Presentation
SIGN IN TO VIEW THIS PRESENTATION Sign In
Towards AI-Driven Interfaces for Scientific Data Management
DescriptionScientific data management requires researchers to navigate fragmented toolchains spanning data gathering, resource allocation, application deployment, and analysis. While Large Language Models offer natural language interfaces for HPC tasks, existing approaches suffer from system-specific training dependencies and lack standardized tool integration. We present IOWarp-mcps, a comprehensive suite of Model Context Protocol (MCP) tools enabling AI-driven scientific data management across complete workflows. Our framework addresses large-scale scientific datasets through two core principles: chunked I/O access for memory-efficient data partitioning and label-based filtering for selective data reduction before model ingestion. We evaluate IOWarp-mcps across three scenarios: automated dataset discovery from the National Data Platform, molecular dynamics trajectory analysis from LAMMPS simulations, and parallel I/O benchmark deployment. Results demonstrate significant productivity improvements, with configuration tasks reduced from 5-10 minutes to approximately one minute. IOWarp-mcps bridges the gap between conversational AI and scientific computing, providing intuitive interfaces for complex data management operations.
Event Type
Workshop
TimeMonday, 17 November 20254:00pm - 4:05pm CST
Location230
Data Analytics
High Performance I/O, Storage, Archive, & File Systems
Storage
Livestreamed
Recorded
TP
W