Visualization and analysis of GPU summer school applicants and participants

Elaine Wah, Erik Johnson, Loretta Auvil, Umesh Thakkar, Wen Mei Hwu, David Kirk, Thom H. Dunning, Sharon C. Glotzer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the recent development of petascale computing systems, a long-term effort is needed to educate and train the next generation of researchers. As part of its graduate education component, the Virtual School of Computational Science and Engineering held a summer school in August 2008 entitled "Accelerators for Science and Engineering Applications," providing participants with knowledge and hands-on experience with graphics processing units (GPUs). In this paper, we present visualizations exploring the broad spectrum of summer school applicants and participants. We examine demographic information of the overall applicant pool, accepted and attending applicants, and remote participants, as well as apply hierarchical clustering and rule association techniques to all applicant data. These statistical and data mining analyses demonstrate the wide range of fields of study where research applications can be readily accelerated through the use of massively parallel computing resources.

Original languageEnglish (US)
Title of host publicationProceedings - 4th IEEE International Conference on eScience, eScience 2008
Pages362-363
Number of pages2
DOIs
StatePublished - Dec 1 2008
Externally publishedYes
Event4th IEEE International Conference on eScience, eScience 2008 - Indianapolis, IN, United States
Duration: Dec 7 2008Dec 12 2008

Publication series

NameProceedings - 4th IEEE International Conference on eScience, eScience 2008

Other

Other4th IEEE International Conference on eScience, eScience 2008
CountryUnited States
CityIndianapolis, IN
Period12/7/0812/12/08

Fingerprint

Visualization
Association rules
Parallel processing systems
Particle accelerators
Data mining
Education
Graphics processing unit

Keywords

  • Clustering
  • Data mining
  • Education
  • GPU
  • Rule association
  • Visualization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

Cite this

Wah, E., Johnson, E., Auvil, L., Thakkar, U., Hwu, W. M., Kirk, D., ... Glotzer, S. C. (2008). Visualization and analysis of GPU summer school applicants and participants. In Proceedings - 4th IEEE International Conference on eScience, eScience 2008 (pp. 362-363). [4736797] (Proceedings - 4th IEEE International Conference on eScience, eScience 2008). https://doi.org/10.1109/eScience.2008.134

Visualization and analysis of GPU summer school applicants and participants. / Wah, Elaine; Johnson, Erik; Auvil, Loretta; Thakkar, Umesh; Hwu, Wen Mei; Kirk, David; Dunning, Thom H.; Glotzer, Sharon C.

