Acknowledgement and Facilities Statement

Acknowledgement

Please acknowledge Research Computing in all publications

The systems and technical support provided by Research Computing are essential to the success of many research projects at the University of Virginia. Proper recognition of these resources helps ensure that cutting edge computing facilities remain available to all University researchers.
When writing a research report, journal article, or any publication that requires citation of author contributions, please include an acknowledgement of UVA Research Computing.

A recommended format
The authors acknowledge Research Computing (rc.virginia.edu) at The University of Virginia for providing computational resources and technical support that contributed to the results reported in this publication.

Facilities Statement

Sample Language for Describing UVA Research Computing environments

Research Computing (UVA RC) serves as the principal center for computational resources and associated expertise at the University of Virginia (UVA). Each year UVA RC provides services to over 433 active (principal investigators) PIs that sponsor more than 2,463 unique users from 14 different schools/organizations at the University, maintaining a breadth of systems to support the computational and data intensive work of UVA’s researchers. 
UVA RC provides access to high-performance computing (HPC) resources and associated storage as a basic entitlement for all UVA PIs and their delegates. Access is limited by an allocation policy, but additional resources are available for purchase.

Standard Security Resources

High Performance Computing 

UVA RC’s HPC systems are designed with high-speed networks, high performance storage, GPUs, and large amounts of memory in order to support modern compute and memory intensive programs. UVA RC operates two HPC systems: Rivanna and Afton. In total, these systems are comprised of over 900 compute nodes, with a total of more than 48,000 X86 64-bit compute cores. Scheduled using Slurm, these resources can support over 1.5 PFLOP of peak CPU performance. PC nodes are equipped with between 375 GB and 1.5 TB of RAM to support applications that require small and large amounts of memory, and 55 nodes include various configurations of the NVIDIA general purpose GPU accelerators (RTX2080, RTX3090, A6000, V100, A40, A100 and H200), from 4- to 10-way.

UVA RC also acquires and maintains capability systems focused on addressing specific areas of high computational need. This includes an 18 node DGX BasePOD system with 8x A100 GPU devices per node. The BasePOD provides a shared memory space across all GPUs in the system allowing the system to work collectively on models with memory needs larger than what can be held in a single node. 

These capabilities will be supplemented in 2026 with 64 H200 GPU cards, 56 GPU B200 cards, and 8 GPU RTX PRO 6000 cards.

Data Storage

All researchers on UVA RC's systems have access to a high-performance parallel storage platform. This system provides 12PB (PetaBytes) of storage with sustained read and write speeds of up to 10 GB/sec. The integrity of the data is protected by daily snapshots. UVA RC also supports a second-tier storage solution, 12 PB, designed to address the growing need for resources that support data-intensive research by offering a lower cost, scalable solution. The system is tightly integrated with other UVA RC storage and computing resources in order to support a wide variety of research data life cycles and data analysis workflows. 

UVA RC provides researchers with access to a robust, high-performance standard security zone (SSZ) data storage ecosystem designed to support a broad range of computational and data-intensive research workflows. Core storage services include an SSZ Research Project high-performance parallel file system that delivers approximately 12 PB of usable capacity with sustained read and write performance of up to 10 GB/sec. This system is tightly integrated with UVA RC’s computing infrastructure and includes daily snapshotting to protect data integrity and enable rapid recovery from accidental deletion or corruption.

To support data-intensive SSZ research projects with diverse performance and cost requirements, UVA RC also operates a second-tier, scalable SSZ Research Standard storage platform providing approximately 16 PB of capacity. This tier is optimized for large-scale data analysis, long-term project storage, and workflows that benefit from cost-effective, high-throughput access, while remaining fully integrated with UVA RC compute resources.

UVA RC also provides an SSZ Weka-based scratch file system (10TB/user) designed specifically for temporary, high-throughput data access. This scratch platform is tightly coupled to UVA-RC’s compute infrastructure and delivers extremely low-latency, high-bandwidth performance optimized for large-scale parallel workflows such as AI/ML training, GPU-accelerated computing, simulation, and data staging. As a transient storage tier, it does not include snapshotting or long-term data retention.

In addition to these SSZ storage services, UVA-RC offers High Security Zone (HSZ) research storage solutions for projects with enhanced data security, compliance, or access-control requirements. These include an HSZ Research Standard file storage platform, providing approximately 13 PB of secure, scalable capacity, as well as a high-performance parallel HSZ Research Project file system, offering approximately 1.6 PB of project-scoped storage. Both HSZ storage systems are designed to meet institutional and regulatory security standards, support controlled access, and integrate seamlessly with approved HSZ VM and HPC compute resources.

Together, these storage platforms enable UVA RC to support the full research data lifecycle—from active computation and analysis to secure project storage and long-term data stewardship across a wide spectrum of scientific and scholarly domains.

