/tag/rivanna

  • Rivanna Maintenance: Sept 22, 2020

    Rivanna will be down for maintenance on Tuesday, September 22, beginning at 8:30 a.m. It is expected to return to service later in the day. RC engineers will be installing new hardware that is needed to stabilize the /scratch filesystem. You may continue to submit jobs until the maintenance period begins, but if the system determines your job will not have time to finish, it will not start until Rivanna is returned to service. If you have any questions or concerns about the maintenance period, please contact our user support team at hpc-support@virginia.edu.
  • Automated Image Labeling and Iterative Learning - Live Seminar: September 15, 2020

    MathWorks engineers will offer a free live webinar on September 15th from 2:00 to 3:00 Eastern time.
  • Deep Learning for Neuroscience - Live Seminar: September 22, 2020

    MathWorks engineers will offer a free live webinar on September 22th from 2:00 to 3:30 Eastern time.
  • R Updates: June 17, 2020

    During the June maintenance, we made changes to R which will affect how your R programs run on Rivanna. A brief description of the changes is as follows:
  • R Updates: June 17, 2020

    During the June maintenance, we will make changes to R which will affect how your R programs run on Rivanna. Below is a list of the changes and how they will affect your code. 1. The gcc-built versions of R will be updated to goolf-built versions. Instead of loading gcc before loading R, you will need to load goolf or gcc openmpi. For example: module load goolf R/4.0.0. Remember to update any SLURM scripts that have module load gcc R or module load gcc R/3.x.x. 2. The locations of the R libraries will be updated. We are changing the locations of the R libraries (i.
  • Transitioning to New R Modules: June 17, 2020

    The recommended steps for transitioning your R programs after the June maintenance are as follows: Determine which version of R you will be using (e.g., R/3.6.3). Open a terminal window on Rivanna and load the version of R that you chose in step #1 (e.g., module load goolf R/3.6.3). (Optional) Run our script to rebuild your existing R library for the newer version of R. For example, if you had been using R/3.5.1 and are switching to R/3.6.3, type the following in the terminal window: updateRlib 3.5.1 . Make sure that you have loaded any other modules (e.g., curl, gdal) that your packages may need.
  • Rivanna Maintenance: June 17, 2020

    You may continue to submit jobs until the maintenance period begins, but if the system determines your job will not have time to finish, it will not start until Rivanna is returned to service.


  • Rivanna Queues

    Several queues (or “partitions”) are availble to users for different types of jobs. One queue is restricted to single-node (serial or threaded) jobs; another for multinode parallel programs, and others are for access to specialty hardware such as large-memory nodes or nodes offering GPUs. Partition Max time per job Max nodes per job Max cores per job Max memory per core Max memory per node per job SU Charge Rate standard 7 days 1 40 9GB 375GB 1.00 parallel 3 days 45 900 9GB 120GB 1.00 largemem 4 days 1 16 64GB 975GB 1.
  • RC Acquires New Accounting Management Software for Rivanna

    Research Computing will be activating a new accounting management package for Rivanna on June 17, 2020. The software was purchased from Adaptive Computing, which specializes in advanced management applications for high-performance systems. Rivanna users can expect to see more accurate reporting on their Service Unit (SU) balances and burn rates. Information on usage by individual members of an allocation group will also be available. Commands such as allocations will remain but will reflect the new accounting. Users should be aware that the new accounting system implements “liens” on running jobs, and that the SUs requested for each job will be held in a reserved pool until the job completes.
  • Instructional Use of Rivanna

    Instructors can request instructional allocations on Rivanna for classes and extended workshops. These allocations are time-limited and generally allow access to a restricted set of nodes and only one special SLURM partition, but are otherwise equivalent to any allocation. Resource Availability Hardware and Partition Instructional allocations use a dedicated instructional partition. The standard allocation is 25,000 SUs for the semester during which the course is conducted. For workshops, the allocation will persist during the workshop and for two days subsequent to it. Class allocations also can access 50GB of shared storage temporarily. The standard number of cores is 20 on one node.
  • High Performance Computing

    Research Computing supports all UVA researchers who are interested in writing code to address their scientific inquiries. Whether these programming tasks are implemented interactively, in a series of scripts or as an open-source software package, services are available to provide guidance and enable collaborative development. RC has specific expertise in object oriented programming in Matlab, R, and Python. Examples of service areas include: Collaborating on package development Reviewing and debugging code Preparing scripts to automate or expedite tasks Developing web interfaces for interactive data exploration Advising on integration of existing software tools UVA has two local computational facilities available to researchers: Rivanna and Ivy.
  • Rivanna HPC Software

    Overview Research Computing at UVA offers a variety of standard software packages for all Rivanna users. We also install requested software based on the needs of the high-performance computing (HPC) community as a whole. Software used by a single group should be installed by that group’s members, ideally on leased storage controlled by the group. Departments with a set of widely-used software packages may install them to the lsp_apps space. The Research Computing group also provides limited assistance for individual installations. For help installing research software on your PC, please contact Research Software Support at res-consult@virginia.edu. Software Modules and Containers Software on Rivanna is accessed via environment modules or containers.
  • Building Your Code on Rivanna

