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Found 7 papers, 3 papers with code
Understanding Stragglers in Large Model Training Using What-if Analysis
Large language model (LLM) training is one of the most demanding distributed computations today, often requiring thousands of GPUs with frequent synchronization across machines.
RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts
In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
Stateful Large Language Model Serving with Pensieve
Large Language Models (LLMs) are wildly popular today and it is important to serve them efficiently.
New Characterizations and Efficient Local Search for General Integer Linear Programming
We propose a new local search framework that switches between three modes, namely Search, Improve, and Restore modes.
NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers
In this work, we propose a new fuzz testing approach for finding bugs in deep-learning compilers.
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
Our algorithm estimates the AME, a quantity that measures the expected (average) marginal effect of adding a data point to a subset of the training data, sampled from a given distribution.
Hop: Heterogeneity-Aware Decentralized Training
Based on a unique characteristic of decentralized training that we have identified, the iteration gap, we propose a queue-based synchronization mechanism that can efficiently implement backup workers and bounded staleness in the decentralized setting.
