A Scalable Low Discrepancy Point Generator for Parallel Computing

Kwong Ip Liu; Fred J. Hickernell

doi:10.1007/978-3-540-30566-8_31

A Scalable Low Discrepancy Point Generator for Parallel Computing

Kwong Ip Liu^*, Fred J. Hickernell

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

Abstract

The Monte Carlo (MC) method is a simple but effective way to perform simulations involving complicated or multivariate functions. The Quasi-Monte Carlo (QMC) method is similar but replaces independent and identically distributed (i.i.d.) random points by low discrepancy points. Low discrepancy points are regularly distributed points that may be deterministic or randomized. The digital net is a kind of low discrepancy point set that is generated by number theoretical methods. A software library for low discrepancy point generation has been developed. It is thread-safe and supports MPI for parallel computation. A numerical example from physics is shown.

Original language	English
Title of host publication	Parallel and Distributed Processing and Applications
Subtitle of host publication	Second International Symposium, ISPA 2004, Hong Kong, China, December 13-15, 2004, Proceedings
Editors	Jiannong Cao, Laurence T. Yang, Minyi Guo, Francis Lau
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	257-262
Number of pages	6
Edition	1st
ISBN (Electronic)	9783540305668
ISBN (Print)	9783540241287
DOIs	https://doi.org/10.1007/978-3-540-30566-8_31
Publication status	Published - 2 Dec 2004
Event	2nd International Symposium on Parallel and Distributed Processing and Applications, ISPA 2004 - , Hong Kong Duration: 13 Dec 2004 → 15 Dec 2004 https://link.springer.com/book/10.1007/b104574

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	3358
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	ISPA: International Symposium on Parallel and Distributed Processing and Applications

Conference

Conference	2nd International Symposium on Parallel and Distributed Processing and Applications, ISPA 2004
Country/Territory	Hong Kong
Period	13/12/04 → 15/12/04
Internet address	https://link.springer.com/book/10.1007/b104574

User-Defined Keywords

Digital nets
Monte Carlo and Quasi-Monte Carlo methods
Parallel programming
Software library

Access to Document

10.1007/978-3-540-30566-8_31

Cite this

Liu, K. I., & Hickernell, F. J. (2004). A Scalable Low Discrepancy Point Generator for Parallel Computing. In J. Cao, L. T. Yang, M. Guo, & F. Lau (Eds.), Parallel and Distributed Processing and Applications: Second International Symposium, ISPA 2004, Hong Kong, China, December 13-15, 2004, Proceedings (1st ed., pp. 257-262). (Lecture Notes in Computer Science; Vol. 3358), (ISPA: International Symposium on Parallel and Distributed Processing and Applications). Springer. https://doi.org/10.1007/978-3-540-30566-8_31

Liu, Kwong Ip ; Hickernell, Fred J. / A Scalable Low Discrepancy Point Generator for Parallel Computing. Parallel and Distributed Processing and Applications: Second International Symposium, ISPA 2004, Hong Kong, China, December 13-15, 2004, Proceedings. editor / Jiannong Cao ; Laurence T. Yang ; Minyi Guo ; Francis Lau. 1st. ed. Berlin, Heidelberg : Springer, 2004. pp. 257-262 (Lecture Notes in Computer Science). (ISPA: International Symposium on Parallel and Distributed Processing and Applications).

@inproceedings{5037b764128d4ba6973e26907e65ad0c,

title = "A Scalable Low Discrepancy Point Generator for Parallel Computing",

abstract = "The Monte Carlo (MC) method is a simple but effective way to perform simulations involving complicated or multivariate functions. The Quasi-Monte Carlo (QMC) method is similar but replaces independent and identically distributed (i.i.d.) random points by low discrepancy points. Low discrepancy points are regularly distributed points that may be deterministic or randomized. The digital net is a kind of low discrepancy point set that is generated by number theoretical methods. A software library for low discrepancy point generation has been developed. It is thread-safe and supports MPI for parallel computation. A numerical example from physics is shown.",

keywords = "Digital nets, Monte Carlo and Quasi-Monte Carlo methods, Parallel programming, Software library",

author = "Liu, {Kwong Ip} and Hickernell, {Fred J.}",

note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2nd International Symposium on Parallel and Distributed Processing and Applications, ISPA 2004 ; Conference date: 13-12-2004 Through 15-12-2004",

year = "2004",

month = dec,

day = "2",

doi = "10.1007/978-3-540-30566-8_31",

language = "English",

isbn = "9783540241287",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "257--262",