Proceedings - 4th IEEE International Conference on eScience, eScience 2008. 2008. p. 362-363 4736797 (Proceedings - 4th IEEE International Conference on eScience, eScience 2008).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wah, E, Johnson, E, Auvil, L, Thakkar, U, Hwu, WM, Kirk, D, Dunning, TH & Glotzer, SC 2008, Visualization and analysis of GPU summer school applicants and participants. in Proceedings - 4th IEEE International Conference on eScience, eScience 2008., 4736797, Proceedings - 4th IEEE International Conference on eScience, eScience 2008, pp. 362-363, 4th IEEE International Conference on eScience, eScience 2008, Indianapolis, IN, United States, 12/7/08. https://doi.org/10.1109/eScience.2008.134
Wah E, Johnson E, Auvil L, Thakkar U, Hwu WM, Kirk D et al. Visualization and analysis of GPU summer school applicants and participants. In Proceedings - 4th IEEE International Conference on eScience, eScience 2008. 2008. p. 362-363. 4736797. (Proceedings - 4th IEEE International Conference on eScience, eScience 2008). https://doi.org/10.1109/eScience.2008.134
Wah, Elaine ; Johnson, Erik ; Auvil, Loretta ; Thakkar, Umesh ; Hwu, Wen Mei ; Kirk, David ; Dunning, Thom H. ; Glotzer, Sharon C. / Visualization and analysis of GPU summer school applicants and participants. Proceedings - 4th IEEE International Conference on eScience, eScience 2008. 2008. pp. 362-363 (Proceedings - 4th IEEE International Conference on eScience, eScience 2008).
@inproceedings{d4e9bb94c1bf4b6b9c88443b751876cf,
title = "Visualization and analysis of GPU summer school applicants and participants",
abstract = "With the recent development of petascale computing systems, a long-term effort is needed to educate and train the next generation of researchers. As part of its graduate education component, the Virtual School of Computational Science and Engineering held a summer school in August 2008 entitled {"}Accelerators for Science and Engineering Applications,{"} providing participants with knowledge and hands-on experience with graphics processing units (GPUs). In this paper, we present visualizations exploring the broad spectrum of summer school applicants and participants. We examine demographic information of the overall applicant pool, accepted and attending applicants, and remote participants, as well as apply hierarchical clustering and rule association techniques to all applicant data. These statistical and data mining analyses demonstrate the wide range of fields of study where research applications can be readily accelerated through the use of massively parallel computing resources.",
keywords = "Clustering, Data mining, Education, GPU, Rule association, Visualization",
author = "Elaine Wah and Erik Johnson and Loretta Auvil and Umesh Thakkar and Hwu, {Wen Mei} and David Kirk and Dunning, {Thom H.} and Glotzer, {Sharon C.}",
year = "2008",
month = "12",
day = "1",
doi = "10.1109/eScience.2008.134",
language = "English (US)",
isbn = "9780769535357",
series = "Proceedings - 4th IEEE International Conference on eScience, eScience 2008",
pages = "362--363",
booktitle = "Proceedings - 4th IEEE International Conference on eScience, eScience 2008",

}

TY - GEN

T1 - Visualization and analysis of GPU summer school applicants and participants

AU - Wah, Elaine

AU - Johnson, Erik

AU - Auvil, Loretta

AU - Thakkar, Umesh

AU - Hwu, Wen Mei

AU - Kirk, David

AU - Dunning, Thom H.

AU - Glotzer, Sharon C.

PY - 2008/12/1

Y1 - 2008/12/1

N2 - With the recent development of petascale computing systems, a long-term effort is needed to educate and train the next generation of researchers. As part of its graduate education component, the Virtual School of Computational Science and Engineering held a summer school in August 2008 entitled "Accelerators for Science and Engineering Applications," providing participants with knowledge and hands-on experience with graphics processing units (GPUs). In this paper, we present visualizations exploring the broad spectrum of summer school applicants and participants. We examine demographic information of the overall applicant pool, accepted and attending applicants, and remote participants, as well as apply hierarchical clustering and rule association techniques to all applicant data. These statistical and data mining analyses demonstrate the wide range of fields of study where research applications can be readily accelerated through the use of massively parallel computing resources.

AB - With the recent development of petascale computing systems, a long-term effort is needed to educate and train the next generation of researchers. As part of its graduate education component, the Virtual School of Computational Science and Engineering held a summer school in August 2008 entitled "Accelerators for Science and Engineering Applications," providing participants with knowledge and hands-on experience with graphics processing units (GPUs). In this paper, we present visualizations exploring the broad spectrum of summer school applicants and participants. We examine demographic information of the overall applicant pool, accepted and attending applicants, and remote participants, as well as apply hierarchical clustering and rule association techniques to all applicant data. These statistical and data mining analyses demonstrate the wide range of fields of study where research applications can be readily accelerated through the use of massively parallel computing resources.

KW - Clustering

KW - Data mining

KW - Education

KW - GPU

KW - Rule association

KW - Visualization

UR - http://www.scopus.com/inward/record.url?scp=62749182365&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=62749182365&partnerID=8YFLogxK

U2 - 10.1109/eScience.2008.134

DO - 10.1109/eScience.2008.134

M3 - Conference contribution

AN - SCOPUS:62749182365

SN - 9780769535357

T3 - Proceedings - 4th IEEE International Conference on eScience, eScience 2008

SP - 362

EP - 363

BT - Proceedings - 4th IEEE International Conference on eScience, eScience 2008

ER -