GDL

Performance Analysis of High Performance k-Mean Data Mining Algorithm for Multicore Heterogeneous Compute Cluster

ISSN 2223-4985
Journal from gdlhub / 2017-08-14 11:52:32
Oleh : Ramesh Singh Yadava , P.K.Mishra, International Journal of Information and Communication Technology Research
Dibuat : 2012-06-23, dengan 1 file

Keyword : MPI (Message Passing Interface), MPICH2 (High performance & widely portable implementation of MPI)
Subjek : Performance Analysis of High Performance k-Mean Data Mining Algorithm for Multicore Heterogeneous Compute Cluster
Url : http://esjournals.org/journaloftechnology/archive/vol2no4/vol2no4_8.pdf
Sumber pengambilan dokumen : Internet

In this paper, we have study the performance of k-Mean data-mining algorithm (k-Mean),which is implemented on the heterogeneous


compute cluster with the multi core programming. The multicore program is implemented with MPI and C for the parallel computing


and utilizing the maximum compute power of the heterogeneous cluster. The heterogeneous cluster is established with the help of


MPICH2.



We have also analyzed the efficiency and performance of k-Mean data mining algorithm for the large dataset. The dataset, which we have


used, is chess.txt [1]. The dataset is divided into the number of cores and core compute the dataset independently and makes a data cluster


of similar dataset on each processor core.



Through this implementation, we have justified that the communication time among the processor cannot be negligible for large dataset.


So, the compute time for same dataset on different processors core with same speed and memory is different and the different processor


with different speeds and memory access also take different time.

Deskripsi Alternatif :

In this paper, we have study the performance of k-Mean data-mining algorithm (k-Mean),which is implemented on the heterogeneous


compute cluster with the multi core programming. The multicore program is implemented with MPI and C for the parallel computing


and utilizing the maximum compute power of the heterogeneous cluster. The heterogeneous cluster is established with the help of


MPICH2.



We have also analyzed the efficiency and performance of k-Mean data mining algorithm for the large dataset. The dataset, which we have


used, is chess.txt [1]. The dataset is divided into the number of cores and core compute the dataset independently and makes a data cluster


of similar dataset on each processor core.



Through this implementation, we have justified that the communication time among the processor cannot be negligible for large dataset.


So, the compute time for same dataset on different processors core with same speed and memory is different and the different processor


with different speeds and memory access also take different time.

Beri Komentar ?#(0) | Bookmark

Properti	Nilai Properti
ID Publisher	gdlhub
Organisasi	International Journal of Information and Communication Technology Research
Nama Kontak	Herti Yani, S.Kom
Alamat	Jln. Jenderal Sudirman
Kota	Jambi
Daerah	Jambi
Negara	Indonesia
Telepon	0741-35095
Fax	0741-35093
E-mail Administrator	elibrarystikom@gmail.com
E-mail CKO	elibrarystikom@gmail.com