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Impact Of Thread Scheduling On Modern Gpus, Orevaoghene Addoh
Impact Of Thread Scheduling On Modern Gpus, Orevaoghene Addoh
Electronic Theses and Dissertations
The Graphics Processing Unit (GPU) has become a more important component in high-performance computing systems as it accelerates data and compute intensive applications significantly with less cost and power. The GPU achieves high performance by executing massive number of threads in parallel in a SPMD (Single Program Multiple Data) fashion. Threads are grouped into workgroups by programmer and workgroups are then assigned to each compute core on the GPU by hardware. Once assigned, a workgroup is further subgrouped into wavefronts of the fixed number of threads by hardware when executed in a SIMD (Single Instruction Multiple Data) fashion. In this …
Segmentation And Spatial Depth Ridge Detection Of Unorganized Point Cloud Data, James Clark Church
Segmentation And Spatial Depth Ridge Detection Of Unorganized Point Cloud Data, James Clark Church
Electronic Theses and Dissertations
Visual 3D data are of interest to a number of fields: medical professionals, game designers, graphic designers, and (in the interest of this paper) ichthyologists interested in the taxonomy of fish. Since the release of the Kinect for the Microsoft XBox, game designers have been interested in using the 3D data returned by the device to understand human movement and translate that movement into an interface with which to interact with game systems. In the medical field, researchers must use computer vision tools to navigate through the data found in CT scans and MRI scans. These tools must segment images …
Exploration Into The Performance Of Asymmetric D-Ary Heap-Based Algorithms For The Hsa Architecture, Stephen Adams
Exploration Into The Performance Of Asymmetric D-Ary Heap-Based Algorithms For The Hsa Architecture, Stephen Adams
Electronic Theses and Dissertations
No abstract provided.
An Efficient Storage And Retrieval Mechanism For Large Unstructured Grids, Oyindamola Akande
An Efficient Storage And Retrieval Mechanism For Large Unstructured Grids, Oyindamola Akande
Electronic Theses and Dissertations
The size of spatial scientific datasets is steadily increasing due to improvements in instruments and availability of computational resources. Scientific datasets today are often far too large to fit into a single machine's memory or even a single disk. However, much of the research on efficient storage and access to spatial datasets has focused on large multidimensional arrays. In contrast, unstructured grids consisting of collections of implices (e.g. triangles or tetrahedra) present special challenges that have received less attention. Data values found at the vertices of the simplices may be dispersed throughout a datafile, producing especially poor disk locality. Partitioning …
Raptorqp2p: Maximize The Performance Of P2p File Distribution With Raptorq Coding, Zeyang Su
Raptorqp2p: Maximize The Performance Of P2p File Distribution With Raptorq Coding, Zeyang Su
Electronic Theses and Dissertations
BitTorrent is the most popular Peer-to-Peer (P2P) file sharing system widely used for distributing large files over the Internet. It has attracted extensive attentions from both network operators and researchers for investigating its deployment and performance. For example, recent studies have shown that under steady state, its rarest first scheme with the tit-for-tat mechanism can work very effectively and make BitTorrent near optimal for the generic file downloading process. However, in practice, the highly dynamic network environment, especially the notorious user churns prevalently existing in most peer-to-peer systems, can severely degrade the downloading performance. In this thesis, we first study …
Random Forests Based Rule Learning And Feature Elimination, Sheng Liu
Random Forests Based Rule Learning And Feature Elimination, Sheng Liu
Electronic Theses and Dissertations
Much research combines data from multiple sources in an effort to understand the underlying problems. It is important to find and interpret the most important information from these sources. Thus it will be beneficial to have an effective algorithm that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance. We propose an efficient approach, combining rule extraction and feature elimination, based on 1-norm regularized random forests. This approach simultaneously extracts a small number of rules generated by random forests and selects important features. To evaluate this approach, we have applied it to …