The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: AI4S: 6th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
Authors: Abhishek Dwaraki (Hewlett Packard Enterprise Labs, Hewlett Packard Enterprise); Sreenivas. R. Sukumar, Christopher D. Rickett, Clarete Riana Crasta, and Harumi Kuno (Hewlett Packard Enterprise (HPE)); Michael Neal, Pankaj Pandey, and Ryan Yates (University of Mississippi); Karlon West (Hewlett Packard Enterprise (HPE)); Amar Gopal Chittiboyina and Ikhlas A. Khan (University of Mississippi); and John L. Byrne, Sekwon Lee, and David Emberson (Hewlett Packard Enterprise (HPE))
Abstract: Modern scientific discovery increasingly integrates simulations, data, and AI models. Existing systems rarely let scientists compose expressive queries that retrieve multi-modal datasets and invoke complex simulations or AI inferences. We introduce the Intelligent Data Search (IDS) framework to bridge this gap. IDS extends the Cray Graph Engine to provide a scalable in-memory datastore (feature, vector, and knowledge graph), a unified query engine combining keyword, set-theoretic, and linear-algebraic operators, a model repository for UDFs and pre-trained AI models, and a distributed multi-tier cache for intermediate and simulation outputs. We evaluate IDS on a life-sciences workflow with the NCNPR, integrating AlphaFold, AutoDock Vina, and Smith–Waterman within a single query. Results show strong HPC scaling, a complex “what-could-be” query executing millions of searches and thousands of inferences in seconds, and 5–15× end-to-end speedup from caching. IDS empowers scientists to ask and iterate model-driven “what-if” questions over petascale data with minimal latency.
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