Computer Engineering Commons^™

Accelerating The Stochastic Simulation Algorithm Using Emerging Architectures, David Dewayne Jenkins

Masters Theses

In order for scientists to learn more about molecular biology, it is imperative that they have the ability to construct and evaluate models. Model statistics consistent with the chemical master equation can be obtained using Gillespie's stochastic simulation algorithm (SSA). Due to the stochastic nature of the Monte Carlo simulations, large numbers of simulations must be run in order to get accurate statistics for the species populations and reactions. However, the algorithm tends to be computationally heavy and leads to long simulation runtimes for large systems. In this research, the performance of Gillespie's stochastic simulation algorithm is analyzed and optimized …

Vision-Based Reinforcement Learning Using A Consolidated Actor-Critic Model, Christopher Allen Niedzwiedz

Masters Theses

Vision-based machine learning agents are tasked with making decisions based on high-dimensional, noisy input, placing a heavy load on available resources. Moreover, observations typically provide only partial information with respect to the environment state, necessitating robust state inference by the agent. Reinforcement learning provides a framework for decision making with the goal of maximizing long-term reward. This thesis introduces a novel approach to vision-based reinforce- ment learning through the use of a consolidated actor-critic model (CACM). The approach takes advantage of artificial neural networks as non-linear function approximators and the reduced com- putational requirements of the CACM scheme to yield …

Automated System To Debug Under-Performing Network Flows In Wide Area Networks, Harika Tandra

Masters Theses

Locating the cause of performance losses in large high performance Wide Area Networks (WAN) is an extremely challenging problem. This is because WANs comprise several distributed sub-networks (Autonomous Networks), with their own independent network monitoring systems. Each individual monitoring system has limited or no access to network devices outside its own network. Moreover, conventional network monitoring systems are designed only to provide information about the health of individual network devices, and do not provide sufficient information to monitor endto- end performance – thus, adding severe overhead on debugging end-toend performance issues.

In this thesis, an automated tool is designed that …

Accelerating Quantum Monte Carlo Simulations With Emerging Architectures, Akila Gothandaraman

Doctoral Dissertations

Scientific computing applications demand ever-increasing performance while traditional microprocessor architectures face limits. Recent technological advances have led to a number of emerging computing platforms that provide one or more of the following over their predecessors: increased energy efficiency, programmability/flexibility, different granularities of parallelism, and higher numerical precision support. This dissertation explores emerging platforms such as reconfigurable computing using fieldprogrammable gate arrays (FPGAs), and graphics processing units (GPUs) for quantum Monte Carlo (QMC), a simulation method widely used in physics and physical chemistry. This dissertation makes the following significant contributions to computational science. First, we develop an open-source userfriendly hardware-accelerated simulation …

Parallel Processing Architecture For Solving Large Scale Linear Systems, Arun Nagari

Masters Theses

Solving linear systems with multiple variables is at the core of many scienti…c problems. Parallel processing techniques for solving such system problems has have received much attention in recent years. A key theme in the literature pertains to the application of Lower triangular matrix and Upper triangular matrix(LU) decomposing, which factorizes an N N square matrix into two triangular matrices. The resulting linear system can be more easily solved in O(N²) work. Inher- ently, the computational complexity of LU decomposition is O(N³). Moreover, it is a challenging process to parallelize. A highly-parallel …

Minimum Transmission Power Configuration In Real-Time Wireless Sensor Networks, Xiaodong Wang

Masters Theses

Multi-channel communications can effectively reduce channel competition and interferences in a wireless sensor network, and thus achieve increased throughput and improved end-to-end delay guarantees with reduced power consumption. However, existing work relies only on a small number of orthogonal channels, resulting in degraded performance when a large number of data flows need to be transmitted on different channels. In this thesis, empirical studies are conducted to investigate the interferences among overlapping channels. The results show that overlapping channels can also be utilized for improved real-time performance if the node transmission power is carefully configured. In order to minimize the overall …