Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for +publisher:"University of Houston" +contributor:("Wu, Xuqing"). Showing records 1 – 2 of 2 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


University of Houston

1. Megharaj, Paraag Ashok Kumar. Integrating Processing In-Memory (PIM) Technology into General Purpose Graphics Processing Units (GPGPU) for Energy Efficient Computing.

Degree: MSin Electrical Engineering, Electrical Engineering, 2017, University of Houston

Processing-in-memory (PIM) offers a viable solution to overcome the memory wall crisis that has been plaguing memory system for decades. Due to advancements in 3D stacking technology in recent years, PIM provides an opportunity to reduce both energy and data movement overheads, which are the primary concerns in present computer architecture community. General purpose GPU (GPGPU) systems, with most of its emerging applications data intensive, require large volume of data to be transferred at fast pace to keep the computations in processing units running, thereby putting an enormous pressure on the memory systems. To explore the potential of PIM technology in solving the memory wall problem, in this research, we integrate PIM technology with GPGPU systems and develop a mechanism that dynamically identifies and offloads candidate thread blocks to PIM cores. Our offloading mechanism shows significant performance improvement (30% by average and up to 2.1x) as compared to the baseline GPGPU system without block offloading. Advisors/Committee Members: Fu, Xin (advisor), Chen, Jinghong (committee member), Wu, Xuqing (committee member).

Subjects/Keywords: Processing in-memory; GPGPU; Offloading

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Megharaj, P. A. K. (2017). Integrating Processing In-Memory (PIM) Technology into General Purpose Graphics Processing Units (GPGPU) for Energy Efficient Computing. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/4820

Chicago Manual of Style (16th Edition):

Megharaj, Paraag Ashok Kumar. “Integrating Processing In-Memory (PIM) Technology into General Purpose Graphics Processing Units (GPGPU) for Energy Efficient Computing.” 2017. Masters Thesis, University of Houston. Accessed April 14, 2021. http://hdl.handle.net/10657/4820.

MLA Handbook (7th Edition):

Megharaj, Paraag Ashok Kumar. “Integrating Processing In-Memory (PIM) Technology into General Purpose Graphics Processing Units (GPGPU) for Energy Efficient Computing.” 2017. Web. 14 Apr 2021.

Vancouver:

Megharaj PAK. Integrating Processing In-Memory (PIM) Technology into General Purpose Graphics Processing Units (GPGPU) for Energy Efficient Computing. [Internet] [Masters thesis]. University of Houston; 2017. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10657/4820.

Council of Science Editors:

Megharaj PAK. Integrating Processing In-Memory (PIM) Technology into General Purpose Graphics Processing Units (GPGPU) for Energy Efficient Computing. [Masters Thesis]. University of Houston; 2017. Available from: http://hdl.handle.net/10657/4820


University of Houston

2. Kotadia, Kinjal 1994-. Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering.

Degree: MS, Computer Science, 2018, University of Houston

Detecting Social Network Groups from Video data acquired from surveillance cameras is a challenging problem currently being addressed by the Data Mining and Computer Vision Communities. As a part of continuing research in this area, a new graph-based post analysis approach is developed to process data obtained from the state-of-the-art Detection and Tracking systems to extract the various social groups present in it. The process of extracting social network groups is primarily divided into two tasks. The first task consists of finding a method to compute a graph that connects all the people present in the video. Motion similarity between the tracks of the people on the ground plane is used as a metric to compute the weights on the edges of the graph. The second task is to cut the graph to form groups which is done by creating a minimal spanning tree and cutting the edges with least weights. The number of cuts to be made depends on the number of groups that are present in the video. To deal with the problem of unknown number of groups, the parameter of consistency of within cluster distances is exploited and the number of groups is decided by the finding the elbow point in the plot. The method shows promising results with UCLA Courtyard Dataset Videos and Simulation systems. This work can be regarded as one of the many approaches to solve the problem of “Detecting Social Networks from Video Data” which tend to exhibit decent outcomes. Advisors/Committee Members: Shah, Shishir Kirit (advisor), Eick, Christoph F. (committee member), Wu, Xuqing (committee member).

Subjects/Keywords: Social Network Groups; Motion Similarity; Graph Clustering; Elbow Method

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kotadia, K. 1. (2018). Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3117

Chicago Manual of Style (16th Edition):

Kotadia, Kinjal 1994-. “Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering.” 2018. Masters Thesis, University of Houston. Accessed April 14, 2021. http://hdl.handle.net/10657/3117.

MLA Handbook (7th Edition):

Kotadia, Kinjal 1994-. “Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering.” 2018. Web. 14 Apr 2021.

Vancouver:

Kotadia K1. Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering. [Internet] [Masters thesis]. University of Houston; 2018. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10657/3117.

Council of Science Editors:

Kotadia K1. Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering. [Masters Thesis]. University of Houston; 2018. Available from: http://hdl.handle.net/10657/3117

.