    Building your Application Creating an executable from source with a compiled language requires two steps, compiling and linking. The combination of these is generally called building. The output of the compiler is generally an object file, which on Unix will end in a .o suffix. Object files are machine code and are not human-readable, but they are not standalone and cannot be executed. The linker, which is usually invoked through the compiler, takes all object files, along with any external libraries, and creates the executable (also called a binary). Compilers are invoked on source files with a line such as
  • Running a Bioinformatics Software Pipeline with Wdl/Cromwell

    WDL (pronounced widdle) is a workflow description language to define tasks and workflows. WDL aims to describe tasks with abstract commands that have inputs, and once defined, allows you to wire them together to form complex workflows. Learn More CROMWELL is the execution engine (written in Java) that supports running WDL scripts on three types of platforms: local machine (e.g. your laptop), a local cluster/compute farm accessed via a job scheduler (e.g. SLURM, GridEngine) or a cloud platform (e.g. Google Cloud or Amazon AWS). Learn More Introduction Pre-requisites: This tutorial assumes that you have an understanding of the basic structure of a WDL script.
  • Introduction to Databases

    There are two main families of databases: Relational and NoSQL. Relational databases store information in an orderly, column, row, and table schema. They “relate” the tables together to present different views of the data. NoSQL databases are much less structured. This means they can store different data alongside each other – which makes things both easier to store but harder to query across. Relational Databases (RDBMS) Most users have at least heard of relational databases like: MySQL / MariaDB PostgreSQL Microsoft SQL Server Oracle Relational databases operate on the concepts of tables, relations, indexes, SQL, CRUD operations, and joins.
  • SSH Keys

    Users can authenticate their SSH sessions using either a password or an ssh key. The instructions below describe how to create a key and use it for password-less authentication to your Linux instances. About SSH Keys SSH keys are a pair of encrypted files that are meant to go together. One half of the pair is called the “private” key, and the other half is the “public” key. When users use the private key to connect to a server that is configured with the public key, the match can be verified and the user is signed in. Or, put it more simply, when data is encrypted using one half of the key, it can be decrypted using the other half.
  • Software Containers

    Overview Containers bundle an application, the libraries and other executables it may need, and even the data used with the application into portable, self-contained files called images. Containers simplify installation and management of software with complex dependencies and can also be used to package workflows. Singularity is a container application targeted to multi-user, high-performance computing systems. It interoperates well with SLURM and with the Lmod modules system. Singularity can be used to create and run its own containers, or it can import Docker containers. Creating Singularity Containers To create your own image from scratch, you must have root privileges on some computer running Linux (any version).
  • Rivanna Software Updates: March 11, 2020

    The Rivanna maintenance has been completed on March 11 and the system is back in service. The following software modules have been removed from Rivanna during the maintenance period. Please use the suggested newer versions: gcc/5.4.0 & toolchains -> 7.1.0 All modules that depend on gcc/5.4.0 are now available under gcc/7.1.0. The only exception is cushaw3/3.0.3. Please contact us if you need to use it. pgi/19.7 & toolchains -> 19.10 All modules that depend on pgi/19.7 are now available under pgi/19.10. anaconda/5.2.0-py2.7 -> 2019.10-py2.7 All modules that depend on anaconda/5.2.0-py2.7 are now available under anaconda/2019.
  • Debuggers and Profilers

    Debuggers To use a debugger, it is necessary to rebuild your code with the -g flag added. All object files must be removed anytime compiler flags change. If you have a Makefile run make clean if it is available. The program must then be run under the control of the debugger. For example, if you are using gdb, you run gdb ./myexec Adding debugging flags generally disables any optimization flags you may have added, and can slow down the code. Please remember to recompile with -g removed once you have found your bugs. Gnu Debugger (gdb) and Profiler (gprof) The Gnu Compiler Collection compilers are free, open-source tools.
  • Intel on Rivanna

    Description Intel C and C++ compilers Software Category: compiler For detailed information, visit the Intel website. Available Versions To find the available versions and learn how to load them, run: module spider intel The output of the command shows the available Intel module versions. For detailed information about a particular Intel module, including how to load the module, run the module spider command with the module’s full version label. For example: module spider intel/18.0 ModuleVersion Module Load Command intel18.0 module load intel/18.0 intel20.0 module load intel/20.0 Compiler For general information on building code using compilers, please see our How-To pages:
  • Custom Jupyter Kernels

    You can create custom kernels from an Anaconda environment or a Singularity container. In both cases you’ll need to install the ipykernel package. Jupyter kernel based on Anaconda environment To create a custom kernel of the Anaconda environment myenv that uses Python 3.7: module load anaconda/2019.10-py3.7 conda create -n myenv python=3.7 ipykernel <other_packages> source activate myenv python -m ipykernel install –user –name myenv –display-name "My Env" Note: You can customize the display name for your kernel. It is shown when you hover over a tile in JupyterLab. If you do not specify a display name, the default Python [conda env:<ENV_NAME>] will be shown.
  • Rivanna Maintenance: March 11, 2020

