# High Memory High-Performance Computer Cluster for Biomedical Research

> **NIH NIH S10** · BAYLOR COLLEGE OF MEDICINE · 2022 · $595,589

## Abstract

PROJECT SUMMARY
Biomedical investigators at Baylor College of Medicine (BCM) are increasingly dependent on high performance
computer cluster (HPC) based basic and integrative analysis of sequence and other high-dimensional data to
conduct their research. The Biostatistics and Informatics Shared Resource (BISR), a Shared Resource in the
College’s Advanced Technology Cores, currently manages a Beowulf style cluster as a service for
computational investigators. This cluster is highly used but is aging and does not have the type of high-memory
nodes needed for efficient timely processing of single-cell and single nucleus sequencing experiments, which
typically require 100-200 GB of memory per processor. In some cases, analyses simply cannot be run.
Although there are other HPC capabilities at BCM, for example in the Human Genome Sequencing Center or
within individual labs as well as other HPC resources in the region, none of these offer satisfactory solutions to
our users. Internal BCM-based systems are not designed for high-memory requiring jobs. None are open to
general users, and none are operated as a shared resource that ensures consistent up-times, high-speed
network connections, mountable storage and regulatorily compliant data protections. External resources are
simply not available to general users outside of the owner institution, or they are expressly designed for certain
types of jobs and place limits on usage that preclude their use for the types of runs needed by our users. The
new BISR HPC will fill a unique niche in providing high-memory HPC capabilities, as a formally managed
shared resource, to BCM biomedical investigators. In addition, we are not simply providing raw CPU hours to
computationally expert users who do not need any help. We provide assistance to investigators that straddle
wet and dry lab research by offering central software management and troubleshooting. The full potential of a
recently acquired S10-supported ultra-high throughput NovaSeq6000 sequencer and a recently CPRIT-funded
single-cell sequencing Core may fail to be realized without this computational support. We propose to build a
new high-memory GPU-enabled system specifically designed to support the burgeoning need of investigators
who are conducting large single-cell and/or single nucleus sequencing experiments. Typical experiments
involve sequences from 100’s to 10,000’s of cells/per biologic unit and 10’s to 1000’s of biologic units. These
experiments represent hundreds of thousands of genomic, transcriptomic and/or epigenomic sequences that
must be processed, aligned and integrated. The proposed system will include a front-end node, 22 compute
nodes each with 36 processors and 1 TB of memory, 1 GPU server with 8 GPU’s and 1PB direct attached
storage. Major Users and their projects will account for about 82% of usage. Demand for single-cell
sequencing is growing and we anticipate that there will be numerous additional users. Availability of this HPC
will...

## Key facts

- **NIH application ID:** 10414419
- **Project number:** 1S10OD032185-01
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** SUSAN G. HILSENBECK
- **Activity code:** S10 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $595,589
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10414419

## Citation

> US National Institutes of Health, RePORTER application 10414419, High Memory High-Performance Computer Cluster for Biomedical Research (1S10OD032185-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10414419. Licensed CC0.

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