Object storage has exploded in popularity in recent years due to its ability to deliver virtually unlimited storage scalability. Unlike traditional file- or block-based storage, object storage treats each individual piece of data as a self-contained object. Every object contains three things—the data itself, the metadata, and a universally unique identifier—and is stored in a structurally flat container known as a “bucket.” Any object within the bucket can be quickly retrieved and analyzed (regardless of file type) based on the custom metadata. Thanks to this unique architecture, object storage allows users to store and manage extremely high volumes of unstructured data while retaining speed and data availability.

“Traditional filesystems can face significant performance bottlenecks when scaling to tens or hundreds of millions of files,” says Erik Gough, Senior Research Scientist at RCAC. “The performance impacts can be felt by all users of a shared filesystem when a single user is performing millions of metadata operations simultaneously. Object storage is designed specifically to eliminate this contention on filesystem metadata servers at large scale.”

Object storage is ideal for researchers working with large volumes of static data, such as those leveraging artificial intelligence (AI) and machine learning (ML) methods in their work. Typical use-cases for object storage include creating and utilizing scientific data lakes, instantly accessing ML/AI training datasets, and archiving large volumes of rich media content. With Anvil Object Storage, researchers now have access to a storage pool that can scale to petabyte levels without the metadata performance bottlenecks typically found in traditional file systems, allowing for the storage of billions of items without performance degradation.

“With Anvil Object Storage,” says Gough, “we are giving Purdue and national researchers a powerful new storage tier that is optimized for large-scale datasets that are required for AI and LLM training. We’re excited to see how this new storage tier impacts scientific discovery on Anvil.”

Anvil Object Storage is a 1-petabyte (PB) high-performance, software-defined storage tier integrated directly into the Anvil Composable Subsystem. The new storage service is powered by Ceph, providing scalable, all-flash S3-compatible storage to Anvil users. Researchers can create dedicated storage buckets to securely store, access, and manage their data using familiar S3 client tools such as rclone or s3cmd. The service provides a REST-based API endpoint accessible from Anvil systems and supports flexible access controls for modern data-intensive workflows.

Anvil Object Storage is specifically optimized to host the massive image, text, and sensor datasets required for training Large Language Models (LLMs) and complex computer vision systems. The storage is directly accessible from Anvil’s nodes (including NVIDIA A100 and H100 nodes), facilitating rapid data ingestion for AI training and model hosting.

Access is currently available upon request, subject to available capacity, with a total system capacity of 1PB. To request access, please submit a ticket through the ACCESS or NAIRR ticketing system.

Anvil is one of Purdue University’s most powerful supercomputers, providing researchers from diverse backgrounds with advanced computing capabilities. Built through a $10 million system acquisition grant from the National Science Foundation (NSF), Anvil supports scientific discovery by providing resources through the NSF’s Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS), a program that serves tens of thousands of researchers across the United States. Anvil also supports advanced artificial intelligence research as an official resource provider of the National Artificial Intelligence Research Resource (NAIRR) Pilot.

Researchers may request access to Anvil via the ACCESS allocations process or through the NAIRR allocations process. More information about Anvil is available on Purdue’s Anvil website. Anyone with questions should contact anvil@purdue.edu. Anvil is funded under NSF award No. 2005632.

Written by: Jonathan Poole, poole43@purdue.edu

RCAC’s core mission is to provide access to leading-edge computational and data storage systems as well as expertise in a broad range of HPC activities. Outside of simply being a resource and expertise provider, the center also strives to actively promote the effective use of HPC and to lead the charge in developing the HPC workforce of tomorrow. RCAC has a long history of engaging with students to stoke interest in HPC. The CI-XP Program, for instance, teaches crucial computing skills that will be needed after graduation, and provides real workplace opportunities so that they may grow as professionals in the field. With this in mind, the outreach and engagement team decided to sponsor two amazing, student-focused hackathons this year.

“I spent years coaching athletes at the college, national, and international level, so high performance has always been my thing,” says Suzanna Gardner, Lead Research Operations Administrator for Outreach & Engagement at RCAC. “Figuring out how to help people grow stronger, get more strategic, and build something that actually lasts. That mindset doesn't go away. It just finds new places to show up. For us, one of those places is hackathons. We sponsor them because access matters. Students are among the most talented, driven people out there, and many of them just haven't had a seat at the table yet. When RCAC shows up at a hackathon, we're not there to put our logo on a banner and call it a day. We're there to say: this supercomputer, this technology, this future - it belongs to you too. That's how you build the next generation of researchers. Not by waiting for them to find their way in, but by showing up and opening the door.”

The first hackathon of the semester was Hack Indy 2026. This event is the largest student-run hackathon at Purdue University in Indianapolis, with over 200 students participating. Hack Indy took place over the course of three days. Students faced the challenge of building any software project they wanted, with the caveat that it must be built entirely during the hackathon—no prior development was allowed. Different prizes were offered for specific categories, such as “Best use of Gemini API” or “Best use of ElevnLabs.” RCAC sponsored Hack Indy 2026 by providing computing resources on the NSF-funded Anvil supercomputer. With this sponsorship, the participants had access to the Anvil-AI partition, which was recently upgraded to include 84 Nvidia 80GB H100 SXM GPUs, meaning Hack Indy users could make full use of highly sought after, cutting-edge compute resources typically reserved for advanced research.

Outside of competing, Hack Indy also provided participants with a weekend filled with keynote presentations and workshops given by industry leaders. As part of this, RCAC hosted a one-hour hands-on workshop on supercomputing, teaching students the basics of HPC and highlighting different ways computing can be used for science. RCAC staff members were also on hand through the entirety of the weekend to answer questions or assist with any issues.

The second hackathon of the semester was Catapult 2026. Catapult 2026 was a 36-hour AI + ML × Entrepreneurship hackathon hosted by ML@Purdue, a student organization for machine learning and artificial intelligence. The event was designed for students to come together, build innovative projects, attend workshops, and compete for prizes. RCAC offered 1000 SUs on the Gautschi-AI partition for students to use during the hackathon. Gautschi is Purdue’s most powerful supercomputer, with Gautschi-AI boasting a whopping 160 next-generation NVIDIA H100 GPUs. Thanks to having Gautschi access, the students could tackle any project they wanted during the hackathon without experiencing any computing slow down or lag time.

For Catapult 2026, RCAC also hosted a booth at the event, provided on-site expertise, gave presentations on HPC, and offered a prize for “Best Machine Learning Project.” The winning team developed VIE. VIE is a web tool that combats AI facial recognition systems by making faces in photos unidentifiable to computer systems, but without any perceptible change to human eyes. The group relied heavily on the Gautschi H100 GPUs to develop VIE. For their prize, RCAC will provide the team with 750 SUs on Gautschi-AI, which they can utilize over the course of one year.