    Rivanna will be down for maintenance on Wednesday, March 11, beginning at 6 a.m. You may continue to submit jobs until the maintenance period begins, but if the system determines your job will not have time to finish, it will not start until Rivanna is returned to service. Rivanna is expected to return to service later in the day. The following software modules will be removed from Rivanna during the maintenance period (please use the suggested newer versions): gcc/5.4.0 & toolchains -> 7.1.0 All modules that depend on gcc/5.4.0 will be available under gcc/7.1.0. The only exception is cushaw3/3.
  • PyTorch on Rivanna

    Description PyTorch is an open source Python package to create deep learning networks. The latest PyTorch versions are now provided as prebuilt Singularity containers on Rivanna. The basic concept of running Singularity containers on Rivanna is described here. Similar to other popular deep learning frameworks like TensorFlow, Theano and CNTK, computations supported by the PyTorch package can be accelerated on general purpose graphics processing units (GPUs). All PyTorch container images provided on Rivanna require access to a GPU node. Access to GPU nodes is detailed in the sections below. PyTorch on Rivanna The PyTorch Singularity module is available on Rivanna:
  • Bioinformatics Resources on Rivanna

    The UVA research community has access to numerous bioinformatics software installed directly or available through the bioconda Python modules. Click here for a comprehensive list of currently-installed bioinformatics software. Popular Bioinformatics Software Below are some popular tools and useful links for their documentation and usage: .tg {border-collapse:collapse;border-spacing:0;border-color:#ccc;} .tg td{font-family:Arial, sans-serif;font-size:14px;padding:10px 5px;border-style:solid;border-width:0px;overflow:hidden;word-break:normal;border-color:#ccc;color:#333;background-color:#fff;} .tg th{font-family:Arial, sans-serif;font-size:14px;font-weight:normal;padding:10px 5px;border-style:solid;border-width:0px;overflow:hidden;word-break:normal;border-color:#ccc;color:#333;background-color:#f0f0f0;} .tg .tg-hy9w{background-color:#eceeef;border-color:inherit;vertical-align:middle;} .tg .tg-dc35{background-color:#f9f9f9;border-color:inherit;vertical-align:middle;} .tg .tg-hy9w-nw{background-color:#eceeef;border-color:inherit;vertical-align:middle;white-space:nowrap;} .tg .tg-dc35-nw{background-color:#f9f9f9;border-color:inherit;vertical-align:middle;white-space:nowrap;} .tg .tg-0qmj{font-weight:bold;background-color:#eceeef;border-color:inherit;vertical-align:middle;} .scroll thead, .scroll tbody {display: block} .scroll tbody {overflow-y: auto; height: 500px;} .scroll thead tr:after {content: ‘';overflow-y: scroll; visibility: hidden; height: 0;} Tool Version Description Useful Links BEDTools 2.
  • A Short MPI Tutorial

    Tutorials and books on MPI A helpful online tutorial is available from the Lawrence Livermore National Laboratory. The following books can be found in UVA libraries: Parallel Programming with MPI by Peter Pacheco. Using MPI : Portable Parallel Programming With the Message-Passing Interface by William Gropp, Ewing Lusk, and Anthony Skjellum. Using MPI-2: Advanced Features of the Message- Passing Interface by William Gropp, Ewing Lusk, and Rajeev Thakur. MPI: The Complete Reference : The MPI Core by Marc Snir, Steve Otto, Steven Huss-Lederman, David Walker, and Jack Dongarra. MPI: The Complete Reference : The MPI-2 Extensions by William Gropp, Steven Huss-Lederman, Andrew Lumsdaine, Ewing Lusk, Bill Nitzberg, and Marc Snir.
  • Building and Running MPI Code

    Building an MPI Code All implementations provide wrappers around the underlying compilers that simplify compilation. As it is very important to use the headers that correspond to a given library, users are urged to make use of the wrappers whenever possible. For OpenMPI and MVAPICH2 these are: mpicc (C) mpicxx (C++) mpif90 (Fortran free or fixed format) For Intel MPI these use gcc/g++/gfortran by default, which is generally not recommended; to use the Intel compilers the corresponding wrappers are: mpiicc mpiicpc mpiifort Most MPI programs are distributed for Linux with a Makefile or a means to create a Makefile, such as configure or cmake.
  • Docker Images on Rivanna

    Docker requires sudo privilege and therefore it is not supported on Rivanna. To use a Docker image you will need to convert it into Singularity. Convert a Docker image There are several ways to convert a Docker image: Download a remote image from Docker Hub Build from a local image cached in Docker daemon Build from a definition file (advanced) Instructions are provided in each of the following sections. Docker Hub Docker images hosted on Docker Hub can be downloaded and converted in one step via the singularity pull command: module load singularity/3.5.2 singularity pull docker://account/image Use the exact same command as you would for docker pull.
  • How to add packages to a container?

