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Found 6 papers, 1 papers with code
dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training
Distributed training using multiple devices (e. g., GPUs) has been widely adopted for learning DNN models over large datasets.
Aryl: An Elastic Cluster Scheduler for Deep Learning
We introduce Aryl, a new cluster scheduler to address these problems.
Prediction of GPU Failures Under Deep Learning Workloads
To improve the precision and stability of predictions, we propose several techniques, including parallel and cascade model-ensemble mechanisms and a sliding training method.
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
Extensive experiments on various GNN models and large graph datasets show that BGL significantly outperforms existing GNN training systems by 20. 68x on average.
Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem
With MIG, A100 can be the most cost-efficient GPU ever for serving Deep Neural Networks (DNNs).
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
This mutual-training process between BO and the loss-prediction model allows us to limit the training steps invested in the BO search.
