Parallel Scalable Libraries and Algorithms for Computer Vision
Principal Investigators: L. H. Jamieson, Susanne E. Hambrusch, E. Delp
Research Assistants: Y. Liu, J. Xu
This project develops libraries, algorithms, and tools to enable the
effective use of scalable high performance computing technologies.
The research focuses on the application area of computer vision and
image processing (CVIP).
The research is predicated on the philosophy that high performance
computing must meet seemingly contradictory goals: software
development should be carried out in an architecture- and
technology-independent environment, but both algorithms and system
software should take full advantage of the hardware features of
potentially diverse architectures. These contradictory demands are
reconciled by pursuing research in three areas:
- The development and validation of a computational model that
serves as a platform for architecture-independent algorithm development,
gives an accurate prediction of algorithm performance on a particular
architecture, and supports a realistic scalability metric.
- The implementation of fundamental algorithms and complete CVIP
task scenarios on diverse architectures, including the MasPar MP-1 and
MP-2, Intel Touchstone Delta, iPSC/860, Paragon, and the IBM SP-1 and
- The development of applications-driven library-based software
tools that bridge the software gap between usability and high
performance. Emphasis is on using libraries and a computational model
to capture scalability information, and on providing applications users
with high performance codes in a flexible environment.