    Strictly speaking, you cannot add packages to an existing container since it is not editable. However, you can try to install missing packages locally. Using python-pip as an example: singularity exec <container.sif> pip install –user <package> Replace <container.sif> with the actual filename of the container and <package> with the package name. The Python package will be installed in your home directory under .local/lib/pythonX.Y where X.Y is the Python version in the container. If the installation results in a binary, it will often be placed in .local/bin. Remember to add this to your PATH: export PATH=~/.local/bin:$PATH You should be able to use the new package/binary in the container, as your entire home directory is mounted at runtime.
  • How To Guides for Rivanna Users

    Building Compiled Code Using Make Building and Running MPI Code Bioinformatics on Rivanna Custom Jupyter Kernels Docker Images on Rivanna Adding packages to a container
  • How To

    General General Tips and Tricks General HowTos › Rivanna High Performance Computing Platform Rivanna HowTos › Ivy Secure Data Computing Platform Ivy HowTos › Storage Research Data Storage & Transfer Storage HowTos ›
  • Rivanna FAQs

    General Usage Allocations Applications Job Management Storage Management Data Transfer Other Questions General Usage How do I gain access to Rivanna? A faculty or research staff member must first request an allocation on Rivanna. Full details can be found here. How do I log on to Rivanna? Use an SSH client from a campus-connected machine and connect to rivanna.hpc.virginia.edu. Instructions for using ssh and other login tools, as well as recommended clients for different operating systems, are here. You can also access Rivanna through our Web-based interface Open OnDemand or FastX. If you are off Grounds and connecting via SSH client or FastX you must use the UVA VPN.
  • Research Data Storage

    There are a variety of options for storing large-scale research data at UVA. Public and moderately sensitive data storage systems can be accessed from the Rivanna high performance computing system. Highly sensitive data can be stored and accessed within the Ivy secure computing environment. Information Security at UVA provides an overview of the data sensitivity classifications. UVA graduate and undergraduate students are not permitted to request storage accounts. This must be done by their faculty advisor[s]. Information Technology Services (ITS) also provides multiple tiers of data storage for personal and non-research storage needs. .tg {border-collapse:collapse;border-spacing:0;border-color:#ccc;} .tg td{font-family:Arial, sans-serif;font-size:14px;padding:10px 5px;border-style:solid;border-width:0px;overflow:hidden;word-break:normal;border-color:#ccc;color:#333;background-color:#fff;} .
  • Graphical SFTP/SCP Transfer Tools

    Several options are available to transfer data files between a local computer and Rivanna through user-friendly, graphical methods. Off Campus? Connecting to Rivanna from off Grounds via Secure Shell Access (SSH) or FastX requires a VPN connection. We recommend using the UVA More Secure Network if available. The UVA Anywhere VPN should only be used if the UVA More Secure Network is not available. Only Windows and Mac OSX operating systems are supported. Linux users should refer to these unsupported instructions to install and configure a VPN. Open OnDemand users do not need a VPN to access Rivanna. MobaXterm MobaXterm is a Windows application that combines an ssh client for logging in, a graphical secure-copy client for easy drag-and-drop file transfer, and an X11 server for displaying graphical applications.
  • Globus Data Transfer

    Globus Data Transfer Globus is a simple, reliable, and fast way to access and move your research data between systems. Globus allows you to transfer data to and from systems such as: Laptops HPC clusters (Rivanna) Lab / departmental storage Tape archives Cloud storage Off-campus resources (XSEDE, National Labs) Globus can help you share research data with colleagues and co-investigators, or to move data back and forth between a lab workstation and Rivanna or your personal computer. Are your data stored at a different institution? At a supercomputing facility? All you need is your campus login. Getting Started A Globus “collection” (also called an “endpoint”) is any computer running the Globus Connect software.
  • Rivanna Maintenance: December 18, 2020

    Rivanna will be taken down for routine maintenance on Wednesday, December 18, beginning at 6 a.m. You may continue to submit jobs until the maintenance period begins, but if the system determines your job will not have time to finish, it will not start until Rivanna is returned to service. Rivanna is expected to return to service by 6 a.m. on Thursday, December 19.
  • JIRA Downtime: December 13, 2020

    The JIRA ticketing system will be taken offline on Friday, December 13 from 6 p.m. to 9 p.m. while our system engineers continue the process of migrating the ticketing system from a local environment to the cloud. Please avoid submitting requests during this period if possible. Although moving to a cloud-based ticketing system will improve the speed and efficiency of our customer service in the long run, in the short-term it may cause disruptions for some users. If you are unable to login to JIRA after the migration is completed, you will need to change your password using your UVA e-mail address.
  • Research Value Storage

    Overview Research Computing offers several budget options for storing public and moderately sensitive research data. Information Security at UVA provides details about data sensitivity classifications. The Research Value Storage provides users with a solution for research data storage and collaboration. Members in the same group have access to a shared directory created by the team lead or PI. Group membership can be defined and managed through ITS MyGroups system. Value storage is mounted on the Rivanna HPC cluster and can also be accessed on a personal computer with an SMB mount, allowing for point-and-click file manipulation. If you are not a researcher, UVA ITS offers Academic Value storage for long-term storage of large scale data.
  • Public and Moderately Sensitive Data Storage

    /home /home is a free 50GB space provided to users of the Rivanna HPC system and is visible from the Rivanna login and compute nodes. /home is the default working directory when logging on to Rivanna. Users can also access their home directory at /home/$USER, where $USER is an individual’s UVa computing ID. /scratch /scratch is a Lustre high performance parallel filesystem accessible via the Rivanna login and compute nodes. All Rivanna HPC users are granted 10TB for free and can access this space within Rivanna at /scratch/$USER, where $USER is an individual’s UVa computing ID. The /scratch directory is not intended for long-term data storage.
  • Important Notes from the 17 September Rivanna Maintenance

