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The implementation and evaluation of parallel algorithms on C.mmp /Oleinick, Peter N. January 1978 (has links)
Thesis (Ph. D.)--Carnegie-Mellon University. / "CMU-CS-78-151." "Supported by the Defense Advanced Research Projects Agency (Contract: F44620-73-C-0074, monitored by the Air Force Office of Scientific Research), and in part by the Office of Naval Research (Contract: N00014-77-C-0500." Includes bibliographical references (leaves [96]-97).
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Scalable parallel algorithms and software for large scale proteomicsKulkarni, Gaurav Ramesh. January 2010 (has links) (PDF)
Thesis (M.S. in computer science)--Washington State University, May 2010. / Title from PDF title page (viewed on June 2, 2010). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 64-66).
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Solutions for some problems in star graphs /Au Yeung, Chun-kan. January 2005 (has links)
Thesis (Ph. D.)--University of Hong Kong, 2005.
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Systolic arrays for the matrix iterative methodsHaider, Shahid Abbas January 1993 (has links)
The systolic array research was pioneered by H. T. Kung and C. E. Leiserson. Systolic arrays are special purpose synchronous architectures consisting of simple, regular and modular processors which are regularly interconnected to form an array. Systolic arrays are well suited for computational bound problems in Linear Algebra. In this thesis, the numerical problems, especially iterative algorithms are chosen and implemented on the linear systolic array. same.
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Massively Parallel Computing and Polynomial GCD'sSantavy, Martin January 1987 (has links)
Note:
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Parallel conic recognition by bus automata /Breene, Leila Anne January 1982 (has links)
No description available.
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Real-Time Processing and Visualization of 3D Time-Variant DatasetsElshahali, Mai Hassan Ahmed Ali 14 September 2015 (has links)
Scientific visualization is primarily concerned with the visual presentation of three-dimensional phenomena in domains like medicine, meteorology, astrophysics, etc. The emphasis in scientific visualization research has been on the efficient rendering of measured or simulated data points, surfaces, volumes, and a time component to convey the dynamic nature of the studied phenomena. With the explosive growth in the size of the data, interactive visualization of scientific data becomes a real challenge. In recent years, the graphics community has witnessed tremendous improvements in the performance capabilities of graphics processing units (GPUs), and advances in GPU-accelerated rendering have enabled data exploration at interactive rates. Nevertheless, the majority of techniques rely on the assumption that a true three-dimensional geometric model capturing physical phenomena of interest, is available and ready for visualization. Unfortunately, this assumption does not hold true in many scientific domains, in which measurements are obtained from a given scanning modality at sparsely located intervals in both space and time. This calls for the fusion of data collected from multiple sources in order to fill the gaps and tell the story behind the data.
For years, data fusion has relied on machine learning techniques to combine data from multiple modalities, reconstruct missing information, and track features of interest through time. However, these techniques fall short in solving the problem for datasets with large spatio-temporal gaps. This realization has led researchers in the data fusion domain to acknowledge the importance of human-in-the-loop methods where human expertise plays a major role in data reconstruction.
This PhD research focuses on developing visualization and interaction techniques aimed at addressing some of the challenges that experts are faced with when analyzing the spatio-temporal behavior of physical phenomena. Given a number of datasets obtained from different measurement modalities and from simulation, we propose a generalized framework that can guide research in the field of multi-sensor data fusion and visualization. We advocate the use of GPU parallelism in our developed techniques in order to emphasize interaction as a key component in the successful exploration and analysis of multi-sourced data sets. The goal is to allow the user to create a mental model that captures their understanding of the spatio-temporal behavior of features of interest; one which they can test against real data measurements to verify their model. This model creation and verification is an iterative process in which the user interacts with the visualization, explores and builds an understanding of what occurred in the data, then tests this understanding against real-world measurements and improves it.
We developed a system as a reference implementation of the proposed framework. Reconstructed data is rendered in a way that completes the users' cognitive model, which encodes their understanding of the phenomena in question with a high degree of accuracy. We tested the usability of the system and evaluated its support for this cognitive model construction process. Once an acceptable model is constructed, it is fed back to the system in the form of a reference dataset, which our framework uses to guide the real-time tracking of measurement data. Our results show that interactive exploration tasks enable the construction of this cognitive model and reference set, and that real-time interaction is achievable during the exploration, reconstruction, and enhancement of multi-modal time-variant three-dimensional data, by designing and implementing advanced GPU-based visualization techniques. / Ph. D.
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Shuffle-X graphs and their cayley variants陳貴海, Chen, Guihai. January 1997 (has links)
published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy
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Virtual links for multicomputersWai, Siu-kit, 衛兆傑 January 1996 (has links)
(Uncorrected OCR)
Abstract of Thesis entitled 'Virtual Links for Multicomputers' Submitted by
Siu Kit Wai
for the degree of
Master of Philosophy at Univsersity of Hong Kong in October 1996
In order to increase computation power, multiple autonomous computers or processors are connected to form a multicomputer. The performance boost is the result of exploiting in parallel the processing power available in individual processors. Parallel processing, however, requires the cooperation among the processors, which implies interprocessor communication. The efficiency of such communications is limited by the bandwidth and number of communication channels between directly connected processors.
Multiple processes on a processor share a few hardware communication links/channels to communication with processes executing on a different processor. Effective and efficient sharing of channels is important for the overall system performance; hence it is important that the sharing be properly managed. When the sharing is not provided by the hardware, it can be provided in software at system level. Without a managing component, processes need to be programmed to flight for and gain exclusive access to the communication links. This is usually not effective, error-prone, and could reduce the overall performance of processes executing in the processor.
Flexibility is a main advantage of providing a channel-sharing mechanism at system level. Parameters such as packet size, and configuration of the system can be customized and tuned to meet the communication characteristics of different applications.
In this project, we investigate how link sharing can be provided at system level. Our approach is based on idea of virtual links. The system is designed to be as transparent and easy to be used as possible. We will discuss how different
parameters and configurations affect the system functionality and performance. We also compare this software solution to other existing solutions including a hardware solution.
ii / abstract / toc / Computer Science / Master / Master of Philosophy
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Towards a new extension relation for compositional test case generation for CSP concurrent processesChan, Wing-kwong., 陳榮光. January 2003 (has links)
published_or_final_version / Computer Science and Information Systems / Doctoral / Doctor of Philosophy
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