DSA Thrust Seminar: Accelerating Machine Learning with GPUs

2:00pm - 3:00pm
ZOOM ID:912 2300 6264, Password:123321

Efficient systems have made notable contributions to the recent success of machine learning. My research work has been mainly dedicated to improving the efficiency of machine learning, particularly with GPUs. In this seminar, I will present our work on accelerating Support Vector Machines (SVMs) and Gradient Boosting Decision Trees (GBDTs). The series of research work has led to open source projects: (i) ThunderSVM which is ~100 times faster and (ii) ThunderGBM which is ~10 times faster and more scalable to high dimensional problems than their counterparts. Faster systems may bring breakthroughs. Our recent results on a popular sentiment analysis problem show that our SVM based solution can achieve competitive predictive accuracy to the Deep Neural Network (and can even outperform the majority of the BERT) based approaches. Furthermore, our solution is about 40 times faster in inference and has 100 times fewer parameters than the models using BERT.

Event Format
Speakers / Performers:
Dr. Zeyi WEN
The University of Western Australia

Dr. Zeyi WEN has been a Lecturer in Computer Science at The University of Western Australia (UWA) since 2019. Before working at UWA, he was a research fellow at National University of Singapore (NUS) and The University of Melbourne after his PhD completion at The University of Melbourne in 2015. Dr. Wen is a winner of the 2019 IEEE Transactions on Parallel and Distributed Systems (TPDS) Best Paper Award. His areas of research include machine learning systems, high-performance computing and data mining.

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