Premium
Improving classification and clustering techniques using GPUs
Author(s) -
Jararweh Yaser,
Shehab Mohammed A.,
Yaseen Qussai,
AlAyyoub Mahmoud
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5538
Subject(s) - computer science , cluster analysis , implementation , graphics processing unit , general purpose computing on graphics processing units , scheduling (production processes) , graphics , cuda , process (computing) , gpu cluster , parallel computing , data mining , artificial intelligence , computer graphics (images) , operations management , economics , programming language , operating system
Summary Classification and clustering techniques are used in different applications. Large‐scale big data applications such as social networks analysis applications need to process large data chunks in a short time. Classification and clustering tasks in such applications consume a lot of processing time. Improving the performance of classification and clustering algorithms enhances the performance of applications that use such type of algorithms. This paper introduces an approach for exploiting the graphics processing unit (GPU) platform to improve the performance of classification and clustering algorithms. The proposed approach uses two GPUs implementations, which are the pure GPU or GPU‐only implementation and the GPU‐CPU hybrid implementation. The results show that the hybrid implementation, which optimizes the subtask scheduling for both the CPU and the GPU processing elements, outperforms the approach that uses only the GPU.