Paper Info
Title
Parallel evaluation of Pittsburgh rule-based classifiers on GPUs
Abstract
Individuals from Pittsburgh rule-based classifiers represent a complete solution to the classification problem and each individual is a variable-length set of rules. Therefore, these systems usually demand a high level of computational resources and run-time, which increases as the complexity and the size of the data sets. It is known that this computational cost is mainly due to the recurring evaluation process of the rules and the individuals as rule sets. In this paper we propose a parallel evaluation model of rules and rule sets on GPUs based on the NVIDIA CUDA programming model which significantly allows reducing the run-time and speeding up the algorithm. The results obtained from the experimental study support the great efficiency and high performance of the GPU model, which is scalable to multiple GPU devices. The GPU model achieves a rule interpreter performance of up to 64 billion operations per second and the evaluation of the individuals is speeded up of up to 3.461xwhen compared to the CPU model. This provides a significant advantage of the GPU model, especially addressing large and complex problems within reasonable time, where the CPU run-time is not acceptable.
Year
DOI
Venue
2014
10.1016/j.neucom.2013.01.049
Neurocomputing
Keywords
Field
DocType
evaluation process,parallel evaluation model,cpu run-time,multiple gpu device,cpu model,pittsburgh rule-based classifier,nvidia cuda programming model,rule set,computational cost,gpu model,rule interpreter performance,parallel computing,classification
Rule-based system,Central processing unit,Data set,Cuda programming,Computer science,Interpreter,Artificial intelligence,Machine learning,Complex problems,Scalability
Journal
Volume
ISSN
Citations 
126,
0925-2312
9
PageRank 
References 
Authors
0.48
40
3
Name
Order
Citations
PageRank
Alberto Cano126918.88
Amelia Zafra243222.64
S. Ventura32318158.44