{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:theses-1148"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:theses-1148","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"GPU implementation of block transforms","abstract":"Traditionally, intensive floating-point computational ability of Graphics Processing Units (GPUs) has been mainly limited for rendering and visualization application by architecture and programming model. However, with increasing programmability and architecture progress, GPUs inherent massively parallel computational ability have become an essential part of today's mainstream general purpose (non-graphical) high performance computing system. It has been widely reported that adapted GPU-based algorithms outperform significantly their CPU counterpart. The focus of the thesis is to utilize NVIDIA CUDA GPUs to implement orthogonal transforms such as signal dependent Karhunen-Loeve Transform and signal independent Discrete Cosine Transform. GPU architecture and programming model are examined. Mathematical preliminaries of orthogonal transform, eigen-analysis and algorithms are re-visited. Due to highly parallel structure, GPUs are well suited to such computation. Further, the thesis examines multiple implementations schemes and configuration, measurement of performance is provided. A real time processing display application frame is developed to visually exhibit GPU compute capability.","abstract_html":"Traditionally, intensive floating-point computational ability of Graphics Processing Units (GPUs) has been mainly limited for rendering and visualization application by architecture and programming model. However, with increasing programmability and architecture progress, GPUs inherent massively parallel computational ability have become an essential part of today's mainstream general purpose (non-graphical) high performance computing system. It has been widely reported that adapted GPU-based algorithms outperform significantly their CPU counterpart. The focus of the thesis is to utilize NVIDIA CUDA GPUs to implement orthogonal transforms such as signal dependent Karhunen-Loeve Transform and signal independent Discrete Cosine Transform. GPU architecture and programming model are examined. Mathematical preliminaries of orthogonal transform, eigen-analysis and algorithms are re-visited. Due to highly parallel structure, GPUs are well suited to such computation. Further, the thesis examines multiple implementations schemes and configuration, measurement of performance is provided. A real time processing display application frame is developed to visually exhibit GPU compute capability.","abstract_has_math":false,"creators":["Zhang, Boyan"],"institution":null,"degree_name":"Master of Science in Electrical Engineering - (M.S.)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Ali N. Akansu","Richard A. Haddad","Edip Niver"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-08-31T07:00:00Z","date_published":"2012-08-31T07:00:00Z","updated_at":"2026-07-24T03:22:26Z","subjects":["Karhunen-Loeve transform","Discrete cosine transform","Graphics processing unit architecture","Electrical and Electronics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/theses/149","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ali N. Akansu","Richard A. 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However, with increasing programmability and architecture progress, GPUs inherent massively parallel computational ability have become an essential part of today's mainstream general purpose (non-graphical) high performance computing system. It has been widely reported that adapted GPU-based algorithms outperform significantly their CPU counterpart. The focus of the thesis is to utilize NVIDIA CUDA GPUs to implement orthogonal transforms such as signal dependent Karhunen-Loeve Transform and signal independent Discrete Cosine Transform. GPU architecture and programming model are examined. Mathematical preliminaries of orthogonal transform, eigen-analysis and algorithms are re-visited. Due to highly parallel structure, GPUs are well suited to such computation. Further, the thesis examines multiple implementations schemes and configuration, measurement of performance is provided. A real time processing display application frame is developed to visually exhibit GPU compute capability."]},{"key":"dc:title","label":"Title","values":["GPU implementation of block transforms"]}]}],"canonical_facts":{"dc:contributor":["Ali N. Akansu","Richard A. Haddad","Edip Niver"],"dc:creator":["Zhang, Boyan"],"dc:description.abstract":["Traditionally, intensive floating-point computational ability of Graphics Processing Units (GPUs) has been mainly limited for rendering and visualization application by architecture and programming model. However, with increasing programmability and architecture progress, GPUs inherent massively parallel computational ability have become an essential part of today's mainstream general purpose (non-graphical) high performance computing system. It has been widely reported that adapted GPU-based algorithms outperform significantly their CPU counterpart. The focus of the thesis is to utilize NVIDIA CUDA GPUs to implement orthogonal transforms such as signal dependent Karhunen-Loeve Transform and signal independent Discrete Cosine Transform. GPU architecture and programming model are examined. Mathematical preliminaries of orthogonal transform, eigen-analysis and algorithms are re-visited. Due to highly parallel structure, GPUs are well suited to such computation. Further, the thesis examines multiple implementations schemes and configuration, measurement of performance is provided. A real time processing display application frame is developed to visually exhibit GPU compute capability."],"dc:identifier":["https://digitalcommons.njit.edu/theses/149"],"dc:subject":["Karhunen-Loeve transform","Discrete cosine transform","Graphics processing unit architecture","Electrical and Electronics"],"dc:title":["GPU implementation of block transforms"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Science in Electrical Engineering - (M.S.)"]},"updated_at":"2026-07-24T03:22:26Z"}