The Information Sciences Institute (ISI) is a unit of the University of Southern California’s highly ranked Viterbi School of Engineering. ISI is one of the nation’s largest, most successful university-affiliated computer research institutes.
The Video, Image, Speech and Text Analytics (VISTA) group at ISI has spent the past three years advancing the state of research for facial recognition, a technology with significant implications for security and commerce.
In order to conduct this research, “We needed a reliable, powerful workload management platform that would enhance performance and have the ability to run complex, diverse workloads across multiple users within the entire ISI organization,” said Stephen Rawls, programmer and research analyst.
VISTA selected Univa Grid Engine and cites key contributing factors over other vendors: built-in advanced GPU support, detailed documentation, ongoing product upgrades and customer support.
One of the ways ISI scientists are teaching computers how to recognize faces is by extracting facial landmarks. In ISI’s system, there are 68 landmarks such as eyebrows, nose and mouth. Using code and algorithms, the project uses deep learning to teach computers to mimic how neurons in the brain talk to each other.
Specific experiments required weeks of compute time to run terabytes of data. For instance, one experiment involved the image processing of over 3 million images. VISTA utilized Univa Grid Engine to set up the parallel processing and manage dependencies for the entire process without fail.
“With Univa Grid Engine, we have an infrastructure that schedules workloads to GPUs. We now operate at 95% capacity with lower overall costs,” said Rawls.
You can read the full case study here.
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