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SmartNICs accelerate large-scale GNN training by reducing communication overhead · 2 sources tracked

Two new research papers propose methods to accelerate large-scale graph neural network (GNN) training by offloading tasks to SmartNICs. LGNNIC focuses on reducing communication overhead by performing neighbor sampling and quantization on SmartNICs, achieving significant speedups. SNI-GNN introduces in-network embedding prediction on SmartNICs to reduce inter-node communication, demonstrating substantial speedups and minimal accuracy loss. AI

IMPACT These approaches could significantly improve the efficiency and scalability of training large graph neural networks, enabling more complex applications.

RANK_REASON Two academic papers proposing novel methods for accelerating GNN training.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SmartNICs accelerate large-scale GNN training by reducing communication overhead · 2 sources tracked

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Two academic papers proposing novel methods for accelerating GNN training.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Liad Gerstman, Aditya Dhakal, Dejan Milojicic, Avi Mendelson ·

    LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs

    arXiv:2608.07733v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are widely used across domains such as natural sciences, social network analysis, chip design, and recommendation systems. However, as graph sizes grow, storing and processing them entirely on a single…

  2. arXiv cs.LG TIER_1 English(EN) · Guofan Yu, Sitian Chen, Zhenheng Tang, Xiaowen Chu, Amelie Chi Zhou ·

    SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction

    arXiv:2608.06441v1 Announce Type: new Abstract: Full-graph GNN training delivers high accuracy but scales poorly on multi-server clusters due to heavy, irregular inter-node embedding exchanges. We present SNI-GNN, a SmartNIC-assisted full-graph training system that reduces commun…