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You searched for +publisher:"Texas State University – San Marcos" +contributor:("Zong, Ziliang"). Showing records 1 – 14 of 14 total matches.

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Texas State University – San Marcos

1. Chen, Xinbo. Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 In recent years, convolutional neural network (CNN) has been widely used to improve the training time and accuracy of image recognition applications. These CNNs are… (more)

Subjects/Keywords: Neural network; CNN; Energy efficiency

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APA (6th Edition):

Chen, X. (2016). Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6853

Chicago Manual of Style (16th Edition):

Chen, Xinbo. “Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/6853.

MLA Handbook (7th Edition):

Chen, Xinbo. “Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition.” 2016. Web. 31 Mar 2020.

Vancouver:

Chen X. Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/6853.

Council of Science Editors:

Chen X. Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6853


Texas State University – San Marcos

2. Mahajan, Divya. Energy efficiency analysis and optimization of relational and NoSQL databases.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 As big data becomes the norm of various industrial applications, the complexity of database workloads and database system design has increased significantly. To address these… (more)

Subjects/Keywords: Energy efficiency; Optimization; Relational databases; NoSQL; MongoDB; DVFS; Cassandra; MySQL

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APA (6th Edition):

Mahajan, D. (2016). Energy efficiency analysis and optimization of relational and NoSQL databases. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6963

Chicago Manual of Style (16th Edition):

Mahajan, Divya. “Energy efficiency analysis and optimization of relational and NoSQL databases.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/6963.

MLA Handbook (7th Edition):

Mahajan, Divya. “Energy efficiency analysis and optimization of relational and NoSQL databases.” 2016. Web. 31 Mar 2020.

Vancouver:

Mahajan D. Energy efficiency analysis and optimization of relational and NoSQL databases. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/6963.

Council of Science Editors:

Mahajan D. Energy efficiency analysis and optimization of relational and NoSQL databases. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6963


Texas State University – San Marcos

3. Abdulsalam, Sarah. Using the Greenup, Powerup And Speedup Metrics To Evaluate Software Energy Efficiency.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 Green computing has made significant progress in the past decades, which is evidenced by more energy efficient hardware (e.g. low power CPUs, GPUs, SSDs) and… (more)

Subjects/Keywords: Energy efficiency; Power; MPI

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APA (6th Edition):

Abdulsalam, S. (2016). Using the Greenup, Powerup And Speedup Metrics To Evaluate Software Energy Efficiency. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6855

Chicago Manual of Style (16th Edition):

Abdulsalam, Sarah. “Using the Greenup, Powerup And Speedup Metrics To Evaluate Software Energy Efficiency.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/6855.

MLA Handbook (7th Edition):

Abdulsalam, Sarah. “Using the Greenup, Powerup And Speedup Metrics To Evaluate Software Energy Efficiency.” 2016. Web. 31 Mar 2020.

Vancouver:

Abdulsalam S. Using the Greenup, Powerup And Speedup Metrics To Evaluate Software Energy Efficiency. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/6855.

Council of Science Editors:

Abdulsalam S. Using the Greenup, Powerup And Speedup Metrics To Evaluate Software Energy Efficiency. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6855


Texas State University – San Marcos

4. Azimi Moghaddam, Sahar. GPU Execution Tracing and Compression.

Degree: MS, Computer Science, 2017, Texas State University – San Marcos

 Program tracing is widely used for debugging and performance optimization. Whenever a program is traced, the overhead in terms of extra runtime and in terms… (more)

Subjects/Keywords: GPU execution tracing; GPU trace compression; Trace compression; Parallel programming; Data compression

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APA (6th Edition):

Azimi Moghaddam, S. (2017). GPU Execution Tracing and Compression. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/7738

Chicago Manual of Style (16th Edition):

Azimi Moghaddam, Sahar. “GPU Execution Tracing and Compression.” 2017. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/7738.

MLA Handbook (7th Edition):

Azimi Moghaddam, Sahar. “GPU Execution Tracing and Compression.” 2017. Web. 31 Mar 2020.

Vancouver:

Azimi Moghaddam S. GPU Execution Tracing and Compression. [Internet] [Masters thesis]. Texas State University – San Marcos; 2017. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/7738.