“We've been talking with the ML@Purdue student organization for a few years now," says Geoffrey Lentner, Principal AI Scientists at RCAC. "It's been a privilege to work with them facilitating practice and experience in HPC for student projects. The Catapult hackathon is always a fun experience - I enjoy discussing data science and AI concepts and new ideas with the students. I'm impressed with the skills some of the undergraduates already poses and ML@Purdue is a big part of giving some of these students the chance to build these skills. For some students the hackathon is their first exposure to resources like this, and that's great too.”

In continuing with its mission to develop the HPC workforce of the future, RCAC will be hosting multiple summer camps this year. These camps are aimed at middle and high school students looking to gain hands-on experience with computing, STEM, and HPC.

The first camp is “Code Explorers: Coding and AI.” Starting on June 21, this one-week camp will help students discover the power of coding, data science, and environmental action. Participants will get a taste of the full research pipeline: from collecting real-world environmental data to analyzing that data using essential coding skills to presenting their results in a compelling and visually satisfying way. The students will also explore the fundamentals of machine learning, write their own Python code, and learn to refine their code using AI techniques. To learn more, please visit: Code Explorers: Coding and AI (registration closed due to max capacity)

The second camp is another week-long immersive experience, called “CyberSafe Heroes: Empowering High School Students for a Secure Digital Future.” This camp begins on July 12 and is designed to spark interest in cybersecurity and equip students with essential digital skills through hands-on activities. Aligned with national cybersecurity education standards, the program covers encryption, ethical hacking, and online safety while promoting responsible technology use and digital citizenship. The students will engage in encryption challenges, ethical hacking simulations, and cybersecurity escape rooms, explore how artificial intelligence (AI) is used in modern cybersecurity, and even connect with industry professionals through career panels. To learn more, please visit: CyberSafe Heroes: Empowering High School Students for a Secure Digital Future (registration closed due to max capacity)

The final summer camp offering from RCAC is the Mission to Mars Summer Day Camp. This camp is designed for incoming 7th-9th grade students. Participants will step into the role of Mission Control during a simulated Mars mission in crisis. Through three interconnected, story-driven STEM challenges, campers will investigate real problems, design creative solutions, and collaborate with peers - all in a race to save the mission. Three one-day sessions are currently available:

• Session 1 - June 15, 2026
• Session 2 - June 16, 2026
• Session 3 - June 17, 2026

Each session runs from 9:00 AM – 4:00 PM. Snacks and lunch are included. To learn more about the day camp and to register, please visit: Mission to Mars Summer Day Camp

RCAC operates the centrally-maintained research computing resources at Purdue University, providing access to leading-edge computational and data storage systems as well as expertise and support to Purdue faculty, staff, and student researchers. To learn more about HPC and how RCAC can help you, please visit: https://www.rcac.purdue.edu/ or reach out to rcac-help@purdue.edu to request consultation.

Written by: Jonathan Poole, poole43@purdue.edu

Datasets are invaluable for research, but this holds especially true for AI research. AI modeling and machine learning typically rely on immediate access to massive collections of data, whether the end goal is to train a new model or to use an existing one for more specific research. Domain-specific datasets accommodate a researcher’s needs via a single package. However, these packets of data have a downside.

The problem inherent in datasets is that they contain exactly what a researcher wants—a colossal amount of data. The sheer volume of information contained within datasets entails an extraordinary storage footprint, long transfer time, and impact on the machine’s memory. Even researchers utilizing HPC resources can be waylaid by the effort of obtaining the datasets they need and ensuring they are located where their system can use them. The Anvil team at the Rosen Center for Advanced Computing (RCAC) decided to help researchers bypass this issue by amassing datasets into a data repository that is pre-downloaded and ready for use on an HPC system, backed by a fast underlying network. Now, anyone with access to Anvil can use these datasets immediately in their work, saving them both time and hassle on their projects, and accelerating research.

“Making popular datasets natively available on Anvil fundamentally changes how researchers work,” said Haniye Kashgarani. “Datasets with very large numbers of files are hosted in Anvil Object Storage and are also made available in optimized formats such as SquashFS and LMDB on the Anvil file system. This immediate, high-performance access allows scientists to fully leverage HPC and AI workflows without the overhead of data transfers, storage constraints, or redundant downloads. As demand grows, additional widely used datasets can be added to the platform upon request.”

The most recent additions to Anvil’s data repositories are its AI datasets. This collection covers computer vision, PhysicalAI, and robotics, and supports tasks such as detection, segmentation, tracking, control, reinforcement learning, and large-scale model pretraining and evaluation across domains, including everyday objects, smart spaces, and embodied PhysicalAI. There are currently nine datasets in the collection, with more to come. The new AI datasets will enable scientists on Anvil to leverage machine learning techniques and quickly develop AI models that can be embedded into physical systems such as robots or drones, without needing to download and manage the data themselves.

In addition to the new AI datasets, Anvil hosts dataset collections for geospatial, hydrological, meteorological, covariates, igenomes, and GeoAI research. In total, these collections amount to over 215TB worth of data. RCAC’s efforts to centralize and host these valuable datasets on Anvil make the data more easily discoverable, accessible, and usable for scientists throughout the nation.

“One of our goals with Anvil is to push the limits of scientific discovery,” says Arman Pazouki, Director of Scientific Applications at RCAC and co-PI on the Anvil project. “Hosting these datasets on Anvil makes research more efficient, allowing our users to focus on conducting science instead of on data management. As a result, researchers will be able to harness the power of AI and machine learning easier than ever before, expediting the rate at which scientific breakthroughs are possible. This is just one small step in how Anvil is helping to reshape the world of research and expand access on a national scale.”

Researchers who would like to use Anvil’s datasets can learn more here: Anvil Dataset Documentation

To learn more about High-Performance Computing and how it can help you, please visit our “Why HPC?” page.

Anvil is one of Purdue University’s most powerful supercomputers, providing researchers from diverse backgrounds with advanced computing capabilities. Built through a $10 million system acquisition grant from the National Science Foundation (NSF), Anvil supports scientific discovery by providing resources through the NSF’s Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS), a program that serves tens of thousands of researchers across the United States. Anvil also supports advanced artificial intelligence research as an official resource provider of the National Artificial Intelligence Research Resource (NAIRR) Pilot.

Researchers may request access to Anvil via the ACCESS allocations process or through the NAIRR allocations process. More information about Anvil is available on Purdue’s Anvil website. Anyone with questions should contact anvil@purdue.edu. Anvil is funded under NSF award No. 2005632.

