| Type of Document |
Master's Thesis |
| Author |
Ponce, Sean Philip
|
| Author's Email Address |
ponce@vt.edu |
| URN |
etd-08062009-133358 |
| Title |
Towards Algorithm Transformation for Temporal Data Mining on GPU |
| Degree |
Master of Science |
| Department |
Computer Science |
| Advisory Committee |
| Advisor Name |
Title |
| Cao, Yong |
Committee Chair |
| Feng, Wu-Chun |
Committee Member |
| Ramakrishnan, Naren |
Committee Member |
|
| Keywords |
- CUDA
- GPGPU
- temporal data mining
|
| Date of Defense |
2009-07-07 |
| Availability |
unrestricted |
Abstract
Data Mining allows one to analyze large amounts of data. With increasing amounts of data being collected, more computing power is needed to mine these larger and larger sums of data. The GPU is an excellent piece of hardware with a compelling price to performance ratio and has rapidly risen in popularity. However, this increase in speed comes at a cost. The GPU's architecture executes non-data parallel code with either marginal speedup or even slowdown. The type of data mining we examine, temporal data mining, uses a ¯nite state machine (FSM), which is non-data parallel. We contribute the concept of algorithm transformation for increasing the data parallelism of an algorithm. We apply the algorithm transformation process to the problem of temporal data mining which solves the same problem as the FSM-based algorithm, but is data parallel. The new GPU implementation shows a 6x speedup over the best CPU implementation and 11x speedup over a previous GPU implementation.
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| Files |
| Filename |
Size |
Approximate Download Time
(Hours:Minutes:Seconds)
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| 28.8 Modem |
56K Modem |
ISDN (64 Kb) |
ISDN (128 Kb) |
Higher-speed Access |
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ponce-thesis.pdf |
621.11 Kb |
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