Council of Science Editors:

Azimi Moghaddam S. GPU Execution Tracing and Compression. [Masters Thesis]. Texas State University – San Marcos; 2017. Available from: https://digital.library.txstate.edu/handle/10877/7738


Texas State University – San Marcos

5. Romoser, Brian M. HASTE: A Heterogeneously Accelerated SQL Transaction Engine.

Degree: MS, Computer Science, 2014, Texas State University – San Marcos

 Databases are the backbone of the digital age and empower the storage and processing of massive amounts of data. As private and public data grows… (more)

Subjects/Keywords: Parallel Database; Parallelism; Hardware acceleration; GPGPU; MIC'; Xeon Phi; CUDA; Heterogeneous Computing

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APA (6th Edition):

Romoser, B. M. (2014). HASTE: A Heterogeneously Accelerated SQL Transaction Engine. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6550

Chicago Manual of Style (16th Edition):

Romoser, Brian M. “HASTE: A Heterogeneously Accelerated SQL Transaction Engine.” 2014. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/6550.

MLA Handbook (7th Edition):

Romoser, Brian M. “HASTE: A Heterogeneously Accelerated SQL Transaction Engine.” 2014. Web. 31 Mar 2020.

Vancouver:

Romoser BM. HASTE: A Heterogeneously Accelerated SQL Transaction Engine. [Internet] [Masters thesis]. Texas State University – San Marcos; 2014. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/6550.

Council of Science Editors:

Romoser BM. HASTE: A Heterogeneously Accelerated SQL Transaction Engine. [Masters Thesis]. Texas State University – San Marcos; 2014. Available from: https://digital.library.txstate.edu/handle/10877/6550


Texas State University – San Marcos

6. Maleki, Sepideh. Higher-Order and Tuple-Based Massively-Parallel Prefix Sums.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 Prefix sums are an important parallel primitive, especially in massively-parallel programs. This thesis discusses two orthogonal generalizations thereof, which we call higher-order and tuple-based prefix… (more)

Subjects/Keywords: Prefixsum; GPU; Tuplebased; Higherorder; Scan; Number theory; Computer science; Parallel processing (Electronic computers)

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APA (6th Edition):

Maleki, S. (2016). Higher-Order and Tuple-Based Massively-Parallel Prefix Sums. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6056

Chicago Manual of Style (16th Edition):

Maleki, Sepideh. “Higher-Order and Tuple-Based Massively-Parallel Prefix Sums.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/6056.

MLA Handbook (7th Edition):

Maleki, Sepideh. “Higher-Order and Tuple-Based Massively-Parallel Prefix Sums.” 2016. Web. 31 Mar 2020.

Vancouver:

Maleki S. Higher-Order and Tuple-Based Massively-Parallel Prefix Sums. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/6056.

Council of Science Editors:

Maleki S. Higher-Order and Tuple-Based Massively-Parallel Prefix Sums. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6056


Texas State University – San Marcos

7. Devale, Sindhu. Low-Overhead Tracing of Large-Scale Parallel Programs.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 Some parallelization bugs only manifest themselves when a program is executed at scale. Such bugs are notoriously difficult to find, and tracing parallel programs at… (more)

Subjects/Keywords: Tracing; Large-scale Parallel Programs; Parallel processing (Electronic computers)

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APA (6th Edition):

Devale, S. (2016). Low-Overhead Tracing of Large-Scale Parallel Programs. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6049

Chicago Manual of Style (16th Edition):

Devale, Sindhu. “Low-Overhead Tracing of Large-Scale Parallel Programs.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/6049.

MLA Handbook (7th Edition):

Devale, Sindhu. “Low-Overhead Tracing of Large-Scale Parallel Programs.” 2016. Web. 31 Mar 2020.

Vancouver:

Devale S. Low-Overhead Tracing of Large-Scale Parallel Programs. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/6049.

Council of Science Editors:

Devale S. Low-Overhead Tracing of Large-Scale Parallel Programs. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6049


Texas State University – San Marcos

8. Bulgerin, Travis. 3D Sketch Recognition Using The Microsoft Kinect.

Degree: MS, Computer Science, 2014, Texas State University – San Marcos

 The concept of sketch-based recognition has recently been used to enhance object categorization and speed up image retrieval. However, in each of the previous studies,… (more)

Subjects/Keywords: Sketch; Recognition; Computer graphics; Optical data processing; Computer-aided design; Image processing; Optical pattern recognition; Pattern recognition systems; Human-computer interaction

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APA (6th Edition):

Bulgerin, T. (2014). 3D Sketch Recognition Using The Microsoft Kinect. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/4988

Chicago Manual of Style (16th Edition):

Bulgerin, Travis. “3D Sketch Recognition Using The Microsoft Kinect.” 2014. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/4988.