    Learn about recent changes implemented during the Sept. 17, 2019 maintenance.
  • Computing Environments at UVA

    Rivanna The primary vehicle for high-performance computing since 2014 has been the Rivanna cluster. Rivanna is a heterogenous system consisting of approximately 186 x 20-core nodes with 128GB of RAM each, 25 x 28-core nodes with 256 GB of RAM, and 152 x 40-core nodes with 384GB each. Five “big memory” nodes offer 1TB of RAM with 16 cores each. All nodes are supported by a high-performance FDR Infiniband network using Mellanox hardware and some have EDR Infiniband. A number of nodes supporting specialty hardware are included; there are 8 nodes with NVIDIA K80 GPGPUs, 4 nodes with NVIDIA V100 GPGPUs, 1 node with NVIDIA V100, and 2 nodes with NVIDIA RTX2080TI boards.
  • Computing Systems

    UVA Research Computing can help you find the right system for your computational workloads. From supercomputers to HIPAA secure systems to cloud-based deployments with advanced infrastructure, various systems are available to researchers. Are you submitting a grant proposal and need standard information about UVA research computing environments? Get it here. High Performance Computing - Rivanna A traditional high performance cluster with job scheduler, large file system, modules, and MPI processing. Get Started on Rivanna Secure Computing for Highly Sensitive Data - Ivy A multi-platform, HIPAA-compliant system for secure data that includes dedicated virtual machines (Linux and Windows), JupyterLab Notebooks, and Apache Spark.
  • Allocations

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  • Rivanna Storage

    There are a variety of options for storing large-scale research data at UVa. Public and moderately sensitive data storage systems can be accessed from the Rivanna high performance computing system. .tg {border-collapse:collapse;border-spacing:0;border-color:#ccc;} .tg td{font-family:Arial, sans-serif;font-size:14px;padding:10px 5px;border-style:solid;border-width:0px;overflow:hidden;word-break:normal;border-color:#ccc;color:#333;background-color:#fff;} .tg th{font-family:Arial, sans-serif;font-size:14px;font-weight:normal;padding:10px 5px;border-style:solid;border-width:0px;overflow:hidden;word-break:normal;border-color:#ccc;color:#333;background-color:#f0f0f0;} .tg .tg-hy9w{background-color:#eceeef;border-color:inherit;vertical-align:top} .tg .tg-dc35{background-color:#f9f9f9;border-color:inherit;vertical-align:top} .tg .tg-0qmj{font-weight:bold;background-color:#eceeef;border-color:inherit;vertical-align:top} Storage Directories Name Quota Price Data Protection Accessible from Best Practices /home 50GB Free 2-week snapshots Rivanna /home is best used as a working directory when using Rivanna interactively. SLURM jobs run against /home will be slower than those run against /scratch. The /home directory is a personal storage space that is not shareable with other users.
  • Workshops

    UVA Research Computing provides training opportunities covering a variety of data analysis, basic programming and computational topics. All of the classes listed below are taught by experts and are freely available to UVa faculty, staff and students. Upcoming Workshops Workshops for 2020 have been moved to online learning formats due to campus closures from COVID-19. DATE WORKSHOP INSTRUCTOR 11/01/2020 Tensorflow - Turning the Knobs (Overview and Q&A) Jacalyn Huband 11/12/2020 Automation of Image Processing with Fiji/ImageJ and OMERO Karsten Siller 11/17/2020 Using Bioinformatics Tools on Rivanna Gladys Andino Research Computing is partnering with the Research Library and the Health Sciences Library to deliver workshops covering a variety of research computing topics.
  • Rivanna

    Rivanna is the University of Virginia’s High-Performance Computing (HPC) system. As a centralized resource it has hundreds of pre-installed software packages available for computational research across many disciplines. Currently the Rivanna supercomputer has over 8,000 cores and 8PB of various storage. All UVA faculty, staff, and postdoctoral associates are eligible to use Rivanna, or students when part of faculty research. The sections below contain important information for new and existing Rivanna users. Please read each carefully. New users are invited to attend one of our free orientation sessions (“Introduction to the HPC System”) held throughout the year during office hours or by appointment.
  • FastX Web Portal

    Overview FastX is a commercial solution that enables users to start an X11 desktop environment on a remote system. It is available on the Rivanna frontends. Using it is equivalent to logging in at the console of the frontend. Using FastX for the Web We recommend that most users access FastX through its Web interface. To connect, point a browser to: https://rivanna-desktop.hpc.virginia.edu Off Campus? Connecting to Rivanna from off Grounds via Secure Shell Access (SSH) or FastX requires a VPN connection. We recommend using the UVA More Secure Network if available. The UVA Anywhere VPN should only be used if the UVA More Secure Network is not available.
  • Open OnDemand

