The Lab Director of the Intelligent Data and Systems Lab (IDS Lab), Feng Yan’s research on big data has led to collaborations with industry partners like Amazon, Microsoft Research, and Google. A recent National Science Foundation CAREER award will allow Yan to tackle one of the most important aspects of w88 mobile learning: Its environmental impact. As computing power increases, its draw on energy also increases—to the point that the impact on the environment of simple programs rivals that of an international plane flight or, in some cases, the lifetime emissions of an automobile.
Recently, Yan discussed this aspect of his CAREER w88 mobile, along with his desire to pursue research that has the ability to improve society.
What is the goal of the CAREER project?
w88 mobile-Learning-as-a-Service (MLaaS) is an emerging computing paradigm that provides optimized execution of w88 mobile learning tasks, such as model design, model training, and model serving, on cloud infrastructure. The goal of this CAREER project is to gain a fundamental understanding of MLaaS and utilize the unique features of MLaaS to design efficient, automated, and user-centric MLaaS systems. This approach will significantly reduce resource waste and shorten the model design cycles through a variety of novel optimization approaches and by eliminating candidate models that fail to meet model serving latency and target accuracy. To support complete MLaaS workflow, this project will also develop MLaaS model serving methodologies that can meet service level latency requirements with minimum resource consumption using intelligent autoscaling. Important insights and technologies will be produced targeting resource management and energy saving of the next-generation w88 mobile learning systems and cloud infrastructure. The findings of this project will also contribute to related fields of parallel and distributed systems, performance evaluation and optimization, and green computing.
What inspired you to pursue this w88 mobile?
Explosive growth in model complexity and data size along with the surging demands of w88 mobile is already resulting in substantial increases in computational resource and energy requirements. Unfortunately, existing w88 mobile systems have poor resource management and limited support for user specified performance and cost requirements, exacerbating waste in computing resources and energy.
For example, training a typical ML w88 mobile on the cloud may take tens to hundreds of hours that costs hundreds to thousands of dollars. According to a recent study from Emma Strubell et al., to design and train a large-scale w88 mobile using an automated approach, e.g., Automated ML (AutoML), can take 274,120 hours with a steep cost of millions of dollars. What’s more concerning is the carbon footprint. The study from Emma Strubell et al. pointed out that the design and training of a large-scale NLP w88 mobile consumes 656,347kWh·PUE power and emits 626,155 CO2e (lbs), which is 5 times more than the carbon footprint of a car’s lifetime (including fuel consumption). Even the emission of training a common BERT w88 mobile on GPU is roughly equivalent to the emission of a trans-American flight.
Even though dozens of new w88 mobile features and services are rolling out each year, the resource management of w88 mobile has been often overlooked. The state-of-the-practice resource management of w88 mobile does not consider the unique features of ML workloads and thus suffers from poor efficiency. In addition, existing management tools require manual specification and tuning of lots of unintuitive system parameters, which makes these tools difficult to be used correctly and effectively.
The above motivated me to develop new resource management methodologies for supporting automated and efficient MLaaS as well as integrate the learned lessons into the w88 mobile so that students and a larger audience understand the importance and urgency of efficient MLaaS.
What does this w88 mobile mean to you and for your work?
I am very honored to receive this prestigious award.... It allows me to expand my lab and recruit more talented students to work on the proposed topic of automated and efficient w88 mobile-Learning-as-a-Service.
I am very honored to receive this prestigious award. This award provides necessary support for me to carry out the proposed research and education agenda. It allows me to expand my lab and recruit more talented students to work on the proposed topic of automated and efficient w88 mobile-Learning-as-a-Service. These substantial integrated research and education activities will help me grow from an early-career researcher to a more experienced researcher. I am encouraged by this award and will keep on trying my best to conduct high impact and productive research in the areas of big data, w88 mobile learning, computer systems, as well as cross-disciplinary topics.
What impact on society do you hope to achieve through your work?
This project has the potential to tremendously reduce the resource and energy consumptions as well as the carbon footprint associated with the fast-growing societal demands in w88 mobile learning and cloud computing. This project will also provide unique opportunities for both undergraduate and graduate students by training them in the art of system optimization combined with the latest w88 mobile learning domain knowledge, to prepare them as future generation computer scientists and engineers.
Anything else you’d like to add?
I would like to thank all my students for their excellent w88 mobile work and contribution to this project. I want to thank our Department of Computer Science and Engineering Chair Dr. Eelke Folmer for his consistent support and help, my mentor Dr. Sushil Louis for his great mentoring, as well as other colleagues in our department for their support, help, and collaboration. I want to thank Craig Holloman and the Engineering w88 mobile Office for their great support and help in preparing and submitting the proposal. I want to thank Dr. Mridul Gautam, Jennifer A Bonk, Kate Dunkelberger, and others in w88 mobile & Innovation for their great assistance. I want to thank our Dean Manos Maragakis and Associate Dean Indira Chatterjee for their support from the college level, President Brian Sandoval, Vice President & Provost Jeffrey Thompson, and Vice Provost David Zeh for their support from the University level. I want to thank my family, friends, and collaborators.