Written by: Jonathan Poole, poole43@purdue.edu

We will provide an update by noon, April 28 or sooner as work progresses.

All RCAC systems and Research Network services will remain unavailable during this extended window.

We appreciate your continued patience as we complete these critical upgrades to improve RCAC infrastructure reliability and performance.

For assistance, questions, or concerns, please contact rcac-help@purdue.edu.

Dong Xu was a Curators’ Distinguished Professor in the Department of Electrical Engineering and Computer Science at the University of Missouri. He joined the Health Informatics Institute at the University of South Florida, as a Professor this year. He is the head of the Digital Biology Lab, a research group focused on the intersection of bioinformatics and artificial intelligence (AI). Recently, Xu and his team decided to tackle a major challenge in the field of computational biology—understanding and predicting protein structures.

Proteins are the building blocks of life, serving many crucial roles in biochemistry. Each protein has its own unique three-dimensional (3D) structure that is intricately tied to its function. Scientists estimate that there are more than 2 million different protein structures. However, not all of these are yet known, much less understood. Being able to accurately predict different protein shapes is the key to unlocking major discoveries in pharmaceutical design, disease comprehension, and biotechnology.

Current research suggests that protein structure follows a linear logic. Much like natural language, which conforms to rules of grammar, proteins seem to have their own rules that instruct how their 3D structures are formed. Knowing this, scientists can try to leverage the underlying “grammar” along with AI methods to develop a language of 3D geometry. This language can then be used to reconstruct and even predict 3D protein structures. Utilizing high-performance computing (HPC) resources to simulate and predict protein structures is not new to the field of bioinformatics, but despite recent advances, creating representations of the 3D geometry of proteins that can be used in generative modeling and downstream AI tasks has proven elusive. Xu and his lab decided to use the Anvil supercomputer to address this challenge.

“For this project, we used Anvil to train a deep learning model to represent protein structures,” says Xu. “So if you think about a protein, it consists of amino acids—they form these chains that create a three-dimensional structure. You have probably heard of AlphaFold, which can predict the structure very well. But in order to really study the massive structures, either experimentally generated or computationally predicted, it requires a good representation of protein structure, one that can be used in an effective way to do downstream tasks. So we needed to develop these representations.”

Through their NAIRR allocation, Xu and his team, especially his Ph.D. student Mahdi Pourmirzaei, had access to Anvil’s H100 GPUs, which they used to develop, train, and test GCP-VQVAE (geometry complete-vector-quantized variational autoencoder) on a corpus of 24 million monomer protein backbone structures. The team’s results were overwhelmingly positive.

GCP-VQVAE produced more accurate protein reconstructions than other deep learning models, maintaining proper orientation and chirality—a difficult task to achieve while still retaining accuracy. The model also demonstrated robust performance to unseen proteins, meaning it could produce protein structures that were not included as part of the training data. Lastly, the GCP-VQVAE model proved to be much more efficient than other models, achieving approximately 408x and 530x lower end-to-end latency. The best part? GCP-VQVAE is fully open-sourced with transparent training and evaluations. Any researcher can utilize the model, see how it was trained, and compare it with other VQVAEs.

To accomplish this, Xu’s lab relied on the advanced GPUs provided by Anvil. The group initially had access to A100 GPUs via their original NAIRR allocation. After starting the project, Xu realized they needed to work at a faster pace and reached out to the Anvil support team. The Anvil team convinced Xu to trade his A100 allocation hours for H100 hours, using Anvil’s Service Unit Swap feature. Although the swap comes at a 2:1 ratio (two A100 hours for one H100 hour), Xu noted that the results were well worth the trade. Not only are the H100s more powerful and efficient than the A100s, but the H100 queue on Anvil is significantly shorter, allowing Xu to run his jobs in a more condensed time frame. All of this led to a much faster time-to-science for the group.

“We initially had an allocation of A100s,” says Xu, “but there were a lot of jobs, so people have to wait. The Anvil staff suggested we trade to the H100s, and when we did that, we didn’t have a queue; the jobs ran immediately. So we got more computing power from the trade, and we didn’t have to wait. It worked really, really well.”

Xu continued, “Anvil also provided top-notch customer service. They gave us a lot of help, which I really appreciate. We had meetings with them, they answered emails very quickly, and they gave a lot of guidance for our students and postdocs.

Overall, Xu and his research lab were immensely pleased with the Anvil supercomputer. He noted that thanks to NAIRR and Anvil, his team was able to produce multiple papers and present at major conferences, such as the AI4Science Workshop at NeurIPS 2025. Aside from the NeurIPS presentation, Anvil helped the team to produce a preprint in bioRxiv and a publication in Computational and Structural Biotechnology Journal.

“Resources like Anvil, they are really valuable to us,” says Xu. “We ran a lot of jobs, and have already published papers acknowledging the allocation; it’s extremely important to us. Without these resources, we wouldn’t be able to do much.”

For more information on Xu’s research, please visit: Dong Xu’s Google Scholar Page.

To learn more about High-Performance Computing and how it can help you, please visit our “Why HPC?” page.

Anvil is one of Purdue University’s most powerful supercomputers, providing researchers from diverse backgrounds with advanced computing capabilities. Built through a $10 million system acquisition grant from the National Science Foundation (NSF), Anvil supports scientific discovery by providing resources through the NSF’s Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS), a program that serves tens of thousands of researchers across the United States. Anvil also supports advanced artificial intelligence research as an official resource provider of the National Artificial Intelligence Research Resource (NAIRR) Pilot.

Researchers may request access to Anvil via the ACCESS allocations process or through the NAIRR allocations process. More information about Anvil is available on Purdue’s Anvil website. Anyone with questions should contact anvil@purdue.edu. Anvil is funded under NSF award No. 2005632.

Written by: Jonathan Poole, poole43@purdue.edu

The “AI Across Purdue” seminar series seeks to bring together Purdue faculty and researchers to explore AI opportunities and build connections across campus and NSF-supported programs. Each session features two 20-minute faculty talks on how they are using AI at Purdue, followed by 20 minutes of Q&A and networking. Since the first meeting in early Spring of 2026, the series has connected more than 100 Purdue faculty and researchers, highlighting the importance of dedicated discussions that explore all aspects of AI in research and education.

