UCR Research Computing Software Support
The University of California, Riverside (UCR) fosters an environment of innovation and excellence in research across all disciplines. UCR’s Research Computing Software Support is designed to empower our research community with access to computational tools and resources, aligning with our goals, objectives, and the campus strategy. This document outlines the key categories of software support provided to ensure researchers have the technological capabilities to advance their work.
Examples of Supported Software
See our Knowledge Base for more information on specific software.
High-Performance Computing (HPC)
Enhance the capacity for complex computational tasks, simulations, and analyses through access to powerful computing resources.
- Slurm: For efficient workload management and resource allocation in HPC environments.
- OpenMPI: Facilitates parallel computing, enabling researchers to run large-scale computational simulations.
- Open OnDemand: Provides easy web-based access to HPC resources, allowing researchers to submit jobs, manage files, and access applications through a browser without the need for command-line interactions.
Data Analysis and Visualization
Provide tools for processing, analyzing, and visualizing large datasets to derive insights and disseminate findings effectively.
- R/RStudio: Offers a comprehensive environment for statistical computing and graphics.
- Python (Pandas, NumPy): Essential libraries for data manipulation and analysis.
- MATLAB: Supports numerical computation, visualization, and programming.
Cloud Computing Platforms
Offer scalable and flexible computing resources via the cloud, supporting a range of research activities without the need for direct management.
- AWS (Amazon Web Services): Provides a comprehensive, evolving cloud computing platform.
- Google Cloud Platform (GCP): UCR’s Ursa Major initiative partners with GCP to deliver advanced cloud computing capabilities, integrating state-of-the-art tools and services to support research computing needs effectively.
Machine Learning and Artificial Intelligence
Equip researchers with the tools to develop algorithms capable of learning from data, making predictions, or automating decisions.
- TensorFlow: Open-source framework for machine learning, ideal for building and training neural networks.
- PyTorch: Library for Python, facilitating machine learning projects with dynamic computation graphs.
- Scikit-learn: Machine learning library for Python, suitable for data mining and analysis.
- Vertex AI / Gemini API: Provides a unified UI and API for managing resources across various machine learning services, simplifying model training and inference workflows.
- Large Language Models (LLM): Enable advanced natural language processing tasks, from text generation to sentiment analysis, leveraging pre-trained models for high accuracy and efficiency.
Computational Biology and Chemistry
Support specialized research in genomics, proteomics, molecular modeling, and quantum chemistry with dedicated software.
- BLAST: Tool for biological sequence comparison.
- GROMACS: Molecular dynamics package for particle simulations.
- Autodock: Automated docking tools for predicting molecular interactions.
Geographic Information Systems (GIS)
Facilitate the storage, analysis, and visualization of geographic and spatial data.
- ArcGIS: Comprehensive geographic information system.
- QGIS: Open-source GIS platform for managing and visualizing geographic information.
Software Development Tools
Provide tools that support the development, collaboration, and version control of software projects relevant to research.
- Git: Version control system for tracking changes in source code.
- Docker: Platform for deploying applications in containers, ensuring consistency across environments.
Jupyter Notebooks and Google Colab
Jupyter Notebooks and Google Colab are interactive platforms that allow for the creation and sharing of documents with live code, visualizations, and text. Supporting languages like Python, R, and Julia, they are ideal for data analysis, machine learning, and more. Jupyter Notebooks can be run locally or on servers, while Google Colab offers cloud-based access to powerful computing resources, such as GPUs. Both facilitate collaborative research, enabling easy sharing and editing among peers.
- Jupyter Notebooks: Enables the creation of documents that contain live code, visualizations, and narrative text.
- Google Colab: A cloud-based platform that allows for the creation of Jupyter notebooks with the added benefit of free access to GPUs and TPUs, facilitating advanced computations, machine learning projects, and collaborative research without requiring local resource setup.
Getting Started
For more information or to request support, please contact the UCR Research Computing team: