# GPU-Accelerated Parallel Computer for Life Sciences Research

> **NIH NIH S10** · VANDERBILT UNIVERSITY · 2022 · $600,000

## Abstract

Project Summary/Abstract
 We request funds to purchase a high-performance, GPU-accelerated parallel computer to provide
financially sustainable support for computationally intensive NIH-funded biomedical research at Vanderbilt
University (VU) and Vanderbilt University Medical Center (VUMC). The proposed system consists of 20 Exxact
“TensorEX” compute nodes, each equipped with four nVidia A6000 “Ampere” GPUs, 512GB of RAM, dual 32-
core AMD EPYC CPUs, an 8TB nVME SSD for scratch I/O, and a dual-port Mellanox 25Gbps network interface.
 GPUs have achieved wide acceptance and application in the high-performance technical computing
(HPTC) and data science fields. Their simplified but highly parallel architectures yield processors that can do
a staggering number of fundamental arithmetic operations simultaneously, at a tiny fraction of the complexity
and therefore cost compared to a similar capability in a traditional CPU-based architecture. The intrinsic ~100-
fold price/performance advantage has driven the authors of many important HPTC codes and libraries to modify
their software to run on GPUs. There are now mature GPU implementations of many critical computationally-
intensive software tools which are key to accelerating the pace of discovery in our research programs.
 The proposed computer system will support research software that is well-known to perform best when
running on GPU coprocessors. Molecular dynamics (MD) codes such as AMBER and GROMACS are in high
demand, to support our structure-based drug design (SBDD) and personalized structural biology (PSB) efforts.
Cryogenic electron microscopy (Cryo-EM) and cryogenic electron tomography (Cryo-ET) image analysis tools
such as RELION leverage the massive investments our institution has recently made and continues to make in
these areas. Machine learning applications for ligand-based drug discovery, and high-dimensional statistical
modeling tools for systems biology are examples of GPU-accelerated tools that are both developed and applied
at Vanderbilt. Research in these areas (and more) would be significantly enhanced, accelerated, and more
cost-effective with access to the proposed instrument. That will translate into direct positive impacts on the
development of small molecule and antibody-based therapeutics, advances in personalized medicine, and
critical advancements in our understanding of disease-related mechanisms at atomic through cellular scales.
 The proposed system will be maintained at the Advanced Computing Center for Research and
Education (ACCRE), which has a large 24/7 on-call staff with extensive experience managing clusters of CPU
and GPU-based computers in support of biomedical research, including equipment from previous S10 awards.
This faculty-driven approach to computing has been extremely successful, making ACCRE a critical component
of the VU/VUMC research enterprise. Technical support at ACCRE is subsidized by institutional funds, further
reducing costs and barriers for...

## Key facts

- **NIH application ID:** 10415306
- **Project number:** 1S10OD032234-01
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Jarrod Anson Smith
- **Activity code:** S10 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $600,000
- **Award type:** 1
- **Project period:** 2022-07-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10415306

## Citation

> US National Institutes of Health, RePORTER application 10415306, GPU-Accelerated Parallel Computer for Life Sciences Research (1S10OD032234-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10415306. Licensed CC0.

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