Unlike traditional training and workshops, this series focuses on advancing knowledge through the sharing of lived experiences and open discussion amongst those at the forefront of AI. Thus far, the talks have covered a wide variety of subjects: materials, mechanical, and nano/quantum engineering, agriculture, biology, and language and culture, as well as some of the AI infrastructure that the researchers used, such as the Gautschi Community Cluster and the Anvil supercomputer. By participating in the “AI Across Purdue” seminar series, attendees not only develop their own AI skillset but also help shape the future of AI at Purdue.

While the “AI Across Purdue” faculty seminar series has been tremendously successful thus far, there’s still time this semester for you to join the conversation. The past and remaining schedule for the series is as follows:

• February 10th: Professor Nikhilesh Chawla (Materials Engineering), Professor Alexandra Boltasseva (Electrical and Computer Engineering)
• March 3rd: Professor Yan Gu (Mechanical Engineering), Professor Jinha Jung (Civil and Construction Engineering)
• March 10th: Professor D. Marshall Porterfield (Agricultural and Biological Engineering), Professor Yaguang Zhang (Agricultural and Biological Engineering)
• March 24th: Professor Vanesa Cañete Jurado (College of Liberal Arts), Professor Maurice Tetne (College of Liberal Arts)
• April 7th: Professor Edwin Garcia (Materials Engineering), Professor Romit Maulik (Mechanical Engineering)
• April 21st: Professor Prianka Baloni (Health Sciences), Professor Mia Liu (Physics and Astronomy)
• April 28th: Professor Mustafa Abdallah (Computer and Information Technology), Professor Rua M. Williams (Applied and Creative Computing)

Each session is attended by RCAC and IPAI organizers in order to answer questions and assist with any AI challenges faced by the attendees. They also acquaint participants with the numerous AI services available through Purdue and the NSF, including the NAIRR Pilot, ACCESS Program, Genesis Mission, Purdue Center for Research Software Engineering, and RCAC AI Support and Expertise.

More information about the “AI Hubs Faculty Seminar Series: AI Across Purdue,” can be found on our Seminar Series Event page.

To stay apprised of upcoming AI-related news and training, please subscribe to our RCAC newsletter and our IPAI newsletter.

RCAC operates the centrally-maintained research computing resources at Purdue University, providing access to leading-edge computational and data storage systems as well as expertise and support to Purdue faculty, staff, and student researchers. To learn more about HPC and how RCAC can help you, please visit https://www.rcac.purdue.edu/ or reach out to https://www.rcac.purdue.edu/rcac-help@purdue.edu to request consultation.

The Institute for Physical Artificial Intelligence (IPAI) is Purdue’s hub for faculty collaboration at the intersection of AI and the physical world. IPAI connects researchers across disciplines to develop secure, robust, and deployable AI systems. Spanning models, hardware, robotics, autonomy, and ethics, IPAI helps apply these capabilities to shared research challenges and emerging opportunities across industry and government domains. To learn more, please visit https://ipai.research.purdue.edu/ or email https://www.rcac.purdue.edu/ipai@purdue.edu.

Anvil is funded under NSF award No. 2005632.

Written by: Jonathan Poole, poole43@purdue.edu

Artificial intelligence is changing the world. Whether it be in research, education, business, or pleasure, there’s hardly a facet of life that AI hasn’t touched. In research, AI and machine learning are making an impact in almost every scientific field, reducing time-to-science and enabling breakthroughs at a rate previously unimagined. Those who harness the power of AI will be positioned to make world-changing discoveries—which is precisely why RCAC set out to provide Purdue with an AI ecosystem designed to enable excellence at scale. To achieve this, RCAC focused its efforts throughout 2025 on three crucial areas: hardware, software, and expertise.

Next-generation GPUs for AI research

AI and machine learning tools and methodologies can revolutionize the research and education landscape, but require the appropriate hardware in order to work; i.e., advanced GPUs. Step one for RCAC was to build and deploy a supercomputer laden with top-tier GPUs and designed to enhance AI workflows in research. Enter the Gautschi Community Cluster.

Gautschi is a state-of-the-art community cluster eponymously named in honor of Walter Gautschi, Professor Emeritus of Computer Science and Professor Emeritus of Mathematics at Purdue University. The supercomputer is a dual-partition system: Gautschi CPU, optimized for traditional, tightly-coupled science and engineering applications; and Gautschi-AI, designed specifically to enhance AI research at Purdue. Thanks to support from Purdue Computes and the Institute for Physical AI (IPAI), Gautschi-AI was built with cutting-edge H100 GPUs, which utilize NVIDIA’s Hopper architecture and a Transformer Engine in order to provide training and speeds that are four times faster than previous generation models. Gautschi-AI has eight total H100 GPUs with 80 GB of RAM, providing Purdue researchers with a whopping 10.7 PetaFLOPS of peak performance—ample processing power for even the largest AI jobs.

RCAC’s deployment of the Gautschi Community Cluster quickly proved successful—the system’s impact on AI research at Purdue has been astounding:

• 45 Principal Investigators (PIs) from 18 departments have purchased allocations (11 new PIs added in Q4, including 2 new departments)
• 18 IPAI affiliate PIs applied for matching allocations
• 700k GPU hours delivered in 2025 (Would cost on average $8.7M to procure at commercial cloud prices)
• IO500 benchmark for worldwide ranking of Gatuschi’s storage capabilities ranked at #20 on 10-node production system
• Completed mixed-precision LINPACK benchmark for another worldwide ranking of Gautschi, ranked #27 in the world

While Gautschi is Purdue’s most powerful supercomputer to date—and the major hardware focus for 2025—it isn’t the only one helping researchers in their AI efforts. Gilbreth is another community cluster available at Purdue. The system received its third expansion in 2024 and now has a total of 411 GPUs, nearly four times its original capacity. Gilbreth is designed as a high-performance computing (HPC) resource specifically optimized for “throughput” applications, supporting general-purpose, medium-scale simulations as well as large numbers of smaller AI jobs. Even though Gilbreth’s resources are primarily comprised of the previous generation NVIDIA GPUs, the system has had a major impact on AI research at Purdue. Between Gautschi and Gilbreth, Purdue AI supercomputers provided 3 million GPU hours in 2025, which would have cost Purdue faculty over $15M to procure on commercial clouds.

To purchase access to the Gautschi Community Cluster today, please visit RCAC’s Cluster Access Purchase page. IPAI is currently offering a matching program for Gautschi-AI, with IPAI matching a one-year allocation of one GPU for each GPU purchased (up to 8 GPUs). To take advantage of the matching program, all researchers need to do is provide a written description of their project and how it relates to physical AI alongside their purchase order.

Software infrastructure for AI workflows

With the AI-specific hardware needs being met by Gautschi and Gilbreth, RCAC’s next step was to ensure the underlying software infrastructure was ready to support AI workloads and researchers. Accomplishing this task required a myriad of software developments, implementations, and solutions, but one key project took center stage for the organization—Purdue GenAI Studio.

