converged ai workflows

3 publications :: all topics :: research areas :: full bibliography

DTIO: Data Stack for AI-driven Workflows

K. Bateman, N. Rajesh, J. Cernuda, L. Logan, B. Nicolae, F. Cappello, X.-H. Sun, A. Kougkas

The 37th International Conference on Scalable Scientific Data Management (SSDBM 2025)

A scalable I/O runtime that unifies disparate I/O stacks for converged HPC, Big Data, and ML workflows.

Viper: A High-Performance I/O Framework for Transparently Updating, Storing, and Transferring Deep Neural Network Models

J. Ye, J. Cernuda, N. Rajesh, K. Bateman, O. Yildiz, T. Peterka, A. Nigmetov, D. Morozov, A. Kougkas, X.-H. Sun, B. Nicolae

The 53th International Conference on Parallel Processing (ICPP'24)

An I/O framework that determines near-optimal checkpoint schedules and accelerates delivery of the latest model updates for DNN training and inference.

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HFlow: A Dynamic and Elastic Multi-Layered Data Forwarder

J. Cernuda, H. Devarajan, L. Logan, K. Bateman, N. Rajesh, J. Ye, A. Kougkas, X.-H. Sun

The 2021 IEEE International Conference on Cluster Computing (CLUSTER'21), September 7-10, 2021

A dynamic and elastic multi-layered data forwarding system leveraging a real-time data movement paradigm for HPC.

[PDF][BibTeX][Citation]