7 papers
IOAgent: Democratizing Trustworthy HPC I/O Performance Diagnosis Capability via LLMs
Chris Egersdoerfer, Arnav Sareen, Jean Luca Bez +3
As the complexity of the HPC storage stack rapidly grows, domain scientists face increasing challenges in effectively utilizing HPC storage systems to achieve their desired I/O per…
I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey
Noah Lewis, Jean Luca Bez, Surendra Byna
Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed h…
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
Kaveen Hiniduma, Zilinghan Li, Aditya Sinha +2
Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and secur…
AIDRIN 2.0: A Framework to Assess Data Readiness for AI
Kaveen Hiniduma, Dylan Ryan, Suren Byna +2
AI Data Readiness Inspector (AIDRIN) is a framework to evaluate and improve data preparedness for AI applications. It addresses critical data readiness dimensions such as data qual…
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI
Kaveen Hiniduma, Suren Byna, Jean Luca Bez +1
"Garbage In Garbage Out" is a universally agreed quote by computer scientists from various domains, including Artificial Intelligence (AI). As data is the fuel for AI, models train…
Parallel I/O Characterization and Optimization on Large-Scale HPC Systems: A 360-Degree Survey
Hammad Ather, Jean Luca Bez, Chen Wang +3
Driven by artificial intelligence, data science, and high-resolution simulations, I/O workloads and hardware on high-performance computing (HPC) systems have become increasingly co…