The Remote Emerging Disease Intelligence – NETwork (REDI-NET) project is a novel, critical, and large scale project and research of a global significance initiated at the University of Notre Dame. The REDI-NET project is aimed at effectively detecting, predicting, and containing potentially emergent zoonotic diseases by improving the accuracy and timeliness of the ‘data-to decision’ pipeline. According to the World Health Organization (WHO), there are one billion cases and millions of deaths arising from zoonosis annually, and emerging zoonosis remain a public and world-wide threat, hence, the project is consequential.

The Center for Research Computing (CRC) anchors and plays a pivotal role in the REDI-NET project. A team of Directors, Data Managers and software development experts at the CRC designed and manages a robust cyberinfrastructure for connecting project partners and laboratories; and for high volume (big data) collection, management, storage, visualization and integration. Some of the key deliverables of the CRC REDI-NET team include the REDI-NET self-contained web-based application for the entire project including tools for multi-variant / high-dimensional data collection; mobile application built for iOS and android; and interactive / descriptive models for data visualization. The CRC team connects and controls vector and eDNA collection across the various partnering laboratories, namely, Gold, Silver and Bronze, and provides all resources and training for laboratory staff and personnel involved in data collection and processing. The project is engaging and promising. Phase III of the REDI-NET project is currently ongoing, and there will be additional deliverables met throughout this phase.

On Tuesday, December 5, the University of Notre Dame joined with partners around the world to launch the AI Alliance. The AI Alliance is a broad, international coalition of organizations that are working across numerous aspects of artificial intelligence (AI) education, research, development, deployment, and governance. Its aim is to enhance the social benefits of AI by supporting open innovation and ensuring that AI systems are safe, secure, and trustworthy. Read more.

Profiling serves as a crucial methodology for evaluating program performance and pinpointing bottlenecks. Both R and Python offer built-in profiling tools to analyze code performance. In Python, the cProfile module is a widely used option, while R users often turn to the profvis package.

To profile your code, activate the profiler while running it. The profiler captures data on time distribution across different code segments. Post-profiling, you can generate a detailed report highlighting areas of concern or bottlenecks. 

Identifying bottlenecks is just the first step; the next involves optimizing the code for improved performance. Consider the following strategies:

After implementing optimizations, it's crucial to re-profile your code. This step ensures that the desired improvements have been achieved and identifies any new bottlenecks introduced during the optimization process.

Tips for Profiling in R:

Tips for Profiling in Python:

By integrating profiling techniques into your coding workflow, you gain valuable insights into the performance of your code. Armed with this information, you can strategically optimize your code, leading to significant improvements in overall efficiency and execution speed.

7:00 a.m., January 5 – 6:00 p.m., January 7, 2024

The first maintenance weekend for 2024 is scheduled to begin at 7am on Friday, January, 5 through the afternoon of Sunday, January, 7. During this time, all CRC systems and services will be shutdown and remain offline for the duration of the maintenance. If you have any questions or concerns, do not hesitate to contact us.

Every Wednesday and Thursday in December
2:00 – 3:00 p.m.
via Zoom

This training is available to new users and current users interested in a refresher course on how to use CRC resources. Attendees learn the basics of accessing CRC resources and submitting jobs on the CRC clusters. This course is a co-requisite when receiving a CRC account. Learn more.

1,417,909 CPU hours
Civil & Environmental Engineering & Earth Sciences

532,841 CPU hours
Chemical & Biomolecular Engineering

391,884 CPU hours
Chemical & Biomolecular Engineering

363,882 CPU hours
Chemistry & Biochemistry

337,614 CPU hours
Chemical & Biomolecular Engineering

297,560 CPU hours
Chemical & Biomolecular Engineering

255,534 CPU hours
Applied & Computational Mathematics & Statistics

246,424 CPU hours
Aerospace & Mechanical Engineering

218,587 CPU hours
Chemistry & Biochemistry

209,170 CPU hours
Chemical & Biomolecular Engineering