to fuse BPTs by combining separate traversals into a
unified frontier on distributed multi-GPU systems. To show the general
applicability of the fused BPT technique, we have incorporated it into two
state-of-the-art influence maximization parallel implementations (gIM and
Ripples). Our experiments on up to 4K nodes of the OLCF Frontier supercomputer
($32,768$ GPUs and $196$K CPU cores) show strong scaling behavior, and that
fused BPTs can improve the performance of these implementations up to
34$\times$ (for gIM) and ~360$\times$ (for Ripples).
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