CRC expands compute resources with powerful new servers |
Researchers at the University of Notre Dame now have access to a significantly enhanced compute environment with the addition of a new array of CPU and GPU servers to the CRC infrastructure. These powerful systems have been seamlessly integrated into existing queues for immediate use, providing researchers with the resources they need to tackle complex computational challenges.
Enhanced CPU Resources for Diverse Needs
The newly added 67 general compute servers are equipped with dual AMD Epyc 7543 32-core processors and 256GB of memory. These servers offer substantial performance improvements over previous generations, making them ideal for a wide range of computational tasks. For memory-intensive applications, four additional servers with 2TB of RAM are available by request.
Unleashing GPU Power for Data-Intensive Computing
The CRC's GPU compute capabilities have also received a major boost with the introduction of 12 new GPU servers. These systems feature four Nvidia A10 series cards, 24GB of onboard memory, and two Intel Xeon Gold 6326 16-core processors. This powerful combination is specifically designed for GPU-accelerated computing, making it ideal for data-intensive tasks such as machine learning and scientific simulations.
Seamless Transition for Users
Researchers can access the new CPU and GPU servers without any additional steps or configuration changes. The CRC has also created the gpu-long queue, which provides continued access to older GPU servers for extended computational tasks.
Fostering Innovation and Discovery
Providing researchers with the latest and most powerful compute resources is a key driver of innovation and discovery. The new servers are expected to play a significant role in advancing research across a wide range of fields.
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Software team hosts reCon 2023 mini-conference |
The Center for Research Computing (CRC) Software Team hosted the second annual reCon mini-conference on October 25-27, 2023.
During the in-person event, the team was able to reconnect and learn from the experience of others through presentations by members of the CRC, Office of Information Technologies, and faculty collaborators. The event also provided the opportunity for attendees to gain hands-on experience in current Large Language Model (LLM) platforms and practice and to spend time reflecting on the University’s Strategic Framework and its implications for the software team.
A Generative AI/LLM workshop was a major highlight of the conference. During the first half of this experience, team members were grouped around current Generative AI platforms, including GitHub Copilot and OpenAI Whisper, ChatGPT, and Dall-E and were instructed to decide how they wanted to use each AI tool. Then they attempted to complete the project.
In the second part of the workshop, each team member walked through a series of tutorials demonstrating how to use Amazon Web Service (AWS) resources to configure, deploy, train, and interact with bespoke LLM-based chatbots. The conference was a big success!
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Debugging Tips for R and Python |
Debugging is the process of finding and fixing errors in source code. It is an essential skill for all programmers, regardless of their experience level. Let's dive into practical debugging techniques for both R and Python, providing you with straightforward tools and strategies to improve your debugging process.
Common debugging techniques:
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Use a debugger. A debugger is a software tool that allows you to step through your code line by line and examine the values of variables. This can be very helpful for finding bugs that are difficult to track down using other methods.
- Log output. Logging output to the console or a file can be very helpful for debugging. This can help you to track the flow of your code and identify where errors are occurring.
- Use print statements. Print statements can be a very helpful way to log output and track the flow of your code.
- Break down the problem. If you are having trouble finding a bug, try breaking the problem down into smaller pieces. This can make it easier to identify the source of the bug.
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Use unit testing. Unit testing is a process of writing automated tests for individual units of code. This can help you to catch bugs early on, before they become more difficult to fix.
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Additional Practical Tips for R and Python Debugging:
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- Use the traceback() function in R and the traceback.print_exc() function in Python to print a stack trace of the current error.
- Check the R console or the Python interpreter for error messages.
- Use the ? operator in R and the help() function in Python to get help on R or Python functions.
- Read the documentation for R and Python libraries.
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| | Every Wednesday and Thursday in November
2:00 – 3:00 p.m.
via Zoom
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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.
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Top 10 Computation Users (October 2023)
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845,809 CPU hours
Civil & Environmental Engineering & Earth Sciences
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625,868 CPU hours
Biological Sciences
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422,016 CPU hours
Chemical & Biomolecular Engineering
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399,413 CPU hours
Chemical & Biomolecular Engineering
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371,705 CPU hours
Chemical & Biomolecular Engineering
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| 318,888 CPU hours
Civil & Environmental Engineering & Earth Sciences
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311,688 CPU hours
Civil & Environmental Engineering & Earth Sciences
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294,638 CPU hours
Chemical & Biomolecular Engineering
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276,493 CPU hours
Chemical & Biomolecular Engineering
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256,797 CPU hours
Chemical & Biomolecular Engineering
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