    Overview Open OnDemand is a graphical user interface that allows access to Rivanna via a web browser. Within the Open OnDemand environment users have access to a file explorer; interactive applications like JupyterLab, RStudio Server & FastX Web; a command line interface; and a job composer and job monitor. Logging in to Rivanna Rivanna is accessible through the Open OnDemand web client at https://rivanna-portal.hpc.virginia.edu. Your login is your UVA computing ID and your password is your Netbadge password. Some services, such as FastX Web, require the Eservices password. If you do not know your Eservices password you must change it through ITS by changing your Netbadge password (see instructions).
  • Open OnDemand: File Explorer

    Open OnDemand provides an integrated file explorer to browse and manage small files. Rivanna has multiple locations to store your files with different limits and policies. Specifically, each user has a relatively small amount of permanent storage in his/her home directory and a large amount of temporary storage (/scratch) where large data sets can be staged for job processing. Researchers can also lease storage that is accessible on Rivanna. Contact Research Computing or visit the storage website for more information. The file explorer provides these basic functions: Renaming of files Viewing of text and small image files Editing text files Downloading & uploading small files To see the storage locations that you have access to from within Open OnDemand, click on the Files menu.
  • Open OnDemand: Job Composer

    Open OnDemand allows you to submit SLURM jobs to the cluster without using shell commands. The job composer simplifies the process of: Creating a script Submitting a job Downloading results Submitting Jobs We will describe creating a job from a template provided by the system. Open the Job Composer tab from the Open OnDemand Dashboard. Go to the New Job tab and from the dropdown, select From Template. You can choose the default template or you can select from the list. Click on Create New Job. You will need to edit the file that pops up, so click the light blue Open Editor button at the bottom.
  • Pulse Laser Irradiation and Surface Morphology

    Dr. Zhigilei and his team are using Rivanna to perform large-scale atomistic simulations aimed at revealing fundamental processes responsible for the modification of surface morphology and microstructure of metal targets treated by short pulse laser irradiation. The simulations are performed with a highly-optimized parallel computer code capable of reproducing collective dynamics in systems consisting of up to billions of atoms. As a result, the simulations naturally account for the complexity of the material response to the rapid laser energy deposition and provide clear visual representations, or “atomic movies,” of laser-induced dynamic processes. The mechanistic insights revealed in the simulations have an immediate impact on the development of the theoretical understanding of laser-induced processes and assist in optimization of laser processing parameters in current applications based on laser surface modification and nanoparticle generation in laser ablation.
  • Fluid Dynamics and Reef Health

    Professor Reidenbach and his team are using Rivanna to run computational fluid dynamics simulations of wave and tide driven flows over coral reefs in order to determine how storms, nutrient inputs, and sediments impact reef health. This is an image of dye fluxing from the surface of the Hawaiian coral Porites compressa utilizing a technique known as planar laser induced fluorescence (PLIF). Reefs such as this one have been severely impacted by human alteration, both locally through additional inputs of sediments and nutrients, and globally through increased sea surface temperatures caused by climate change. Reidenbach is hopeful that his computational models will allow scientists to better predict the future health of reefs based on human activity and improve global reef restoration efforts.
  • Pricing

    Below is a schedule of prices for Research Computing resources. Rivanna Allocations Type SU Limits Cost SU Lifetime Standard 100,000 per application; renewable (400K SUs max per fiscal year) Free 12 months Deans’ Allocations None Free 12 months by default, negotiable Purchased None $0.015 (<1M SUs); $0.01 (=1M SUs) Forever Instructional 25,000 Free 2 weeks after last teaching session ** Non-UVA personnel are charged at a rate of $0.07/SU About Allocations Storage Name Security Cost Project Standard $60 TB/year Value Standard $45 TB/year ZFS Standard $30 TB/year Ivy Central Storage High $45 TB/year Ivy NAS Storage High $60 TB/year Storage Details Request Storage
  • Economic Market Behavior

    While conducting research for a highly-technical study of market behavior, Dr. Ciliberto realized that he needed to parallelize an integration over a sample distribution. RC staff member Ed Hall successfully parallelized Ciliberto’s Matlab code and taught him how to do production runs on the University’s high-performance clusters. “The second stage estimator was computationally intensive,” Ciliberto recalls. “We needed to compute the distribution of the residuals and unobservables for multiple parameter values and at many different points of the distribution, which requires parallelizing the computation. Ed Hall’s expertise in this area was crucial. In fact, without Ed’s contribution, this project could not have been completed.
  • Tracking Bug Movements

    Ed Hall worked with the Brodie Lab in the Biology department, to set up a workflow to analyze videos of bug tracking experiments on the Rivanna Linux cluster. They wanted to use the community Matlab software (idTracker) for beetle movement tracking. Their two goals were to shorten the software runtime and to automate the process. There was a large backlog of videos to go through. Ed installed the idTracker software on Rivanna and modified the code to parallelize the bug tracking process. He wrote and documented shell scripts to automate their workflow on the cluster. PI: Edmund Brodie, PhD (Department of Biology)
  • Logging In

