Thesis (M.E.E.)--University of Delaware, 2007. / Principal faculty advisor: Guang R. Gao, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (doctoral)--University of Oulu, 2007. / Includes bibliographical references. Also available on the World Wide Web.

Thesis (Ph.D.)--University of Wollongong, 2006. / Error in title on title page. Typescript. Includes bibliographical references: leaf 233-240.

Thesis(M.S.)--Auburn University, 2007. / Abstract. Includes bibliographic references (p.64-66).

Thesis (Ph. D.)--University of Alabama at Birmingham, 2009. / Additional advisors: Barrett Bryant, Aniruddha Gokhale, Marjan Mernik, Chengcui Zhang. Description based on contents viewed June 3, 2009; title from PDF t.p. Includes bibliographical references (p. 161-177).

Thesis (M.S. in computer engineering)--Washington State University, December 2005. / Includes bibliographical references (p. 60-61).

Thesis (M.Eng.)--Memorial University of Newfoundland, 2003. / Bibliography: leaves 105-109.

Thesis (MS)--Montana State University--Bozeman, 2010. / Typescript. Chairperson, Graduate Committee: Brock LaMeres. Includes bibliographical references (leaves 59-63).

Developing applications for distributed adaptive computing systems (ACS) requires developers to have knowledge of both parallel computing and configurable computing. Furthermore, portability and scalability are required for developers to use innovative ACS research directly in deployed systems. This thesis presents an Application Programming Interface (API) implementation developed in a scalable parallel ACS system. The API gives the developer the ability to easily control both single board and multi-board systems in a network cluster environment. The API implementation is highly portable and scalable, allowing ACS researchers to easily move from a research system to a deployed system. The thesis details the design and implementation of the API, as well as analyzes its performance. / Master of Science

Distributed adaptive computing systems (ACS) allow developers to design applications using multiple programmable devices. The ACS API, an API created for distributed adaptive com-puting, gives developers the ability to design scalable ACS systems in a cluster networking environment for large applications. One such application, found in the field of bioinformatics, is the DNA sequence alignment problem. This thesis presents a runtime reconfigurable FPGA implementation of the Smith-Waterman similarity comparison algorithm. Additionally, this thesis presents tools designed for the ACS API that assist developers creating applications in a heterogeneous distributed adaptive computing environment. / Master of Science