Exhibits, Demos & Posters
Visualization and Analysis of GPU Summer School Applicants and Participants
Authors
- Elaine Wah, Department of Computer Science, University of California, Los Angeles
- Erik Johnson, Department of Electrical and Computer Engineering, University of Illinois at Urbana‐Champaign
- Loretta Auvil, National Center for Supercomputing Applications, University of Illinois at Urbana‐Champaign
- Umesh Thakkar, National Center for Supercomputing Applications, University of Illinois at Urbana‐Champaign
- Wen-Mei Hwu, Department of Electrical and Computer Engineering, University of Illinois at Urbana‐Champaign
- David Kirk, NVIDIA Corporation
- Thom H. Dunning, National Center for Supercomputing Applications, University of Illinois at Urbana‐Champaign
- Sharon C. Glotzer, Department of Chemical Engineering and the Department of Materials Science and Engineering, University of Michigan, Ann Arbor
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
