Parallel matricization for n-D array operations

Md Abu Hanif Shaikh; G. G.Md Nawaz Ali; Peter Han Joo Chong; Yong Liang Guan

doi:10.1109/TENCON.2016.7848468

Parallel matricization for n-D array operations

Md Abu Hanif Shaikh, G. G.Md Nawaz Ali, Peter Han Joo Chong, Yong Liang Guan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Success of today's computing depends on finding and operating into data flood with very high number of dimensions. Traditional computing technique for multidimensional data is about half century old and shows worst performance when data dimension is very high. Thus efficient representation and operation on it is cramming needs for data scientist. Two dimensional/row-column representation is facile for imagination and visualization. This paper describes an implementation scheme for higher dimensional array with row-column abstraction on parallel environment like GPU. The representation is just fitting odd dimensions along row-direction and even dimensions along column direction which form groups of 2-D block. Each 2-D block of size blockIdx.x×threadIdx.x is independent of each other. Performance of proposed representation is measured with matrix-matrix addition and multiplication operation. Experimental results show better performance over other representation scheme like Extended Karnaugh Map Representation (EKMR). The scheme can be used for implementing very higher dimensional array in both general purpose and scientific computing on GPU.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2432-2435
Number of pages	4
ISBN (Electronic)	9781509025961
DOIs	https://doi.org/10.1109/TENCON.2016.7848468
Publication status	Published - Feb 8 2017
Externally published	Yes
Event	2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore Duration: Nov 22 2016 → Nov 25 2016

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2016 IEEE Region 10 Conference, TENCON 2016
Country/Territory	Singapore
City	Singapore
Period	11/22/16 → 11/25/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

ASJC Scopus Subject Areas

Computer Science Applications
Electrical and Electronic Engineering

Keywords

Array Operation
CUDA
EKMR
G2A
Matrix Operation
Matrix-Matrix Multiplication

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10.1109/TENCON.2016.7848468

Cite this

Shaikh, M. A. H., Ali, G. G. M. N., Chong, P. H. J., & Guan, Y. L. (2017). Parallel matricization for n-D array operations. In Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016 (pp. 2432-2435). Article 7848468 (IEEE Region 10 Annual International Conference, Proceedings/TENCON). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2016.7848468

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abstract = "Success of today's computing depends on finding and operating into data flood with very high number of dimensions. Traditional computing technique for multidimensional data is about half century old and shows worst performance when data dimension is very high. Thus efficient representation and operation on it is cramming needs for data scientist. Two dimensional/row-column representation is facile for imagination and visualization. This paper describes an implementation scheme for higher dimensional array with row-column abstraction on parallel environment like GPU. The representation is just fitting odd dimensions along row-direction and even dimensions along column direction which form groups of 2-D block. Each 2-D block of size blockIdx.x×threadIdx.x is independent of each other. Performance of proposed representation is measured with matrix-matrix addition and multiplication operation. Experimental results show better performance over other representation scheme like Extended Karnaugh Map Representation (EKMR). The scheme can be used for implementing very higher dimensional array in both general purpose and scientific computing on GPU.",

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Shaikh, MAH, Ali, GGMN, Chong, PHJ & Guan, YL 2017, Parallel matricization for n-D array operations. in Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016., 7848468, IEEE Region 10 Annual International Conference, Proceedings/TENCON, Institute of Electrical and Electronics Engineers Inc., pp. 2432-2435, 2016 IEEE Region 10 Conference, TENCON 2016, Singapore, Singapore, 11/22/16. https://doi.org/10.1109/TENCON.2016.7848468

Parallel matricization for n-D array operations. / Shaikh, Md Abu Hanif; Ali, G. G.Md Nawaz; Chong, Peter Han Joo et al.
Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2432-2435 7848468 (IEEE Region 10 Annual International Conference, Proceedings/TENCON).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - Success of today's computing depends on finding and operating into data flood with very high number of dimensions. Traditional computing technique for multidimensional data is about half century old and shows worst performance when data dimension is very high. Thus efficient representation and operation on it is cramming needs for data scientist. Two dimensional/row-column representation is facile for imagination and visualization. This paper describes an implementation scheme for higher dimensional array with row-column abstraction on parallel environment like GPU. The representation is just fitting odd dimensions along row-direction and even dimensions along column direction which form groups of 2-D block. Each 2-D block of size blockIdx.x×threadIdx.x is independent of each other. Performance of proposed representation is measured with matrix-matrix addition and multiplication operation. Experimental results show better performance over other representation scheme like Extended Karnaugh Map Representation (EKMR). The scheme can be used for implementing very higher dimensional array in both general purpose and scientific computing on GPU.

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Parallel matricization for n-D array operations

Abstract

Publication series

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Bibliographical note

ASJC Scopus Subject Areas

Keywords

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