Purdue GenAI Studio is an LLM service that makes open-source LLM models accessible to anyone at Purdue. Developed in collaboration with IPAI, the intent behind the tool was to provide an LLM service that researchers could easily access and use, and which negates the concern of leaking intellectual property or proprietary data. Unlike other LLM services, Purdue GenAI Studio is hosted entirely on-premises using resources within Purdue’s supercomputers, providing researchers with more democratized access to LLMs, as well as more control. No documents or contexts are uploaded into commercial cloud-hosted AI services, nor are any chats, documents, or models shared between users or used for training.

The team at RCAC worked hard throughout 2025 to iteratively develop and improve Purdue GenAI Studio. The LLM service currently has 30+ language models spanning multiple model families (Llama, Mistral, Phi, Gemma, and specialized domain models), and has been integrated with advanced features, including retrieval-augmented generation (RAG), web search capabilities via SearXNG, and document parsing with Docling. Unsurprisingly, given the need for such a tool within research contexts, Purdue GenAI Studio has been met with great success:

• 2500+ active users across research, instruction, and administrative units at Purdue
• Over 22,000 messages delivered by chat UI
• More than 2 Million messages, 1.7 Billion tokens delivered by LLM API in January of 2026 alone

Purdue GenAI Studio is helping users across every discipline to take the next giant leap in their research. The LLM service has been so successful that RCAC has been solicited by peer institutions to provide consultation on establishing similar infrastructure at their respective locations.

To learn more about Purdue GenAI Studio or access the tool, please visit: https://www.rcac.purdue.edu/knowledge/genaistudio?all=true

Expertise for Supporting AI Applications

The final step in RCAC’s plan to thrust Purdue into the vanguard of AI innovation is vitally important, yet easy to overlook. The hardware is in the data center. The software is under the hood. RCAC provided all the tools researchers would need for their AI endeavors. Now the center had to ensure the researchers would know how to use them.

Throughout 2025, RCAC—already known for its world-class HPC support and expertise—set out on the ambitious task of supplying Purdue with as much knowledge on AI for research as possible. The plan was multi-pronged, involving collaborative engagement across multiple teams within the organization. From training and outreach events to consultations and collaborations with its Research Software Engineering Center, RCAC made AI a key focus for its support services.

Training was arguably one of the largest pieces of the AI support services puzzle, with the overarching strategy needing to account for both breadth and depth in order to help Purdue researchers across the board. To advance AI research, knowledge dissemination was crucial. Training efforts focused on ensuring researchers, educators, and students could confidently use AI tools while understanding their limitations, ethical implications, and best practices. The cumulative impact of these efforts was impressive. In the fall of 2025 alone, RCAC delivered 30 training sessions, partly arranged as four thematic series, with more than 1000 people participating in the sessions. RCAC also led multiple signature AI training events throughout the year, including AI Day and summer camps, which saw an additional 150+ participants in extended thematic workshops. A full list of the 2025 trainings, as well as upcoming training sessions in 2026, can be found here: https://www.rcac.purdue.edu/training

Aside from training events, RCAC enabled AI research and education through its Research Software Engineering (RSE) Center. The RSE Center provides end-to-end research software support, from early proposal design and data engineering to development, deployment, visualization, and long-term sustainability. In 2025, the RSE Center provided AI consultation and project support to over 15 faculty and research groups across colleges, including Engineering, Science, Liberal Arts, Health & Human Sciences, Pharmacy, and the Mitch Daniels School of Business. Furthermore, the center contributed to multiple funded and pending grant proposals advancing AI applications in education, research infrastructure, and scientific discovery. The RSE Center is a dedicated AI and research computing resource, ready to help Purdue researchers take their next giant leap. To leverage the RSE Center’s team of AI research scientists and collaborate on a project, please visit: Purdue Center for Research Software Engineering

RCAC support services for AI extend beyond training events and RSE partnerships. Other AI support efforts conducted in 2025 include:

• Hosted hundreds of terabytes of datasets locally to accelerate research on Purdue infrastructure.
• Provided various self-guided AI training materials, detailed notebooks, example code, datasets, and deployment guides, all publicly available through RCAC repositories for asynchronous learning.
• Created extensive documentation for Purdue GenAI Studio covering AI model selection, API access, responsible use guidelines, fine-tuning workflows, and deployment best practices.
• Integrated AI into Purdue curriculum for multiple courses through custom applications. Support for the classroom included generating AI-assisted testing, AI-enhanced simulation, AI-assisted evaluation, creation of practice materials, and AI-enhanced VR networking training systems.

National AI Resource Provider

RCAC’s main AI focus in 2025 was to provide Purdue with a computing ecosystem that would allow the university to become a leader in AI innovation; however, the center also stepped up to fulfill a role in doing the same for the nation.

Anvil, Purdue’s powerful National Science Foundation (NSF)-funded supercomputer, was expanded to support the National Artificial Intelligence Research Resource (NAIRR) Pilot and ACCESS programs. The supercomputer received $5M in supplemental funding to secure advanced GPUs and provide AI support to researchers throughout the United States. Anvil-specific AI upgrades and support efforts in 2025 include:

• Procured and deployed Anvil AI, a new partition housing 84 Nvidia H100 SXM GPUs
• Created a 1PB all-flash object storage for Anvil to support emerging AI storage needs.
• Deployed AI-powered Anvil Notebook service that provides an interactive notebook portal to support instructional and/or lightweight research needs
• Continually developed AnvilGPT, the “Purdue GenAI Studio” available for Anvil users nationwide.

Anvil and Anvil AI are available to researchers via two pathways: 1) the ACCESS allocations process, and 2) the NAIRR allocations process. More information about Anvil is available on Purdue’s Anvil website. Anyone with questions should contact anvil@purdue.edu.

Looking Ahead

While 2025 was an extraordinarily productive year for AI and generative AI initiatives at RCAC, there is no intent to slow down. The Spring 2026 semester will feature the continuation of monthly AI training workshops running January through May. Also, multiple major grant proposals are pending that would significantly expand AI research infrastructure, educational applications, and cross-institutional collaborations.

Planned initiatives for 2026 include expanding GenAI Studio's model catalog, deploying advanced agentic AI capabilities, launching the SciAgents research program for AI-assisted scientific discovery, scaling course-integrated AI applications to thousands of additional students, and continuing to build Purdue's national leadership in responsible, institution-hosted generative AI infrastructure. Beyond these initiatives, RCAC will also continue to educate students and researchers by delivering numerous AI-focused trainings and workshops, including the NSF NAIRR Regional AI Workshop, happening May 20-22 in Indianapolis, Indiana, as well as the Purdue AI Research Showcase, hosted in conjunction with Purdue’s Institute for Physical AI and taking place April 14–15, 2026 on the Purdue University West Lafayette campus.

