6 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…
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…
Data Readiness for AI: A 360-Degree Survey
Kaveen Hiniduma, Suren Byna, Jean Luca Bez
Artificial Intelligence (AI) applications critically depend on data. Poor quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evalu…