arXiv AI

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.

arXiv Machine Learning

Aug 10

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.

By Guofan Yu, Sitian Chen, Zhenheng Tang, Xiaowen Chu, Amelie Chi Zhou

rag benchmarks safety data-engineering

arXiv Machine Learning

Jul 2

FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism

arXiv:2606. 22180v2 Announce Type: replace-cross Abstract: Graph embedding maps graph nodes into low-dimensional vectors to support applications such as recommendation, fraud detection, and graph-based retrieval-augmented generation (GraphRAG).

By Peng Fang, Arijit Khan, Ziqiang Wu, Zhenli Li, Yibo Zhou, Fang Wang, Dan Feng

rag benchmarks

arXiv Machine Learning

2d ago

MLCC: A Congestion Control Technique to Accelerate ML Training

arXiv:2402. 09589v2 Announce Type: replace-cross Abstract: We present MLCC, a novel technique to augment today's congestion control algorithms to accelerate DNN training jobs in shared GPU clusters in a fully distributed manner.

By Anton A. Zabreyko, Sanjoli Narang, Sudarsanan Rajasekaran, Manya Ghobadi

efficiency

arXiv Machine Learning

Jul 7

FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training

arXiv:2607. 05095v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) are widely used for learning from dynamic graphs in applications such as recommendation, social network analysis, and traffic forecasting.

By Yushu Cai, Qingrui Zhu, Lei Liu, Kai Sheng, Hao Chen, Xin He

benchmarks

arXiv AI

Aug 3

Topology-Aware Data Movement for Disaggregated GPU Inference

arXiv:2607. 28633v1 Announce Type: cross Abstract: Disaggregated LLM inference creates a datacenter networking problem that no existing system solves correctly.

By Sanjeev Rao Ganjihal

llms efficiency

arXiv Machine Learning

Jul 9

Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization

arXiv:2607. 06922v1 Announce Type: new Abstract: Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers.

By Shuo Huai, Di Liu, Hao Kong, Weichen Liu, Ravi Subramaniam, Christian Makaya, Qian Lin

efficiency benchmarks safety

arXiv Machine Learning

Jul 21

Taurus: Accelerating Out-of-Core Graph Neural Network Inference on Billion-Scale Graphs

arXiv:2607. 17374v1 Announce Type: cross Abstract: Graph Neural Network (GNN) inference on billion-scale graphs is challenging due to the large memory footprint of features and embeddings and high disk I/O costs in out-of-core settings.

By Pranjal Naman, Yogesh Simmhan

rag efficiency

arXiv Machine Learning

Jul 28

Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANs

arXiv:2607. 23115v1 Announce Type: cross Abstract: This paper aims to enable computation- and communication-efficient GPU sharing across devices within local area networks (LANs), facilitating ubiquitous AI inference on heterogeneous personal devices.

By Zhihao Xu, Hao Zhong, Zeting Zhou, Yuhang Xu, Haoyu Tong, Wei Wang, Jinshan Chen, Keqiang He, Chong Zhu, Shengzhong Liu, Fan Wu, Guihai Chen

efficiency benchmarks

arXiv Machine Learning

Jun 24

FuseSampleAgg: One-Pass Neighborhood Estimation for Budgeted Knowledge-Graph Refresh and Validation

arXiv:2511. 13645v2 Announce Type: replace Abstract: Operational knowledge-graph (KG) pipelines in networking and cybersecurity increasingly need to refresh embeddings under strict time, memory, and audit budgets, especially as curated feeds and LLM-assisted extraction accelerate KG updates.

By Aleksandar Stankovi\'c, Haoran Du, Xinming Wang

llms rag benchmarks

arXiv Machine Learning

Jun 10

ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling

arXiv:2606. 10440v1 Announce Type: cross Abstract: Distributed machine learning (ML) is a key paradigm for today's large-scale artificial intelligence applications.

By William Won, Jinsun Yoo, Tuan Ta, Moumita Dey, Andy Balogh, Pradosh Datta, Furkan Eris, Conor Green, Winston Liu, Changhai Man, Kingshuk Mandal, Amos Rai, Vinay Ramakrishnaiah, Ruchi Shah, David Sidler, Harsh Sikhwal, Hanjiang Wu, Tushar Krishna, Bradford M. Beckmann

arXiv AI

Jun 2

Lodestar: An Online-Learning LLM Inference Router

arXiv:2606. 00946v1 Announce Type: cross Abstract: Efficiently serving large language model (LLM) inference tasks is crucial both for user-perceived latency such as time-to-first-token (TTFT) and for GPU utilization.

By Gangmuk Lim, Wanyu Zhao, Brighten Godfrey, Jiaxin Shan, Le Xu, Liguang Xie

llms benchmarks

arXiv AI

Jul 23

Fine-grained Computation-Communication Overlap via Tile-level Signaling and Scheduling for Mixture-of-Experts

arXiv:2607. 19539v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures increase model capacity without proportionally increasing computation cost and have become a key building block for scaling large language models (LLMs) to trillion-parameter regimes.

By Minyu Cui, Anna Wingkvist, Morgan Ericsson

llms benchmarks

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