MLA Handbook (7th Edition):

Bulgerin, Travis. “3D Sketch Recognition Using The Microsoft Kinect.” 2014. Web. 31 Mar 2020.

Vancouver:

Bulgerin T. 3D Sketch Recognition Using The Microsoft Kinect. [Internet] [Masters thesis]. Texas State University – San Marcos; 2014. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/4988.

Council of Science Editors:

Bulgerin T. 3D Sketch Recognition Using The Microsoft Kinect. [Masters Thesis]. Texas State University – San Marcos; 2014. Available from: https://digital.library.txstate.edu/handle/10877/4988


Texas State University – San Marcos

9. Taheri, Saeed. A Tool for Automatic Suggestions for Irregular GPU Kernel Optimization.

Degree: MS, Computer Science, 2014, Texas State University – San Marcos

 Future computing systems, from handhelds all the way to supercomputers, will be more parallel and more heterogeneous than today’s systems to provide more performance without… (more)

Subjects/Keywords: GPU; Optimization; Irregular; Computer science; Graphics processing units; Parallel computers; Parallel processing (Electronic computers)

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APA (6th Edition):

Taheri, S. (2014). A Tool for Automatic Suggestions for Irregular GPU Kernel Optimization. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/5380

Chicago Manual of Style (16th Edition):

Taheri, Saeed. “A Tool for Automatic Suggestions for Irregular GPU Kernel Optimization.” 2014. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/5380.

MLA Handbook (7th Edition):

Taheri, Saeed. “A Tool for Automatic Suggestions for Irregular GPU Kernel Optimization.” 2014. Web. 31 Mar 2020.

Vancouver:

Taheri S. A Tool for Automatic Suggestions for Irregular GPU Kernel Optimization. [Internet] [Masters thesis]. Texas State University – San Marcos; 2014. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/5380.

Council of Science Editors:

Taheri S. A Tool for Automatic Suggestions for Irregular GPU Kernel Optimization. [Masters Thesis]. Texas State University – San Marcos; 2014. Available from: https://digital.library.txstate.edu/handle/10877/5380


Texas State University – San Marcos

10. Zecena, Ivan. Energy Consumption Analysis of Parallel Algorithms Running on Multicore Systems and GPUS.

Degree: MS, Computer Science, 2013, Texas State University – San Marcos

 As multicore computers and High Performance Computing systems in general continue to increase their number of processors and processing power, so too have the energy… (more)

Subjects/Keywords: Energy-Efficiency; Parallel programming; GPU; Multicore

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APA (6th Edition):

Zecena, I. (2013). Energy Consumption Analysis of Parallel Algorithms Running on Multicore Systems and GPUS. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/5469

Chicago Manual of Style (16th Edition):

Zecena, Ivan. “Energy Consumption Analysis of Parallel Algorithms Running on Multicore Systems and GPUS.” 2013. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/5469.

MLA Handbook (7th Edition):

Zecena, Ivan. “Energy Consumption Analysis of Parallel Algorithms Running on Multicore Systems and GPUS.” 2013. Web. 31 Mar 2020.

Vancouver:

Zecena I. Energy Consumption Analysis of Parallel Algorithms Running on Multicore Systems and GPUS. [Internet] [Masters thesis]. Texas State University – San Marcos; 2013. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/5469.

Council of Science Editors:

Zecena I. Energy Consumption Analysis of Parallel Algorithms Running on Multicore Systems and GPUS. [Masters Thesis]. Texas State University – San Marcos; 2013. Available from: https://digital.library.txstate.edu/handle/10877/5469


Texas State University – San Marcos

11. Hesaaraki, Farbod. Unobtrusive Real-Time Tracing of Parallel Programs.

Degree: MS, Computer Science, 2015, Texas State University – San Marcos

 The US loses approximately 20 to 60 billion a year due to software bugs and glitches, many of which could be avoided if software developers… (more)

Subjects/Keywords: Compression; Tracing; Parallel; Program; Unobtrusive; Real-time; Debugging in computer science; Parallel programming (Computer science)

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APA (6th Edition):

Hesaaraki, F. (2015). Unobtrusive Real-Time Tracing of Parallel Programs. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/5550

Chicago Manual of Style (16th Edition):

Hesaaraki, Farbod. “Unobtrusive Real-Time Tracing of Parallel Programs.” 2015. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/5550.

MLA Handbook (7th Edition):

Hesaaraki, Farbod. “Unobtrusive Real-Time Tracing of Parallel Programs.” 2015. Web. 31 Mar 2020.

