GPU-Accelerated Research for Higher Education
Science is built on consistent, reliable, repeatable findings. Across disciplines, today’s academic researcher builds specialized models, runs complex simulations, and manages huge datasets. This often requires the processing and storing hundreds of terabytes of data. For innovation to happen at the speed of light, tremendous compute power is needed.
Griffin Lacey, solutions architect at NVIDIA, gives an overview on the NVIDIA portfolio of options specific to higher education research. Learn how to optimize the research institution’s infrastructure to take advantage of scalable tools that enable faster, more powerful computing—empowering researchers to test more hypotheses and uncover more insights.
By watching this webinar replay, you'll learn about:
- Understand the different compute options available for higher education research needs;
- Learn how to take advantage of low precision to accelerate workloads; and
- Discover how to leverage multi-GPUs in your research and data center environments.
ONDEMAND WEBINAR REGISTRATION
Presented By
Griffin Lacey
Deep Learning Solutions Architect, NVIDIA
Griffin Lacey is a deep learning solutions architect for NVIDIA. In his current role, he assists higher education and research institutions to design and deploy their scientific compute infrastructure to achieve the optimal outcomes for their needs. Prior to NVIDIA, Griffin was a deep learning researcher for the University of Guelph, as well as a machine learning researcher at Google. He holds a Bachelors and Masters of Engineering degree from the University of Guelph, where his research focused on the efficiency of deep learning at both the hardware and software level.
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