# Beagle-3: A Shared GPU Cluster for Biomolecular Sciences

> **NIH NIH S10** · UNIVERSITY OF CHICAGO · 2020 · $1,997,947

## Abstract

Project Summary/Abstract
Biomedical research depends increasingly on structural and dynamical information about the networks of
macromolecular assemblies that underlie all biological function. High-performance computing (HPC), modeling,
and large-scale simulations are expected to increasingly play a crucial role in this process. This situation is
particularly true in the case of biomolecular systems. Our understanding begins with structural information.
Recent advances in cryo-electron microscopy (cryo-EM) technology have led to a “resolution revolution” in
structural biology and is now possible to reconstruct 3D molecular structures at Å-scale resolution from the
analysis of 2D cryo-EM images. However, the treatment of cryo-EM data leading to the determination of
molecular structure at atomic resolution is computationally intensive. Ultimately, a genuine mechanistic
understanding of complex biomolecular systems will be recognized by the ability to make accurate quantitative
predictions of structure, dynamics and function from computational models. In particular, the ability to create a
virtual reality through classical molecular dynamics (MD) simulations has now become an integral part of the
investigation process in biomedical research. Our NIH-funded research projects rely on intensive
computational analysis. The acquisition of the Beagle supercomputer in 2010 (and its upgrade in 2015), which
was made possible by grants from the NIH, National Center for Research Resources (NCRR), has spurred the
emergence of a vibrant computational community across UChicago and the wider Chicago region.
We propose to continue the Beagle project’s success by expanding into a 3rd phase that addresses the critical
HPC needs of fundamental NIH-funded research in biomolecular structural and computation. Experience
shows that HPC is achieved by a combination of strong CPU (Central Processing Unit) and GPU (Graphics
Processing Unit). Pioneered in 2007 by NVIDIA, a GPU has a massively parallel architecture consisting of
thousands of small but efficient processors designed to handle multiple tasks simultaneously. GPU-accelerated
computing is now commonly used for a range of scientific computations in science and engineering;8 it
achieves unprecedented performance by offloading compute-intensive portions of the application to the GPU,
while the remainder of the code runs on the CPU. The need to meet our scientific objectives and the very large
demand for high performance computing resources on campus to support NIH-funded research projects
justifies this request for a state-of-the-art GPU cluster. The proposed Beagle-3 — A Shared GPU Cluster for
Bimolecular Science — will create new synergies between a multidisciplinary group of users, enable
quantitative assessment and validation of biomolecular models and significantly increase efficiency and
productivity.

## Key facts

- **NIH application ID:** 9940542
- **Project number:** 1S10OD028655-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** BENOIT ROUX
- **Activity code:** S10 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,997,947
- **Award type:** 1
- **Project period:** 2020-09-10 → 2022-09-09

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9940542

## Citation

> US National Institutes of Health, RePORTER application 9940542, Beagle-3: A Shared GPU Cluster for Biomolecular Sciences (1S10OD028655-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9940542. Licensed CC0.

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