Advanced Research Computing — Technology Services is launching a new program to assist faculty working with undergraduate students on research that requires high performance computing resources.
Faculty can now access Flux HPC resources in support of undergraduate research at no cost.
more information
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Fill out a form to request resources through Flux for Undergraduates. An abstract of the intended activity must be submitted.
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Email questions to arc-contact@umich.edu.
Flux is the shared computing cluster available across campus, operated by ARC-TS. Under the new Flux for Undergraduates program, faculty can sponsor undergraduate student groups and individuals in order to access unused computing cycles on Flux for free.
Jacob Abernethy, assistant professor of electrical engineering and computer science, took advantage of the program to help the Michigan Data Science Team access Flux to compete in Big Data competitions. The group was created in fall 2015 with funding from an National Science Foundation CAREER grant that Abernethy was awarded in 2015.
The team enters competitions through sites like https://www.kaggle.com/competitionsKaggle, and is one of the first such teams affiliated with a university.
Abernethy said that after the group’s first competition he surveyed the students as to what worked and what didn’t. He said one of the clearest responses was the need for more robust computing resources.
“Our top two competitors talked about maxing out the resources on not only their own laptop, but also on the clusters provided them by their advisers,” Abernethy said. “It became clear that we needed to talk about Flux.”
Jonathan Stroud, a computer science and engineering graduate student working with Abernethy, runs the team, and said members were maxing out the capabilities of their laptops when they first started.
“For the first couple of competitions, we made sure we picked a problem that people could do on their laptops,” Stroud said. “Still, every night before bed, they would set up their experiments and they ran all night.”
He said success in the data science competitions typically depends on trying several approaches simultaneously, which can be taxing on computing resources.
Stroud said the team typically uses software such as Python, R, and Matlab. Team members come from a wide range of disciplines, including engineering, applied math, physics, and one from the School of Music, Theatre & Dance, Stroud said.
Abernethy said a key method to the machine-learning and data science-experimentation process is the use of cross-validation — that is, testing the performance of a set of parameters on several subsets of data simultaneously.
“This leads to a very obvious need for a distributed system in which we can execute a large number of ‘embarrassingly parallel’ tasks quickly,” Abernethy said.
Being able to use Flux “has been helping us a lot,” Stroud added. “We’ve been contacted by other schools to see how they can do the same thing.”
Jobs submitted under Flux For Undergraduates will run only when unused cycles are available, and will be requeued when those resources are needed by standard Flux jobs. To be most efficient, student groups should use short or checkpointed jobs to take advantage of these available cycles.
Student groups can also purchase Flux allocations for jobs that are higher priority or time constrained. Those allocations can also work in conjunction with the free Flux for Undergraduates jobs.
“The goal is to provide undergraduates with experience in high performance computing, and access to computational resources for their projects,” said Brock Palen, associate director of ARC-TS.
Undergraduate groups and individuals must have sponsorship from a faculty member.
