RE: GPU Workstation for enhancing experience learning of artificial intelligence in MSDA
We would like to request a Linux GPU workstation to enrich student experiential learning of artificial intelligence (AI) in the Master of Science in Data Analytics program (MSDA). GPU-based deep learning methods are the state-of-the-art AI method in data science. Computational training of deep-learning models with real-world big data is time-consuming with CPU or low-end GPUs. Lack of GPU computing power has prohibited many UTC students from applying deep learning methods to big data that are typically in the business world. The proposed Linux workstation will improve GPU access to students in several courses in the MSDA program, including CPSC 5440 Introduction to Machine Learning, CPSC5180 Programming Languages for Advanced Data Analytics, CPSC 5530 Data visualization and Exploration, and CPSC 5240 Principle of Data Analytics.
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Deep learning-based projects are popular choices for many undergraduate and graduates students. Almost all students in CPSC4180/5180 chose deep learning related course projects.
Some of my students are having trouble to get their deep learning model implanted and trained given the limited GPU computing resource and support we have. If the CSE department have our own Linux GUP workstations, our students could be more efficient and productive. Given that we expect more and more data science MS students, increasing GPU computing support seem to be strategically important for both education and research support,
The needs of machine learning and artificial intelligence are reflected by the recent Blue Sky initiative at our department and in our joint new program with College of Business of Data Analytics. GPU-based deep learning is an important skill and knowledge that our students should be trained with for their future employability. In order to provide experiential learning to the students in our department and the college, we need to provide the state-of-the-art deep learning training to our students in the field of artificial intelligence. Given the current cloud and virtual machine technology, GPU is still hard-linked with any virtual machine. So, in order to provide more GPU learning experiences to our students, we literally need to purchase more GPU hardware. It actually does not matter whether these GPU are hosted in a cloud or in workstations, because GPU cannot be virtualized to the best of our knowledge. Given that typical training in real-world data require long-computing time for deep-learning models, dedicated Linux nodes or workstations are the most practical ways to provide experience learning experiences for students to use real-world data for deep-learning projects. Our computers in 312 can be used by students to analyze toy-data, but not sufficient for any real-world data sets. In short, in order to provide real-world experiential learning experiences of AI to our students, we need provide the necessary GPU hardware to students.
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