    Rivanna is accessible through a web portal, secure shell terminals, or a remote desktop environment. For of all of these access points, your login is your UVA computing ID and your password is your Eservices password. If you do not know your Eservices password you must change it through ITS. Off Campus? Connecting to Rivanna from off Grounds via Secure Shell Access (SSH) or FastX requires a VPN connection. We recommend using the UVA More Secure Network if available. The UVA Anywhere VPN should only be used if the UVA More Secure Network is not available. Only Windows and Mac OSX operating systems are supported.
  • MobaXterm

    MobaXterm is the recommended login tool for Windows users. It bundles a tabbed ssh client, a graphical drag-and-drop sftp client, and an X11 window server for Windows, all in one easy-to-use package. Some other tools included are a simple text editor with syntax coloring and several useful Unix utlities such as cd, ls, grep, and others, so that you can run a lightweight Linux environment on your local machine as well as use it to log in to a remote system. Download To download MobaXterm, click the link below. Select the “Home” version, “Installer” edition, Download MobaXterm Run the installer as directed.
  • SLURM Job Manager

    Overview Rivanna is a multi-user, managed environment. It is divided into frontends, which are directly accessible by users, and compute nodes, which must be accessed through the resource manager. We use the Simple Linux Utility for Resource Management (SLURM), an open-source tool that performs cluster management and job scheduling for Linux clusters. Jobs are submitted to the resource manager, which queues them until the system is ready to run them. SLURM selects which jobs to run, when to run them, and how to place them on the compute node, according to a predetermined site policy meant to balance competing user needs and to maximize efficient use of cluster resources.
  • Active Galactic Nuclei

    Some galaxies have an extremely energetic central region known as an Active Galactic Nucleus. These regions are among the brightest objects in the universe, often outshining all of the stars in their home galaxy combined. In at least some cases, the power source at the center of these extraordinary nuclei is actually a black hole; as gases are drawn toward the black hole, they spiral around it, generating gravitational energy that is converted into heat and electromagnetic waves. A simulation created by Prof. John Hawley (CLAS) with collaborators from Johns Hopkins University reveals this process at an unprecedented level of detail.
  • Quantifying Cerebral Cortex Regions

    A powerful new technique for quantifying regions of the cerebral cortex was developed by Nick Tustison and James Stone at the University of Virginia along with collaborators from the University of Pennsylvania. It was evaluated using large data sets comprised of magnetic resonance imaging (MRI) of the human brain processed on a high-performance computing cluster at the University of Virginia. By making this technique available as open-source software, other neuroscientists are now able to investigate various hypotheses concerning the relationship between brain structure and development. Tustison’s and Stone’s software has been widely disseminated and is being actively incorporated into a variety of clinical research studies, including a collaborative effort between the Department of Defense and Department of Veterans Affairs, exploring the long term effects of traumatic brain injury (TBI) among military service members.
  • Compilers on Rivanna

    Rivanna offers multiple compiler bundles for C, C++, and Fortran. Different compilers have different strengths and weaknesses and different error messaging and debugging features, so users should be willing to try another one when appropriate. The modules system manages the compiler environment and ensures that only compatible libraries are available for loading. Many users of compiled languages are working with codes that can employ MPI for multinode parallel runs. MPI users should first understand how their chosen compiler works, then see the MPI instructions at our parallel programming page. Compiled languages can be more difficult to debug, and the assistance of a good debugger can be essential.
  • Machine Learning on Rivanna

    Overview Many machine learning packages can utilize general purpose graphics processing units (GPGPUs). If supported by the respective machine learning framework or application, code execution can be manyfold, often orders of magnitude, faster on GPU nodes compared to nodes without GPU devices. Rivanna has several nodes that are equipped with GPU devices. These nodes are available in the GPU partition. Access to a GPU node and its GPU device(s) requires specific SLURM directives or command line options as described in the Jobs using a GPU Node section. Applications Several machine learning software packages are installed on Rivanna. The most commonly used ones are:
  • Message Passing Interface (MPI) on Rivanna

    Overview MPI stands for Message Passing Interface. The MPI standard is defined by the Message Passing Interface Forum. The standard defines the interface for a set of functions that can be used to pass messages between processes on the same computer or on different computers. MPI can be used to program shared memory or distributed memory computers. There is a large number of implementations of MPI from various computer vendors and academic groups. MPI is supported on the Rivanna cluster. MPI On Rivanna MPI is a standard that describes the behavior of a library. It is intended to be used with compiled languages (C/C++/Fortran).
  • Rivanna HPC Software

    Overview Research Computing at UVA offers a variety of standard software packages for all Rivanna users. We also install requested software based on the needs of the HPC community as a whole. Software used by a single group should be installed by that group’s members, ideally on leased storage controlled by the group. Departments with a set of widely-used software packages may install them to the lsp_apps space. The Research Computing group also provides limited assistance for individual installations. For help installing research software on your PC, please contact Research Software Support at res-consult@virginia.edu. Software Modules and Containers Software on Rivanna is provided via environment modules or as containers.
  • Rivanna Software List

    function searchFunction() { var input, filter, table, tr, td, i, txtValue; input = document.getElementById(“searchInput”); filter = input.value.toUpperCase(); table = document.getElementById(“moduleTable”); tr = table.getElementsByTagName(“tr”); for (i = 0; i -1) { tr[i].style.display = “"; } else { tr[i].style.display = “none”; } } } } Module Category Description R lang R is a free software environment for statistical computing and graphics. abinit chem ABINIT is a package whose main program allows one to find the total energy, charge density and electronic structure of systems made of electrons and nuclei (molecules and periodic solids) within Density Functional Theory (DFT), using pseudopotentials and a planewave or wavelet basis.
  • Software Modules