To stay apprised of upcoming AI-related news and training, please subscribe to our RCAC newsletter. For more information on our AI and data science services, please visit our AI and Data Science page.

RCAC operates the centrally-maintained research computing resources at Purdue University, providing access to leading-edge computational and data storage systems as well as expertise and support to Purdue faculty, staff, and student researchers. To learn more about HPC and how RCAC can help you, please visit: https://www.rcac.purdue.edu/ or reach out to rcac-help@purdue.edu to request consultation.

Anvil is funded under NSF award No. 2005632.

Written by: Jonathan Poole, poole43@purdue.edu

A full catalog of available modules with usage instructions is at the Biocontainers Documentation site. For step-by-step guides on running common genomics workflows on HPC systems, visit the RCAC Bioinformatics Tutorials site, which includes documentation for tools like HiFiasm, BRAKER3, Trinity, and Nextflow, along with best practices for job submission and resource optimization. Full workshop materials are also available for RNA-seq analysis, genome assembly, and genome annotation. For upcoming training sessions and community programs, including the biweekly Genomics Exchange discussion series and the Midwest Bioinformatics Showcase seminar series, see the Computational Biology Services page. For questions or support with bioinformatics workflows on Anvil, contact rcac-help@purdue.edu.

Due to the outage, some jobs on Gilbreth did not requeue automatically. Users should check the status of any jobs that were running early this morning and resubmit them if needed.

Pegasus is a tool to help workflow-based applications function in various environments, including desktops, cloud, and high-performance computing (HPC) systems. It was designed to allow scientists to construct workflows in abstract terms and remove the need to understand the underlying execution environment. Pegasus has been used successfully in a number of scientific fields: astronomy, bioinformatics, earthquake science, gravitational-wave physics, ecology, and cryo-EM, amongst others. A workflow in Pegasus consists of multiple tasks with defined dependencies, and Pegasus handles job submission, data staging, execution ordering, and failure recovery. Some beneficial features of Pegasus include:

• Data Management: Pegasus handles data transfers, input data selection, and output registration.
• Automated Error Recovery and Reliability: Errors are automatically addressed by retrying tasks, workflow-level checkpointing, re-mapping, and trying alternative data sources for data staging.
• Adaptability and Reuse: Pegasus works in a variety of distributed computing environments, and workflows can easily be run in different environments without alteration.
• Scalability: Pegasus can scale both the size of the workflow and the resources the workflow is distributed over without impacting performance.

Pegasus is deployed on Anvil through the Anvil Notebook Service, which provides browser-based access to Jupyter Notebooks running on Anvil infrastructure. The Pegasus Notebook environment includes the Pegasus workflow management system, HTCondor for workflow execution management, and preconfigured integration with Anvil’s SLURM scheduler. This environment allows users to develop and debug workflows interactively using the Pegasus Python API or command-line tools, submit workflows to Anvil’s batch system using their allocations, and monitor workflow execution and logs directly from the notebook interface. No additional Pegasus installation or configuration is required by the user.

To learn more about Pegasus and how to access it on Anvil, please visit: Pegasus on Anvil

Anvil is one of Purdue University’s most powerful supercomputers, providing researchers from diverse backgrounds with advanced computing capabilities. Built through a $10 million system acquisition grant from the National Science Foundation (NSF), Anvil supports scientific discovery by providing resources through the NSF’s Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS), a program that serves tens of thousands of researchers across the United States. Anvil also supports advanced artificial intelligence research as an official resource provider of the National Artificial Intelligence Research Resource (NAIRR) Pilot.

Researchers may request access to Anvil via the ACCESS allocations process or through the NAIRR allocations process. More information about Anvil is available on Purdue’s Anvil website. Anyone with questions should contact anvil@purdue.edu. Anvil is funded under NSF award No. 2005632.

Written by: Jonathan Poole, poole43@purdue.edu

The Beyond the Algorithm Challenge was designed by the NASA Earth Science Technology Office (ESTO) to propel scientific discovery for complex Earth Science problems—in this case, rapid flood analysis—by encouraging the exploration of unconventional and innovative computing methods. Specifically, the ESTO wanted participants to utilize technologies such as quantum computing, quantum machine learning, neuromorphic computing, or in-memory computing, which have all shown promise in overcoming limitations of conventional computing methods. By testing these novel computing methods, the Beyond the Algorithm Challenge paves the way for transforming how Earth Science problems are solved, potentially improving the lives and safety of the American people.

The SECQUOIA group is a Purdue University research organization within the Davidson School of Chemical Engineering. Led by Dr. David Bernal, Assistant Professor of Chemical Engineering, the SECQUOIA group focuses on designing and implementing optimization algorithms using hybrid and cutting-edge hardware technologies, including quantum computing technologies. Upon learning about the Beyond the Algorithm Challenge, Bernal felt that the competition aligned well with SECQUOIA’s work and immediately began assembling a team. Team members for the challenge included: Dr. Bernal, Yirang Park, PhD student in Chemical Engineering; Alan Yi, sophomore in Computer Science; and Daniel Anoruo, senior in Computer Science with a cybersecurity focus from Towson University.

Over the course of 10 weeks, the group designed and refined QUAFFLE (Quantum U-Net Assisted Federated Flood Learning and Estimation). QUAFFLE is a hybrid modeling framework that combines Quantum U-Nets for image segmentation with federated learning, a machine learning approach that decentralizes the training process. To understand the reasoning behind QUAFFLE requires a rudimentary understanding of these architectures and techniques.

U-Net architecture is a tried-and-true convolutional neural network (CNN) used for pixel-level image segmentation. The name stems from the fact that when drawn, the architecture takes the shape of a “U.” U-Nets take images and identify specific objects within those images. The resulting accuracy of the U-Net model correlates with how well it was trained.

Federated learning is a technique in which a global model is collaboratively trained across multiple devices or servers, each of which has its own local model. One of the benefits of federated learning is that each local model can handle a specific type of data—ideal for tasks that involve analyzing dissimilar data. The performance of the global model is improved in this scenario by producing higher-quality training results on smaller, distributed datasets rather than relying on less robust results from one large, centralized dataset.

For the Beyond the Algorithm Challenge, the SECQUOIA group wanted to create a system that was capable of producing accurate flood maps. The group theorized that harnessing the power of quantum computing combined with federated learning would allow for this while improving speed, security, and efficiency, compared to traditional computing methods.

