For the sixth consecutive time, a system powered by GPUs captured the top-spot overall in the Supercomputing conference’s Student Cluster competition.

With longhorn hand-signs and smiles flashing, an 11-person team from the University of Texas, at Austin, claimed the award before a crowd of more than 500 at the end of three-day show in Denver.

It’s no accident that a GPU-based system won the competition. Seven of the eight teams from around the world that finished the grueling 48-hour, nonstop competition utilized Tesla K20x accelerators donated by NVIDIA.

That’s a powerful testament to the grip that parallel processing has on the next generation of computer scientists.

Texas
Shown on the floor of the Denver Supercomputing 2013 show, The University of Texas, at Austin, team beat out seven others to win the Student Cluster competition.

Julian Michael, a junior on the team, said he and his undergrad classmates – all computer science majors with a computational biologist thrown in — had been preparing since March for the competition, putting in 10 hours a week and sleeping just four hours a night toward the end.

“We’re going to go back, get some sleep and get ready for next year’s competition,” he said.

There’s a definite minimalist slant to the competition. Contest rules require that the teams build a supercomputing cluster working with donated equipment and an austere power budget of 3,000 watts, enough to power a pair of microwave ovens.

Scoring takes into account how each system performs on a range of standard HPC applications – such as WRF, used for weather prediction, and GraphLab, a tool for big-data analysis – as well as on extensive  interviews.