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:"Georgia Tech" +contributor:("Vuduc, Rich"). Showing records 1 – 5 of 5 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


Georgia Tech

1. Kannan, Ramakrishnan. Scalable and distributed constrained low rank approximations.

Degree: PhD, Computational Science and Engineering, 2016, Georgia Tech

 Low rank approximation is the problem of finding two low rank factors W and H such that the rank(WH) << rank(A) and A ≈ WH.… (more)

Subjects/Keywords: Distributed; Scalable; NMF; Communication avoiding; HPC; Low rank approximation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Kannan, R. (2016). Scalable and distributed constrained low rank approximations. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54962

Chicago Manual of Style (16th Edition):

Kannan, Ramakrishnan. “Scalable and distributed constrained low rank approximations.” 2016. Doctoral Dissertation, Georgia Tech. Accessed May 26, 2019. http://hdl.handle.net/1853/54962.

MLA Handbook (7th Edition):

Kannan, Ramakrishnan. “Scalable and distributed constrained low rank approximations.” 2016. Web. 26 May 2019.

Vancouver:

Kannan R. Scalable and distributed constrained low rank approximations. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 May 26]. Available from: http://hdl.handle.net/1853/54962.

Council of Science Editors:

Kannan R. Scalable and distributed constrained low rank approximations. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/54962


Georgia Tech

2. Holmes, Michael P. Multi-tree Monte Carlo methods for fast, scalable machine learning.

Degree: PhD, Computing, 2009, Georgia Tech

 As modern applications of machine learning and data mining are forced to deal with ever more massive quantities of data, practitioners quickly run into difficulty… (more)

Subjects/Keywords: Machine learning; SVD; Scalable; Monte Carlo; Kernel estimators; Large data; Monte Carlo method; Trees Development Data processing; Algorithms; Computer algorithms

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Holmes, M. P. (2009). Multi-tree Monte Carlo methods for fast, scalable machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33865

Chicago Manual of Style (16th Edition):

Holmes, Michael P. “Multi-tree Monte Carlo methods for fast, scalable machine learning.” 2009. Doctoral Dissertation, Georgia Tech. Accessed May 26, 2019. http://hdl.handle.net/1853/33865.

MLA Handbook (7th Edition):

Holmes, Michael P. “Multi-tree Monte Carlo methods for fast, scalable machine learning.” 2009. Web. 26 May 2019.

Vancouver:

Holmes MP. Multi-tree Monte Carlo methods for fast, scalable machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2009. [cited 2019 May 26]. Available from: http://hdl.handle.net/1853/33865.

Council of Science Editors:

Holmes MP. Multi-tree Monte Carlo methods for fast, scalable machine learning. [Doctoral Dissertation]. Georgia Tech; 2009. Available from: http://hdl.handle.net/1853/33865


Georgia Tech

3. Agarwal, Virat. Algorithm design on multicore processors for massive-data analysis.

Degree: PhD, Computational Science and Engineering, 2010, Georgia Tech

 Analyzing massive-data sets and streams is computationally very challenging. Data sets in systems biology, network analysis and security use network abstraction to construct large-scale graphs.… (more)

Subjects/Keywords: Graph algorithms; Parallel computing; Massive data; Financial market data; Streaming data; Keyword scanning; Data mining; Algorithms; Pattern recognition systems; Computer algorithms

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Agarwal, V. (2010). Algorithm design on multicore processors for massive-data analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/34839

Chicago Manual of Style (16th Edition):

Agarwal, Virat. “Algorithm design on multicore processors for massive-data analysis.” 2010. Doctoral Dissertation, Georgia Tech. Accessed May 26, 2019. http://hdl.handle.net/1853/34839.

MLA Handbook (7th Edition):

Agarwal, Virat. “Algorithm design on multicore processors for massive-data analysis.” 2010. Web. 26 May 2019.

Vancouver:

Agarwal V. Algorithm design on multicore processors for massive-data analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 May 26]. Available from: http://hdl.handle.net/1853/34839.

Council of Science Editors:

Agarwal V. Algorithm design on multicore processors for massive-data analysis. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/34839

4. Anger, Eric. Application-level modeling and analysis of time and energy for optimizing power-constrained extreme-scale applications.

Degree: PhD, Electrical and Computer Engineering, 2016, Georgia Tech

 The objective of the proposed research is to create a methodology for the modeling and characterization of extreme-scale applications operating within power limitations in order… (more)

Subjects/Keywords: Application modeling; Statistical models; System simulation; Macro-scale simulation; Energy modeling; Power modeling

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Anger, E. (2016). Application-level modeling and analysis of time and energy for optimizing power-constrained extreme-scale applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56331

Chicago Manual of Style (16th Edition):

Anger, Eric. “Application-level modeling and analysis of time and energy for optimizing power-constrained extreme-scale applications.” 2016. Doctoral Dissertation, Georgia Tech. Accessed May 26, 2019. http://hdl.handle.net/1853/56331.

MLA Handbook (7th Edition):

Anger, Eric. “Application-level modeling and analysis of time and energy for optimizing power-constrained extreme-scale applications.” 2016. Web. 26 May 2019.

Vancouver:

Anger E. Application-level modeling and analysis of time and energy for optimizing power-constrained extreme-scale applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 May 26]. Available from: http://hdl.handle.net/1853/56331.

Council of Science Editors:

Anger E. Application-level modeling and analysis of time and energy for optimizing power-constrained extreme-scale applications. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56331

5. Ediger, David. Analyzing hybrid architectures for massively parallel graph analysis.

Degree: PhD, Electrical and Computer Engineering, 2013, Georgia Tech

 The quantity of rich, semi-structured data generated by sensor networks, scientific simulation, business activity, and the Internet grows daily. The objective of this research is… (more)

Subjects/Keywords: Data intensive computing; Computer architectures; Cray XMT; Streaming graph algorithms; Multithreaded graph algorithms; Parallel processing (Electronic computers); Computer algorithms; Graph algorithms; Parallel algorithms

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ediger, D. (2013). Analyzing hybrid architectures for massively parallel graph analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47659

Chicago Manual of Style (16th Edition):

Ediger, David. “Analyzing hybrid architectures for massively parallel graph analysis.” 2013. Doctoral Dissertation, Georgia Tech. Accessed May 26, 2019. http://hdl.handle.net/1853/47659.

MLA Handbook (7th Edition):

Ediger, David. “Analyzing hybrid architectures for massively parallel graph analysis.” 2013. Web. 26 May 2019.

Vancouver:

Ediger D. Analyzing hybrid architectures for massively parallel graph analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 May 26]. Available from: http://hdl.handle.net/1853/47659.

Council of Science Editors:

Ediger D. Analyzing hybrid architectures for massively parallel graph analysis. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/47659

.