Our capstone team work on making BIDMach running on cluster of machines to increase the overall computing power. As a setup, we made great improvement on BIDMach’s communication framework, which is discussed in my paper. The capstone report of my teammate Aleks Kamko is all about our core technical accomplishments - parallel version of machine learning algorithms. Quanlai Li will talk about other miscellaneous achievements in his part, such as the better updating rule - EASGD and computation of network communication bandwidth.
Chapter 1 of this paper (Technical Contribution) covers the motivation, design, implementation, result and discussion of the communication framework, which is the underlying core of parallel models. On the business side, OpenChai’s integrated product aims to solve three main problems in current mainstream machine learning solution - low computation power, waste of energy and data privacy issue. Details will be further discussed in Chapter 2 - Engineering Leadership part of this paper.