editor = "Jiannong Cao and Yang, {Laurence T.} and Minyi Guo and Francis Lau",

booktitle = "Parallel and Distributed Processing and Applications",

edition = "1st",

url = "https://link.springer.com/book/10.1007/b104574",

}

Liu, KI & Hickernell, FJ 2004, A Scalable Low Discrepancy Point Generator for Parallel Computing. in J Cao, LT Yang, M Guo & F Lau (eds), Parallel and Distributed Processing and Applications: Second International Symposium, ISPA 2004, Hong Kong, China, December 13-15, 2004, Proceedings. 1st edn, Lecture Notes in Computer Science, vol. 3358, ISPA: International Symposium on Parallel and Distributed Processing and Applications, Springer, Berlin, Heidelberg, pp. 257-262, 2nd International Symposium on Parallel and Distributed Processing and Applications, ISPA 2004, Hong Kong, 13/12/04. https://doi.org/10.1007/978-3-540-30566-8_31

A Scalable Low Discrepancy Point Generator for Parallel Computing. / Liu, Kwong Ip; Hickernell, Fred J.
Parallel and Distributed Processing and Applications: Second International Symposium, ISPA 2004, Hong Kong, China, December 13-15, 2004, Proceedings. ed. / Jiannong Cao; Laurence T. Yang; Minyi Guo; Francis Lau. 1st. ed. Berlin, Heidelberg: Springer, 2004. p. 257-262 (Lecture Notes in Computer Science; Vol. 3358), (ISPA: International Symposium on Parallel and Distributed Processing and Applications).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - A Scalable Low Discrepancy Point Generator for Parallel Computing

AU - Liu, Kwong Ip

AU - Hickernell, Fred J.

PY - 2004/12/2

Y1 - 2004/12/2

N2 - The Monte Carlo (MC) method is a simple but effective way to perform simulations involving complicated or multivariate functions. The Quasi-Monte Carlo (QMC) method is similar but replaces independent and identically distributed (i.i.d.) random points by low discrepancy points. Low discrepancy points are regularly distributed points that may be deterministic or randomized. The digital net is a kind of low discrepancy point set that is generated by number theoretical methods. A software library for low discrepancy point generation has been developed. It is thread-safe and supports MPI for parallel computation. A numerical example from physics is shown.

AB - The Monte Carlo (MC) method is a simple but effective way to perform simulations involving complicated or multivariate functions. The Quasi-Monte Carlo (QMC) method is similar but replaces independent and identically distributed (i.i.d.) random points by low discrepancy points. Low discrepancy points are regularly distributed points that may be deterministic or randomized. The digital net is a kind of low discrepancy point set that is generated by number theoretical methods. A software library for low discrepancy point generation has been developed. It is thread-safe and supports MPI for parallel computation. A numerical example from physics is shown.

KW - Digital nets

KW - Monte Carlo and Quasi-Monte Carlo methods

KW - Parallel programming

KW - Software library

UR - http://www.scopus.com/inward/record.url?scp=35048817418&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-30566-8_31

DO - 10.1007/978-3-540-30566-8_31

M3 - Conference proceeding

AN - SCOPUS:35048817418

SN - 9783540241287

T3 - Lecture Notes in Computer Science

SP - 257

EP - 262

BT - Parallel and Distributed Processing and Applications

A2 - Cao, Jiannong

A2 - Yang, Laurence T.

A2 - Guo, Minyi

A2 - Lau, Francis

PB - Springer

CY - Berlin, Heidelberg

T2 - 2nd International Symposium on Parallel and Distributed Processing and Applications, ISPA 2004

Y2 - 13 December 2004 through 15 December 2004

ER -

Liu KI, Hickernell FJ. A Scalable Low Discrepancy Point Generator for Parallel Computing. In Cao J, Yang LT, Guo M, Lau F, editors, Parallel and Distributed Processing and Applications: Second International Symposium, ISPA 2004, Hong Kong, China, December 13-15, 2004, Proceedings. 1st ed. Berlin, Heidelberg: Springer. 2004. p. 257-262. (Lecture Notes in Computer Science). (ISPA: International Symposium on Parallel and Distributed Processing and Applications). doi: 10.1007/978-3-540-30566-8_31

A Scalable Low Discrepancy Point Generator for Parallel Computing

Abstract

Publication series

Conference

User-Defined Keywords

Access to Document

Other files and links

Fingerprint

Cite this