Yong Chen
Texas Tech University, TX·Computer Science
Large scientific computing uses a lot of electricity, and much of it adds to carbon emissions. The work here asks how to build and run computing systems that use energy and automation more wisely.
- Fields
Computer Science
Engineering
- Topics
high-performance computing
renewable energy
green computing
- Grants
- 2 known grants, through May 2029
What they work on
- Build and operate a renewable-energy-powered high-performance computing facility, and study the design tradeoffs and barriers of running large-scale scientific computing on variable renewable power.
- Develop tools for remote data center control, automation and workflow management that predict and schedule scientific workloads based on changing energy production.
- Study how computing users respond to different types of power availability, including tradeoffs between cost and quality of service.
- Design code and application programming interfaces for data acquisition and workflow control in cloud and autonomic (self-managing) computing systems, along with standards for cloud software and infrastructure.
- Advance high-performance data analytics, visualization and data center control methods within cloud and autonomic computing settings, together with industry partners.
More about the research
One project powers a high-performance computing facility with renewable energy. The other develops tools for cloud and largely self-managing computing systems as part of a university-industry research center.
Why it matters
Data centers and scientific computing facilities could cut electricity costs and climate impact by running on renewable energy, if workloads can be scheduled around variable power. Tools from this work could help research computing centers and the wider data center industry adopt that approach. The cloud and automation research could give industry partners better software, standards and data center control methods.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Shivaram Venkataraman
University of Wisconsin-Madison, WI·Computer Science·Early-career
Characterize performance variability across many types of accelerator hardware, where identical applications run at different speeds on chips of the same type, and identify which applications are most affected (Fortuna).
Oyekunle Olukotun
Stanford University, CA·Computer Science
Design a stateful network switch architecture that can store, compute on and share application data inside the network, beyond basic packet routing.
Heike Jagode
University of Tennessee-Knoxville, TN·Computer Science
Add performance and power monitoring for AI hardware to the MINCER infrastructure so researchers can make informed choices about optimizing and configuring AI systems.
The school
Texas Tech University: 9 labs on PI Pages, $257M a year on research, 415 research doctorates a year.
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