A major obstacle for the group was mismatched datasets. The flood maps would need to be based on all available imagery, which includes images of differing regions, sizes, and sources (LiDAR, drone, satellite, weather radar, etc).

“One of the main challenges we had with this specific application was that there's a lot of heterogeneity in the data,” says Yirang Park. “To overcome this, we implemented federated learning under a heterogeneous-client setting, where each client trained locally on a random subset of the data and contributed model updates to a shared QUAFFLE model, improving speed and accuracy.”

Another issue the group faced in this challenge is the computational intensity required for flood detection. The very large, heterogeneous datasets needed for the task means that there is a significant amount of training parameters. More training parameters equals more computing power and longer computing times. To combat this, the group decided to replace the bottleneck layers in the U-Net architecture (the layers forming the bottom of the “U”) with quantum layers. The idea was that this would help reduce the number of training parameters required, thus reducing the training time and increasing learning efficiency.

“We theorized that if we needed fewer training parameters, we could speed up the training process,” says Daniel Anoruo. “Replacing the bottleneck with quantum-based architecture allowed us to do that while simultaneously improving feature extraction.”

The final challenge for the group was one of access and scarcity. For now, quantum computers are rare and few researchers are allocated computing time on the machines. The SECQUOIA group used the Anvil supercomputer to solve this problem by simulating two types of quantum computers: a gate-based system (with PennyLane software) and a photonic-based system (with ORCA-SDK software). The benefits of using a powerful supercomputer like Anvil to simulate a quantum computing system were manyfold: the researchers tested and refined QUAFFLE on a computing system they had access to, validated their approach for potential future use on different types of quantum systems, and bypassed the long process of obtaining an allocation on a quantum computer just to test an unproven (at the time) software framework.

“Running these simulations on Anvil gave us an advantage in the sense that we know QUAFFLE is hardware agnostic,” says Park. “There are multiple types of quantum computers, and no one knows which one will be the system, but we do know that QUAFFLE can adapt to different hardware architectures.”

Park continues, “Having a working code that has been proven in simulations and can adapt to various quantum systems has also allowed us to de-risk the approach. We know that we haven’t built something only to find that we’ve wasted time and resources after implementing it on precious quantum resources.”

The SECQUOIA group was thrilled with Anvil’s performance.

“Anvil really saved us,” says Alan Yi. “We tried testing these simulations on our local computers, and they would run for two days and not be done. But with Anvil GPUs, the simulation would finish really quickly, sometimes even less than an hour.”

After completing their work, the group had demonstrated that QUAFFLE was a success—it required 6% fewer parameters and outperformed a centralized quantum U-Net in accuracy when combining different data sources. Their innovative approach led to them securing a position as a finalist in the Beyond the Algorithm Challenge. While they did not ultimately receive the grand prize in the competition, the team’s work stood out for its innovation and real-world potential. QUAFFLE earned recognition from the judges as a promising solution, and the project gained valuable support from industry leaders, including Rigetti, Orca UK Computing, Flower, and IBM. The team plans to continue expanding QUAFFLE, and hopes to someday test it on an actual quantum system.

For more information about the SECQUOIA group, please visit: https://secquoia.github.io. The group’s presentation given to NASA for the Beyond the Algorithm Challenge can be viewed here: https://www.nasa-beyond-challenge.org/project-gallery/secquoia

To learn more about High-Performance Computing and how it can help you, please visit our “Why HPC?” page.

Anvil is one of Purdue University’s most powerful supercomputers, providing researchers from diverse backgrounds with advanced computing capabilities. Built through a $10 million system acquisition grant from the National Science Foundation (NSF), Anvil supports scientific discovery by providing resources through the NSF’s Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS), a program that serves tens of thousands of researchers across the United States.

Researchers may request access to Anvil via the ACCESS allocations process. More information about Anvil is available on Purdue’s Anvil website. Anyone with questions should contact anvil@purdue.edu. Anvil is funded under NSF award No. 2005632.

Written by: Jonathan Poole, poole43@purdue.edu

During this maintenance window, several clusters will experience a temporary outage so that hardware can be safely powered down while facility work is performed:

• Gautschi, Gilbreth, Negishi, Bell, and Anvil cluster nodes will be powered down.

• The Gilbreth’s legacy V100 GPUs, that are well past their lifetime, will be decommissioned.

• Hammer (Math nodes) and Geddes: A subset of nodes will be powered down but the services will be available, unless communicated separately.

How does this maintenance impact you?

• Clusters listed in this message won’t be available to run jobs during the maintenance.

• Any jobs requesting a walltime which would take them past the start of the maintenance will not start and will remain in the queue until after the maintenance is completed.

• Users can continue to access their data.

• GenAI studio will remain available. This maintenance will position Purdue to support growing computational needs. Users should see long‑term benefits in system reliability and our ability to support future computing and AI resources.

If you have questions about how this outage will affect your work or need support, please contact rcac-help@purdue.edu.

Impact To You: At this time, you may notice latency or brief disruptions when accessing certain on-campus or external resources, especially during peak usage periods.

We appreciate your patience while we work to fully resolve the underlying problem and restore normal network performance. We will provide an update by 5:00PM EST today or sooner.

System monitoring has identified an issue where heavy job activity was overloading the Data Depot filesystem used by the clusters and Globus.

You may see the following impacts:

• Globus transfers to and from Depot collections may fail, stall, or run much more slowly than usual.
• Globus logins may be slow or occasionally fail when accessing Depot endpoints.
• Jobs on RCAC clusters that read from or write to Depot may experience slow file access, delayed directory listings, or timeouts.

Our engineers are investigating the high load from a large number of concurrent jobs and are working to reduce the impact on Depot, Globus, and cluster workloads. Existing jobs will continue to run, but any that are heavily Depot‑I/O‑bound may run more slowly or see I/O errors until performance improves. We will provide another update by 5:00PM EST or sooner if the issue is resolved.

Dark matter is elusive. We don’t know what it is or what it is composed of. This mysterious material scoffs at the adage “Seeing is believing”—it does not interact with the electromagnetic force, meaning it neither absorbs, reflects, nor emits light of any kind. We literally cannot see it, yet we know it is there. Dark matter has mass, thereby exerting the effects of gravity on visible matter. It is only by observing these gravitational effects that scientists know dark matter exists. In fact, dark matter accounts for roughly 85% of all matter in the universe, serving as a cosmic scaffolding that organizes galaxies at scale. Without it, galaxies would have long ago been torn asunder by their own rotational velocities, lacking the necessary gravitational pull required to hold together.