    The lmod modules system on Rivanna enables users to easily set their environments for selected software and to choose versions if appropriate. The lmod system is hierarchical; not every module is available in every environment. We provide a core environment which contains most of the software installed by Research Computing staff, but software that requires a compiler or MPI is not in that environment and a compiler must first be loaded. View All Modules   Basic Commands List all available software module avail Use key to list all modules in a particular category. The current choices are base, bio, cae, chem, compiler, data, debugger, devel, geo, ide, lang, lib, math, mpi, numlib, perf, phys, system, toolchain, tools, vis, licensed Example:
  • TensorFlow on Rivanna

    Overview TensorFlow is an open source software library for high performance numerical computation. It has become a very popular tool for machine learning and in particular for the creation of deep neural networks. The latest TensorFlow versions are now provided as prebuilt Singularity containers on Rivanna. The basic concept of running Singularity containers on Rivanna is described here. TensorFlow code is provided in two flavors, either with or without support of general purpose graphics processing units (GPUs). All TensorFlow container images provided on Rivanna require access to a GPU node. Access to GPU nodes is detailed in the sections below.
  • Tools for Research

    Tools and software projects that UVA Research Computing has collaborated on: LOLAweb LOLAweb is a web server and interactive results viewer for enrichment of overlap between a user-provided query region set (a bed file) and a database of region sets. It provides an interactive result explorer to visualize the highest ranked enrichments from the database. LOLAweb is a web interface to the LOLA R package. Launch LOLAweb BARTweb There are a number of commercially licensed tools available to UVa researchers for free. These products, including UVa Box, Dropbox (Health System) and CrashPlan, are most suitable for small-scale storage needs.
  • Sensitive Data Storage - Ivy

    Overview The Ivy secure computing environment meets both HIPAA- and CUI-compliance standards and is ideal for storing highly sensitive research data. Ivy offers several storage options to fit your research computing needs. Ivy Central Storage Ivy Central Storage (ICS) is a highly sensitive data parking zone and central storage pool with a capacity greater than 1PB. This storage space is available for researchers with highly sensitive data and can be mounted on an Ivy virtual machine (VM). For added security, files stored on ICS are read & write only. Executable files can be moved from ICS to VM storage. Researchers can request space on ICS by first requesting an Ivy account using the Ivy request form.
  • What is Research Computing?

    UVA Research Computing (RC) is a new program that aims to support computational biomedical research by providing advanced cyberinfrastructure and expertise in data analysis at scale. Our mission is to foster a culture of computational thinking and promote interdisciplinary collaboration in various data-driven research domains. We offer services related to high performance computing, cloud architecture, scientific programming and big data solutions. We also aim to promote computationally intensive research at UVA through collaborative efforts such as UVA’s own CADRE (Computation And Data Resource Exchange) and XSEDE (Extreme Science and Engineering Discovery Environment). One of our driving philosophies is that researchers already have medical and scientific expertise, and should not have to become computing experts on top of that.
  • Cyberduck

    Cyberduck is a transfer tool for Windows and Mac. It supports a large number of transfer targets and protocols. Only SFTP can be used with Rivanna. The free version will pop up donation requests. Download Download Cyberduck Connecting to Rivanna and File Transfer Launch Cyberduck. After launching Cyberduck, the user interface will open. To initiate a connection to Rivanna, click the Open Connection button. Enter Your Credentials. From the drop-down menu, select SFTP (SSH File Transfer Protocol. Then enter the appropriate information in the following fields: Host: rivanna.hpc.virginia.edu Username: your computing ID Password: your Rivanna password Port: 22 When completed, click Connect.
  • Filezilla

    Filezilla is a cross-platform data transfer tool. The free version supports FTP, FTPS, and SFTP. Only SFTP can be used with Rivanna. Download Download Filezilla Connecting to Rivanna and File Transfer Launch FileZilla. After launching FileZilla, the user interface will open. In the left panel, you should see your local file system and files listed in the left side panels. You will enter your login credentials in the fields highlighted in the figure below. Enter Your Credentials. Fill in the Host, Username, Password, and Port fields. Host: rivanna.hpc.virginia.edu Username: your computing ID Password: your Eservices password Port: 22 When completed, click Quickconnect.
  • SSH on Rivanna

    SSH is the secure shell. It is the primary application used to access Rivanna from the command line. Connecting to a Remote Host For Windows, MobaXterm is our recommended ssh client; this package also provides an SFTP client and an X11 server in one bundle. Mac OSX and Linux users access the cluster from a terminal through OpenSSH, which are preinstalled on these operating systems. Open a terminal (on OSX, the Terminal application) and type ssh -Y mst3k@rivanna.hpc.virginia.edu where mst3k should be replaced by your user ID. You will generally need to use this format unless you set up your user account on your Mac or Linux system with your UVA ID.