Vancouver:

Hesaaraki F. Unobtrusive Real-Time Tracing of Parallel Programs. [Internet] [Masters thesis]. Texas State University – San Marcos; 2015. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/5550.

Council of Science Editors:

Hesaaraki F. Unobtrusive Real-Time Tracing of Parallel Programs. [Masters Thesis]. Texas State University – San Marcos; 2015. Available from: https://digital.library.txstate.edu/handle/10877/5550


Texas State University – San Marcos

12. Fang, Shaomin. Learning Image Saliency from Human Touch Behaviors.

Degree: MS, Computer Science, 2013, Texas State University – San Marcos

 The concept of touch saliency has recently been introduced as a possible alternative for eye tracking in usability studies. This touch saliency study shows that… (more)

Subjects/Keywords: Visual Attention; Touch Saliency; Image Saliency; Touch Behaviors.

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APA (6th Edition):

Fang, S. (2013). Learning Image Saliency from Human Touch Behaviors. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6271

Chicago Manual of Style (16th Edition):

Fang, Shaomin. “Learning Image Saliency from Human Touch Behaviors.” 2013. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/6271.

MLA Handbook (7th Edition):

Fang, Shaomin. “Learning Image Saliency from Human Touch Behaviors.” 2013. Web. 31 Mar 2020.

Vancouver:

Fang S. Learning Image Saliency from Human Touch Behaviors. [Internet] [Masters thesis]. Texas State University – San Marcos; 2013. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/6271.

Council of Science Editors:

Fang S. Learning Image Saliency from Human Touch Behaviors. [Masters Thesis]. Texas State University – San Marcos; 2013. Available from: https://digital.library.txstate.edu/handle/10877/6271

13. LaKomski, Donna. Understanding the Impact of Hybrid Programming on Software Energy Efficiency.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 High performance computing systems today are heterogeneous in nature with multiple CPUs and accelerators/coprocessors in each computing node. The majority of today's programs only utilize… (more)

Subjects/Keywords: Parallel; Hybrid; Energy; Efficiency; Partitioning; Artificial intelligence; Computational intelligence; Soft computing

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APA (6th Edition):

LaKomski, D. (2016). Understanding the Impact of Hybrid Programming on Software Energy Efficiency. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6261

Chicago Manual of Style (16th Edition):

LaKomski, Donna. “Understanding the Impact of Hybrid Programming on Software Energy Efficiency.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/6261.

MLA Handbook (7th Edition):

LaKomski, Donna. “Understanding the Impact of Hybrid Programming on Software Energy Efficiency.” 2016. Web. 31 Mar 2020.

Vancouver:

LaKomski D. Understanding the Impact of Hybrid Programming on Software Energy Efficiency. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/6261.

Council of Science Editors:

LaKomski D. Understanding the Impact of Hybrid Programming on Software Energy Efficiency. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6261

14. Rahman, Saami. An Exploration into the Effectiveness of Prefetching on Program Performance with the Help of an Autotuning Model.

Degree: MS, Computer Science, 2015, Texas State University – San Marcos

 This thesis presents the effects of hardware prefetching on the performance of a collection of programs and how learning algorithms can be used to predict… (more)

Subjects/Keywords: Prefetching; Autotuning; Computer networks; Memory management (Computer science); Automatic control

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APA (6th Edition):

Rahman, S. (2015). An Exploration into the Effectiveness of Prefetching on Program Performance with the Help of an Autotuning Model. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/5537

Chicago Manual of Style (16th Edition):

Rahman, Saami. “An Exploration into the Effectiveness of Prefetching on Program Performance with the Help of an Autotuning Model.” 2015. Masters Thesis, Texas State University – San Marcos. Accessed March 31, 2020. https://digital.library.txstate.edu/handle/10877/5537.

MLA Handbook (7th Edition):

Rahman, Saami. “An Exploration into the Effectiveness of Prefetching on Program Performance with the Help of an Autotuning Model.” 2015. Web. 31 Mar 2020.

Vancouver:

Rahman S. An Exploration into the Effectiveness of Prefetching on Program Performance with the Help of an Autotuning Model. [Internet] [Masters thesis]. Texas State University – San Marcos; 2015. [cited 2020 Mar 31]. Available from: https://digital.library.txstate.edu/handle/10877/5537.

Council of Science Editors:

Rahman S. An Exploration into the Effectiveness of Prefetching on Program Performance with the Help of an Autotuning Model. [Masters Thesis]. Texas State University – San Marcos; 2015. Available from: https://digital.library.txstate.edu/handle/10877/5537

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