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[Defense] Highly Scalable and Accelerated Kernel Machine Training On Diverse Computing Platforms

Wednesday, April 27, 2022

10:00 am - 11:00 am

In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
Ruchi Shah
will defend her proposal
Highly Scalable and Accelerated Kernel Machine Training On Diverse Computing Platforms


Abstract

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.


Wednesday, April 27, 2022
10:00AM - 11:00AM CT
Online via  MS Teams

Dr. Panruo Wu, dissertation advisor

Faculty, students and the general public are invited.

Doctoral Proposal Defense