Towards Algorithm Transformation for Temporal Data Mining on GPU

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2009

Author dc:creator

• Ponce, Sean Philip

Chair dc:contributor.committeechair

• Cao, Yong

Committee members dc:contributor.committeemember

• Feng, Wu-chun
• Ramakrishnan, Naren

• CUDA
• GPGPU
• temporal data mining

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-08062009-133358

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/34387