[Defense] Highly Scalable and Accelerated Kernel Machine Training On Diverse Computing Platforms
Wednesday, April 27, 2022
10:00 am - 11:00 am
will defend her proposal
Highly Scalable and Accelerated Kernel Machine Training On Diverse Computing Platforms
The past decade witnessed a massive growth in the volume of data. With the growing popularity of Machine Learning (ML) algorithms, we expect to see around a 40x increase in the size of digital data generated each day by the end of 2025. Synergistic advancements in the processor architecture trends are inching towards exascale computing. This hardware-software complexity makes designing efficient algorithms to train large-scale datasets challenging across various deployment systems. My thesis focuses on bridging this gap between core ML algorithms and diverse computing platforms. I propose highly scalable and performance-centric Kernel Machines, Support Vector Machine (SVM) to perform classification on multi-core CPUs, GPUs, and hybrid multi CPU-GPU systems. To maximize the throughput across diverse platforms, we optimized state-of-the-art algorithms with efficient data distribution techniques, reduced data dimensionality using rank-revealing approximation methods, lower numerical precision, and advanced optimization techniques like Interior Point Method (IPM) for faster convergence rates. Empirical results have demonstrated nearly 1.8x/6.0x/3.5x performance gain for three very large-scale data sets with a minimal trade-off on the optimal accuracy.
10:00AM - 11:00AM CT
Online via MS Teams
Dr. Panruo Wu, dissertation advisor
Faculty, students and the general public are invited.