Bryce Carey

Title

Low-Order Model of Biological Neural Networks as a Machine Learning Algorithm with Parallel Architecture

Abstract

The Low-Order Model of biological neural networks is a biologically plausible model of dendritic nodes, synapses, neurons, and learning and retrieving mechanisms. The model is adapted into a machine learning algorithm implemented as a C program with parallel architecture. A sample problem involving digit recognition is used as a proof of concept. Performance studies are conducted to demonstrate the benefits of parallel computing applied to the algorithm. These efforts will enable the timely acquisition of results in future studies. This work is in collaboration with advisor Dr. James T. Lo.

Xuan Huang

Title

Parallel Sorting with Application to Communication Analysis Across Switched Network

Abstract

Sorting is one of the fundamental problems of computer science. We introduce some popular sorting algorithms, especially parallel sorting algorithm that runs on distributed-memory cluster. We then use the parallel sorting program as a test case to find contention of communications across a switched network using InfiniBand. The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF).