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Found 5 papers, 1 papers with code
MLTCP: Congestion Control for DNN Training
We present MLTCP, a technique to augment today's congestion control algorithms to accelerate DNN training jobs in shared GPU clusters.
CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters
We present CASSINI, a network-aware job scheduler for machine learning (ML) clusters.
How to Build Low-cost Networks for Large Language Models (without Sacrificing Performance)?
We show that LLMs exhibit a unique communication pattern where only small groups of GPUs require high-bandwidth communication to achieve near-optimal training performance.
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice.
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
We propose to approximate this family of encoding functions through random deep neural networks.
