4 posts tagged with "Wukong"

At the 2026 CIROH Developers Conference (University of Utah, May 27–29) participants used a variety of tools and models on cloud infrastructure they'd never had to install, configure, or even think about. To enable that seamless experience, our Research Cyberinfrastructure team provisioned cloud resources for 15 hands-on workshops across three days in close collaboration with our partners at 2i2c. Here's what we ran, and how.

What we provisioned, by the numbers​

Running an event of this scale requires more than just a single cloud context, as each workshop posed its own computing and data access requirements. CIROH Cyberinfrastructure met this challenge via a coordinated stack spanning multiple cloud providers and services. Across the 15 workshops that needed infrastructure (14 requests), the R2OHC cloud provisioned the following:

One link, no setup: the Workshop Hub​

For 12 of these workshops, their backbone was the CIROH–2i2c JupyterHub, a managed JupyterHub environment that 2i2c operates on Google Cloud. For the conference, we provisioned a dedicated Workshop Hub: an ephemeral environment that lives only for the duration of the event and then spins back down.

The recent DevCon 2025 event showcased not just cutting-edge development practices, but also demonstrated how modern DevOps principles and cloud infrastructure can seamlessly support large-scale technical workshops. Our team had the privilege of providing IT infrastructure and support for over 200 attendees, creating a robust learning environment through an exemplary public-private partnership.

CIROH's Research Cyberinfrastructure and DevOps team.
Left to right, top to bottom:
Manjila Singh, Arpita Patel, Nia Minor, Trupesh Patel, James Halgren; Benjamin Lee.

Predicting water flow with precision across the vast U.S. landscape is a complex challenge. That's why Song et al. 2024 developed δHBV2.0, a cutting-edge hydrologic model. It’s built with high-resolution modeling of physics to deliver seamless, highly accurate streamflow simulations, even down to individual sub-basins. It's already proven to be a major improvement, performing better than older tools at about 4,000 measurement sites. We also provide a comprehensive 40-year water dataset for ~180,000 river reaches to support this.

Penn State research group pushed δHBV2.0 further, training it with even more detailed river data and integrating other trusted models, aiming to make it a key part of the NextGen national water modeling system (as a potential NWM3.0 successor). But here’s a common hurdle: making powerful scientific tools like this easy and reliable for everyone to use within a larger framework can be tough. Setup issues, runtime errors, and inconsistent results can frustrate users.

NGIAB stepped in to solve exactly this problem. Team has taken the complexity out of using the operations-ready models within NextGen by creating one unified, reliable package. Thanks to NGIAB, users don't have to worry about tricky setups or whether the model will run correctly. NGIAB ensures that our models are compatible everywhere and, most importantly, that they run exactly as designed, consistently and faithfully, every single time, no babysitting required. This means users get the full power of our advanced modeling, without the headaches.

Pennsylvania State University (PSU) researchers have been leveraging CIROH Cyberinfrastructure to tackle complex hydrological modeling challenges. This post highlights their innovative approach using the Wukong computing platform in conjunction with Amazon S3 bucket storage to efficiently process and analyze large-scale environmental datasets. 🚀