arXiv Machine Learning

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.

arXiv AI

Aug 11

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.

By Liad Gerstman, Aditya Dhakal, Dejan Milojicic, Avi Mendelson

efficiency benchmarks

arXiv Machine Learning

Jul 7

DBLP: Phase-Aware Bounded-Loss Transport for Burst-Resilient Distributed ML Training

arXiv:2605. 01989v2 Announce Type: replace Abstract: Distributed machine learning (ML) training has become a necessity with the prevalence of billion to trillion-parameter-scale models.

By Zechen Ma, Zixi Qu, Jinyan Yi, David Lin, Yashar Ganjali

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

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

Aug 7

ML-for-ML

arXiv:2608. 06046v1 Announce Type: cross Abstract: AI training workloads are growing rapidly, making their time, energy, and infrastructure costs increasingly important.

By Yutong Zhao, Noga H. Rotman, Gianni Antichi, Ran Ben Basat

fine-tuning

arXiv AI

Jun 9

Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads

arXiv:2606. 09200v1 Announce Type: cross Abstract: The rapid growth of large-scale machine learning (ML) has made distributed training across multiple GPUs a fundamental component of modern ML systems.

By Minyu Cui, Miquel Pericas

efficiency

arXiv AI

Jun 24

Decentralised AI Training and Inference with BlockTrain

arXiv:2606. 24722v1 Announce Type: new Abstract: Frontier AI training is increasingly shaped by access to dense, centrally controlled accelerator clusters.

By Peter Toth

llms

arXiv Machine Learning

Jul 9

Protocol Models: Scaling Decentralized Training with Communication-Efficient Model Parallelism

arXiv:2506. 01260v2 Announce Type: replace Abstract: Scaling models has led to significant advancements in deep learning, but training these models in decentralized settings remains challenging due to communication bottlenecks.

By Sameera Ramasinghe, Thalaiyasingam Ajanthan, Gil Avraham, Yan Zuo, Alexander Long

llms

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 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 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 10

Unifying Local Communications and Local Updates for LLM Pretraining

arXiv:2606. 11081v1 Announce Type: cross Abstract: Communication-efficient pre-training of LLMs is increasingly important as training draws on compute distributed across clusters, data centers, and lower-bandwidth links.

By Pietro Cagnasso, Eugene Belilovsky, Edouard Oyallon

llms benchmarks

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