Gunar Schirner, James Goodman
Date of Award
Master of Science
Department or Academic Unit
College of Engineering, Department of Electrical & Computer Engineering
computer engineering, computer science, remote sensing, GPU, heterogeneous computing, imaging spectroscopy
Electrical and Computer Engineering | Engineering
Graphics Processing Units (GPUs) have proven to be highly effective at accelerating processing speed for a large range of scientific and general purpose applications. As data needs increase, and more complex data analysis methods are used, the processing requirements for solving scientific problems also correspondingly increase. The massive parallel processing power of GPUs can be harnessed and used alongside multi-core CPUs to address these increased needs and allow acceleration of scientific algorithms to open up new realms of possibilities. As an example, there are many scientific problems that require solving non-linear optimization problems of multiple variables across large arrays of data. These types of problems are classified as highly difficult and require a great deal of computational time to solve using traditional techniques. By utilizing modern local optimization techniques, such as the iterative quasi-Newton algorithms, and combining them with the computational throughput of a CPU-GPU heterogeneous computing platform, we can greatly decrease the processing time required to solve scientific problems of this form.
Remote sensing, which is utilized across a wide array of disciplines, including resource management, disaster relief planning, environmental assessment, and climate change impact analysis, represents an ideal problem to be addressed using these techniques. The data volume and processing requirements associated with remote sensing are rapidly expanding as a result of the increasing number of satellite and airborne sensors, greater data accessibility, and expanded utilization of data intensive technologies, such as imaging spectroscopy. In this thesis we demonstrate the advantages of this high performance computing technology by accelerating an imaging spectroscopy algorithm for submerged marine habitats using a CPU-GPU heterogeneous computing platform and a parallel optimization solver written to take advantage of this platform. Results indicate that considerable improvement in performance of approximately an order of magnitude can be achieved using parallel processing on a CPU-GPU computing platform compared to serial processing on the CPU using the same techniques. This technology has enormous potential for continued growth in exploiting high performance computing, and provides the foundation for significantly enhanced remote sensing capabilities.
Sellitto, Matthew, "Accelerating an imaging spectroscopy algorithm for submerged marine environments using heterogeneous computing" (2012). Electrical and Computer Engineering Master's Theses. Paper 74. http://hdl.handle.net/2047/d20002423
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