RMACC HPC Symposium 2025

In today's research landscape, protecting scientific data from tampering and unauthorized access and adhering to compliance driven security measures is critical. Effective data management solutions must ensure integrity and immutability while enabling secure collaboration across institutions and research labs. By implementing policy-driven access controls, encryption, and compliance-driven security measures, organizations can safeguard sensitive research data against breaches and unauthorized modifications. This approach not only enhances data security but also ensures adherence to regulatory frameworks, fostering trust in the integrity and reproducibility of scientific discoveries while enabling secure, compliant data sharing.

A universal quantum computer can be used as a simulator capable of predicting properties of diverse quantum systems. Electronic structure problems in chemistry offer practical use cases around the hundred-qubit mark. This appears promising since current quantum processors have reached these sizes. However, mapping these use cases onto quantum computers yields deep circuits, and for pre-fault-tolerant quantum processors, the large number of measurements to estimate molecular energies leads to prohibitive runtimes. As a result, realistic chemistry is out of reach of current quantum computers in isolation. A natural question is whether classical distributed computation can relieve quantum processors from parsing all but a core, intrinsically quantum component of a chemistry workflow. Here, we incorporate quantum computations of chemistry in a quantum-centric supercomputing architecture, using up to 6400 nodes of the supercomputer Fugaku to assist a quantum computer with a Heron superconducting processor. We simulate the N2 triple bond breaking in a correlation-consistent cc-pVDZ basis set, and the active-space electronic structure of [2Fe-2S] and [4Fe-4S] clusters, using 58, 45 and 77 qubits respectively, with quantum circuits of up to 10570 (3590 2-qubit) quantum gates. We obtain our results using a class of quantum circuits that approximates molecular eigenstates, and a hybrid estimator. The estimator processes quantum samples, produces upper bounds to the ground-state energy and wavefunctions supported on a polynomial number of states. This guarantees an unconditional quality metric for quantum advantage, certifiable by classical computers at polynomial cost. For current error rates, our results show that classical distributed computing coupled to quantum computers can produce good approximate solutions for practical problems beyond sizes amenable to exact diagonalization.

The University of North Dakota (UND) Genomics Core has launched GenomEX 2.0, the first comprehensive and user-friendly bioinformatics platform powered by Oracle Cloud Infrastructure. This innovative platform enables biologists to seamlessly install over 13,000 bioinformatics tools, generate and execute custom code or command lines, and receive real-time guidance from an AI-based bioinformatics assistant—all through intuitive, one-click processes.
To support the computing requirements for any bioinformatics tools, the platform is powered by the Oracle Cloud Infrastructure that provides fully secured (built-in security features and compliance certifications), personalized (adjustable CPU/GPU numbers & memory/storage capacity), dedicated (resources available 24/7 without any queue) and customizable (users have administrator rights) cloud-based high-performance computing environments at unbeatable pricing.
Through the combined expertise of the UND Genomics Core and Oracle, GenomEX 2.0 emerges as a powerful and unique bioinformatics platform, providing every biologist with the freedom to explore biological data independently, regardless of their coding proficiency.

As research data volumes grow and mandates for data publication become more pervasive, automated means for managing these complex workflows to ensure data integrity have a growing role in modern science. In this session we will introduce Globus Flows, a foundational service for orchestrating secure and reliable data management tasks at scale, and Globus Compute, a service which enables you to execute functions on diverse remote systems. We will describe how Globus Flows and Compute fit into the Globus ecosystem of data and compute management services, and how flows can feed into downstream data portals, science gateways, and data commons, enabling search and discovery of data by the broader community. We will demonstrate how to run various Globus provided flows and discuss initiating flows with triggers and inserting compute tasks into your flows. We will conclude with an interactive tutorial detailing how to build custom flows using Jupyter notebooks and the Globus web app.

The next frontier in AI advancement isn’t just about algorithms—it’s about unlocking the wealth of hidden insights trapped within millions of files in HPC environments. While organizations focus on model architectures, the true bottleneck often lies in discovering and preparing relevant data buried in vast storage systems.
This presentation, featuring MetadataHub and a live demonstration, will reveal how intelligent metadata extraction and management transforms unstructured data into AI-ready assets by:

• Uncovering Hidden Context: Live metadata extraction demonstrating how MetadataHubcaptures content and contextual value, revealing unexpected connections between research datasets and enabling new AI training opportunities that would otherwise remain hidden.
• Automating Data Discovery: Demonstrating how MetadataHub automates metadata tagging to identify valuable training data across petabyte-scale storage, reducing data preparation time by up to 90%.
• Enhancing Model Quality: Exploring how rich metadata captured by MetadataHub improves AI model performance by providing better context and enabling more relevant training data selection.
• Scaling Efficiently: Showcasing metadata-driven automation with MetadataHub that optimizes data pipeline efficiency and resource utilization, including GPU/CPU performance, across HPC environments.

The session will highlight a real-world success story from the Zuse Institute Berlin, where MetadataHub unlocked 200 PB of previously underutilized research data for cutting-edge Generative AI applications. A 15-minute live demonstration will guide attendees through their journey—from data discovery to AI-ready datasets—highlighting practical challenges and solutions.
Attendees will leave with actionable strategies for implementing metadata-driven approaches in their own HPC workflows. By showcasing MetadataHub’s ability to extract content and contextual value, this session will demonstrate how metadata transforms unstructured data into a strategic advantage, accelerating AI initiatives and driving HPC innovation.

With the recent changes to the NIH Genomic Data Sharing Policy, more focus is being placed on compliance today. Organizations are looking for solutions to meet these new requirements. In this session we will discuss how Globus data management services allow users to confidently work with  controlled-access data. We will cover all the product features that address managing controlled-access data to enable compliance with NIST SP 800-171. We will demonstrate how with High Assurance collections the service meets these new requirements.