Cloud Computing

UVA RC supports microservices in a clustered orchestration environment that leverages Kubernetes to automate the deployment and management of many containers in an easy and scalable manner. This cluster has 876 cores and 4.9TB of memory allocated to running containerized services, including one node with 4 x A100 GPUs. It also has over 300TB of cluster storage and can attach to UVA RC's broader storage offerings.

High-Security Resources

High Performance Computing

The High-Security HPC cluster (Rio) is a high-performance computing system specifically designed for the processing and analysis of controlled-access and highly sensitive data. It features high-speed networks, high-performance storage, and GPUs—including an NVIDIA HGX H200 GPU and L40s GPU—to support demanding computational tasks. Currently, Rio comprises 39 compute nodes, providing a total of 1,560 x86 64-bit compute cores. Each HPC node is equipped with 375 GB of RAM to accommodate memory-intensive applications. 

Situated within the high-security zone, Rio can only be accessed through Ivy Linux virtual machines, ensuring compliance with stringent security requirements for data storage and processing. Compute time is managed through service unit allocations and fair-share models, with Slurm serving as the job scheduler. This architecture provides a secure, efficient, and robust environment for handling sensitive research workloads.

These capabilities will be supplemented in 2026 with 36 GH200 Grace-Hopper nodes.

Cloud Computing

Ivy is a secure computing environment for researchers consisting of virtual machines (Linux and Windows) backed by a total of 45 nodes and 2048 cores. Researchers can use Ivy to process and store sensitive data with the confidence that the environment is secure and meets HIPAA, FERPA, or CUI requirements.

General Resources

Interactive Computing and Scientific Visualization

UVA RC supports specialized interfaces (i.e., Open OnDemand, FastX) and hardware for remote visualization and interactive computing. Interactive HPC systems allow real-time user inputs in order to facilitate code development, real-time data exploration, and visualizations. Interactive HPC systems are used when data are too large to download to a desktop or laptop, software is difficult or impossible to install on a personal machine, or specialized hardware resources (e.g., GPUs) are needed to visualize large data sets.

Data Centers, Network Connectivity, and Office Facilities

UVARC enables interdisciplinary research through its robust data center facilities with over 1.5 MW of IT capacity to support leading edge computational and data storage systems. UVA-RC's equipment occupies a data center near campus, connected to the 10 Gbps campus network. Dedicated 10 and 100 Gbps links to our regional optical network and Internet2 give our researchers the network capacity and capability needed to collaborate with researchers from around the world. A Globus data transfer node enables data access and transfers to transcend institutional credentials. Located in the Michie North building with over 3,000 sq feet of office space, UVA RC’s offices are a short shuttle ride away from the central UVA Grounds.

Expertise

UVA RC aggregates expertise to provide consulting and collaboration services to researchers addressing all levels of the Research Computing technology stack.

UVA RC's user support staff provide basic support and general onboarding through a help desk and regularly scheduled tutorials. Infrastructure support for hardware systems is delivered by a team of system administrators and analysts. Senior support staff have advanced degrees in relevant domains such as biology, imaging, physics, computer science and materials science, enabling in-depth collaboration on complex projects. 

The Data Analytics Center (DAC) serves as a central hub for computational services and expertise in data analytics, including bioinformatics, image processing, text analysis, and artificial intelligence. By offering specialized support and resources, the DAC connects UVA researchers with the advanced computing capabilities essential for their data-intensive research and analysis. The DAC provides a comprehensive suite of services designed to enhance research efforts. The services are organized into three primary areas: training and technical support, consultations, and collaborations. Training and technical support, offered as a general service, includes tutorials and technical assistance with research computing systems. Consultations, also free of charge, facilitate knowledge sharing and the design of analysis workflows through dedicated meetings. DAC team members are available for collaborations on grants. Collaborations involve more in-depth work than consultations and in general require a DAC member to be embedded into a researcher’s grant. The DAC members, all holding advanced degrees, are adept at reviewing published techniques and adapting them to specific domain use cases, ensuring tailored and effective support for researchers.

The Digital Technology Core (DTC) provides expertise to support the software development and remote data collection needs of faculty using smartphones, smartwatches, wearable sensors, and IoT devices. The DTC is a research engineering team with diverse expertise from complementary fields, including software engineering, statistical data analysis, repeated measures experimental design, and machine learning. The DTC provides a comprehensive suite of services including custom software solutions, data ingestion from third-party web APIs, and an always-on cloud environment for two-way interactions with research participants, such as sending push notifications to smartphones and collecting survey responses. Researchers can receive free consultations from DTC staff as they formulate their research protocols. For longer-term support, researchers may include DTC staff support in their grant applications.

For projects that require significant application development work, UVA RC maintains a Solutions & DevOps team capable of rapid iteration while leveraging non-traditional HPC technologies. Lastly, UVA RC’s Infrastructure Services team enables projects that may require custom hardware or configurations outside of the standard images. Beyond their availability for direct project support, together these teams provide the R&D and operations expertise needed to ensure that UVA RC is providing a modern research computing ecosystem for UVA researchers.