Training a model, fine-tuning one, or running a large analysis takes GPUs. As more courses teach machine learning, more students need them, and the campus platform has not always had enough for all of them. Some students have paid for cloud GPU time themselves to finish coursework and projects.
PARASOL is the campus response. It is the platform students already know, formerly called DSMLP, with more GPUs, a reservation system that matches capacity to the academic calendar, and a name that reflects who it serves.
What the Name Stands For¶
PARASOL is short for Platform for Applied Research, Analytics, Science and Open Learning. The platform supports classroom GPU demand first, and runs research work on the same terms.
What Was Added in Fall 2026¶
The Halıcıoğlu School of Data Science and Computing added new GPU servers to the platform.
- TBAGPU serversAdded in Fall 2026
- TBAGPUs in totalNVIDIA RTX PRO 6000 Blackwell
- TBAPeak GPU-hours a yearApproximate, for coursework and student projects
- TBAOff-peak GPU-hours a yearFor approved research, capstones, and graduate work
The added servers expand the platform for every course, project, and research group that uses it.
GPU Hardware & CUDA lists every GPU model on the cluster, including the new ones, and the GPU class each one serves.
Why Reservations¶
GPU demand is uneven. It peaks around assignment deadlines and in tenth week and finals, and it falls overnight and on weekends. A reservation holds a GPU for a specific window, so a student with a deadline does not have to wait for a free GPU. Off-peak hours cost fewer Service Units, which moves long research runs to the hours when classes use the fewest GPUs.
Reservations explains how to book a window. Service Units & Budgets explains how GPU time is metered.
What Comes Next¶
The new servers also give the campus better data on GPU demand. University leaders will use that data to plan further investment, including software and services that make GPU computing easier to use in courses outside science and engineering.
Industry Tools and Workflows¶
PARASOL runs containers on Kubernetes, with the same tools and workflows used in industry and research computing. Students in undergraduate, master's, and doctoral programs learn on the kind of infrastructure they will use after graduation.
See Get Started or How PARASOL Works.