As one can imagine, studying a material that can’t be seen but whose effects must be observed through a telescope can be tricky. For decades, scientists have tackled this problem by running cosmological simulations that include dark matter and comparing them to what is actually seen in the universe. If the end result of a simulation matches the physical reality seen through the telescope, then that’s a good sign that the scientists are on the right track with their theories. If not, the theory must be altered or dismissed entirely. Recent technological advances have enabled scientists to study dark matter in greater depth than ever before. High-performance computing (HPC) systems provide an astonishing amount of computing power, while the new James Webb Space Telescope (JWST) gives astronomers an unprecedented view of the universe, enabling observations of the first stars and formation of the first galaxies after the Big Bang. This boost in data-gathering ability and computing performance lies at the heart of the dark matter research being conducted at UCLA.

Claire Williams is a PhD student in the Astronomy and Astrophysics Division of the UCLA Department of Physics and Astronomy. Williams’s focus is on theoretical astrophysics. She is part of the Supersonic Project, a collaboration that studies how stream velocity and dark matter affected galaxy formation in the early universe. In this instance, stream velocity refers to the relative velocity of baryons and dark matter during the early formation stages of the universe. The stream velocity has been largely neglected in traditional simulations of galaxy formation. However, recent findings show that the stream velocity was supersonic, which had major implications for how the baryons and dark matter were distributed. Williams’s, and the rest of the research group’s, goal is to improve our understanding of the galaxy formation process by including the stream velocity as a factor in their cosmological simulations.

“So our specific studies are trying to gain a more precise understanding of the process by including effects that previously nobody included,” says Williams. “People already had dark matter, they already had gas, but they were missing the stream velocity. It has been largely ignored because it is challenging to get right in simulations. But neglecting the fact that material was moving past the dark matter at five times the speed of sound inevitably leads to a different result. What our group has done is to run simulations that correctly include the relative motion of dark matter and ordinary matter at early times in the universe.”

Williams and her research group utilize the Anvil supercomputer to run high-resolution AREPO hydrodynamics simulations for a number of different studies. The common theme across these studies is that the group runs theoretical simulations both with and without stream velocity as a factor, and that the results are, or will soon be, compared with JWST observations. The size of the regions being simulated are, quite literally, astronomical, ranging upwards of two megaparsecs. This equates to a volume slightly larger than the Milky Way and Andromeda galaxies combined. The simulations are also incredibly detailed, with each individual particle representing an area roughly 200 times the mass of our sun. For comparison, that’s a single grain of sand on the beach. Simulations this large require a massive amount of computing power and would be impossible without HPC resources like Anvil.

“So we're simulating a region larger than the whole Milky Way, but our individual pieces that are moving around are only a couple 100 times bigger than our own sun,” says Williams. “This is why we need Anvil, because you couldn't run this on your laptop. This takes a couple of weeks to run on the cluster.”

Running the simulations is only the first part of the process; HPC resources are further needed to actually analyze the data. Williams continues:

“Then, at the end of the day, when you finish your simulation run, you basically have a bunch of imaginary particles in an imaginary box. But you have to figure out, ‘How would these particles translate to light that the telescope would see?’ So you need to post-process the simulations, which involves extensive data analysis and specialized algorithms to convert the resulting particles into light in space. We need Anvil for this data analysis as well.”

The end result of the group’s computational work is a theoretical picture of what the universe should look like to us today, as viewed through the JWST. Dark matter was dispersed throughout the universe soon after the Big Bang, unaffected by the forces of electricity and magnetism. The gravitational pull of dark matter led to clumps of particles, which eventually formed into galaxies. And the precise placement of these galaxies was likely influenced directly by the stream velocity. At least, that’s Williams’s hypothesis. Now, the research group must wait to see if it proves true.

“So one of the things that we have found with our studies,” says Williams, “is that the stream velocity should cause some very faint galaxies to shine very brightly for a brief period of time at the beginning of the early universe, because it causes them to form a bunch of stars all at once. Without the stream velocity factored in, you wouldn’t expect to see this happen. And now they're starting to make observations with the JWST that should show what we predict to see. So, hopefully, in the next few years, we can get confirmation from the telescope that this effect is happening.”

Williams continues, “One thing that's kind of cool is that if they don't see that effect, then it poses a big problem for dark matter in general, because all of our models so far are dependent on how we think dark matter should work. So if we make this prediction and the telescope doesn't see it, then we know we've messed up our collective understanding of dark matter along the way and may need to make changes to things we thought we had a grasp on in our cosmology.”

For more information about William’s research, please visit her UCLA Bio Page. More details on the Supersonic Project can be found here: The Supersonic Project

Interested in leveraging the latest advancements in computing to bolster your research? Please visit our “Why HPC?” page to learn more about High-Performance Computing and how it can help you.

Anvil is one of Purdue University’s most powerful supercomputers, providing researchers from diverse backgrounds with advanced computing capabilities. Built through a $10 million system acquisition grant from the National Science Foundation (NSF), Anvil supports scientific discovery by providing resources through the NSF’s Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS), a program that serves tens of thousands of researchers across the United States.

Researchers may request access to Anvil via the ACCESS allocations process. More information about Anvil is available on Purdue’s Anvil website. Anyone with questions should contact anvil@purdue.edu. Anvil is funded under NSF award No. 2005632.

Written by: Jonathan Poole, poole43@purdue.edu

Year: Sophomore

Major: Electrical Engineering

Position: Assistant Computational Researcher

Can you introduce yourself and share a little about who you are? Hello! My name is Elian and I’m an Assistant Researcher!

What are some of your main interests or passions? Some of my interests include Linux, servers, and drinking lots of coffee.

Can you tell us about your role at RCAC? What does your job entail? I am an Assistant Researcher handling tickets from researchers and the entire user base of multiple RCAC clusters, including Anvil and Gautschi. I also help the Apps team to cover issues facing the clusters.

What do you enjoy most about working at RCAC? Working at RCAC allowed me to handle servers on a day-to-day basis and, other than the fact that I'm a huge server nerd, it allowed me to learn more about Linux systems in a very friendly work environment.

Tell us more about your favorite project you like to show off!

Project title: Handling Apps tickets at RCAC

Project description: I handle tickets from the wide range of users that Purdue's multiple clusters cover and support. Whenever issues arise, I would be one of the first people to handle the ticket then I'd handle their issues, noting whatever arises in RCAC's database.

What did you learn from this project? A lot of patience, especially from (slightly, and understandably) upset researchers who thought they had lost their life's work (thankfully they hadn’t!).