Statistical analysis of a portable parallel non-linear programming algorithm

Jrjung Lyu, A. Gunasekaran

Research output: Contribution to journalArticle

Abstract

It is well known that the availability of cost-effective and powerful parallel computers has enhanced the ability of the operations research community to solve laborious computational problems. But many researchers argue that the lack of portability of parallel algorithms is a major drawback to utilizing parallel computers. This paper studies the performance of a portable parallel unconstrained non-gradient optimization algorithm, when executed in various shared-memory multiprocessor systems, compared with its non-portable code. Analysis of covariance is used to analyse how the algorithm's performance is affected by several factors of interest. The results yield more insights into the parallel computing.

Original languageEnglish
Pages (from-to)253-258
Number of pages6
JournalStatistics and Computing
Volume4
Issue number4
DOIs
Publication statusPublished - 1994 Dec 1

Fingerprint

Parallel Programming
Nonlinear programming
Parallel Computers
Nonlinear Programming
Statistical Analysis
Statistical methods
Analysis of Covariance
Shared-memory multiprocessors
Operations research
Portability
Multiprocessor Systems
Operations Research
Parallel processing systems
Parallel Computing
Parallel algorithms
Parallel Algorithms
Optimization Algorithm
Availability
Data storage equipment
Costs

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

Cite this

@article{eb339775b65f4e38978f80cf60a62cb8,
title = "Statistical analysis of a portable parallel non-linear programming algorithm",
abstract = "It is well known that the availability of cost-effective and powerful parallel computers has enhanced the ability of the operations research community to solve laborious computational problems. But many researchers argue that the lack of portability of parallel algorithms is a major drawback to utilizing parallel computers. This paper studies the performance of a portable parallel unconstrained non-gradient optimization algorithm, when executed in various shared-memory multiprocessor systems, compared with its non-portable code. Analysis of covariance is used to analyse how the algorithm's performance is affected by several factors of interest. The results yield more insights into the parallel computing.",
author = "Jrjung Lyu and A. Gunasekaran",
year = "1994",
month = "12",
day = "1",
doi = "10.1007/BF00156748",
language = "English",
volume = "4",
pages = "253--258",
journal = "Statistics and Computing",
issn = "0960-3174",
publisher = "Springer Netherlands",
number = "4",

}

Statistical analysis of a portable parallel non-linear programming algorithm. / Lyu, Jrjung; Gunasekaran, A.

In: Statistics and Computing, Vol. 4, No. 4, 01.12.1994, p. 253-258.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Statistical analysis of a portable parallel non-linear programming algorithm

AU - Lyu, Jrjung

AU - Gunasekaran, A.

PY - 1994/12/1

Y1 - 1994/12/1

N2 - It is well known that the availability of cost-effective and powerful parallel computers has enhanced the ability of the operations research community to solve laborious computational problems. But many researchers argue that the lack of portability of parallel algorithms is a major drawback to utilizing parallel computers. This paper studies the performance of a portable parallel unconstrained non-gradient optimization algorithm, when executed in various shared-memory multiprocessor systems, compared with its non-portable code. Analysis of covariance is used to analyse how the algorithm's performance is affected by several factors of interest. The results yield more insights into the parallel computing.

AB - It is well known that the availability of cost-effective and powerful parallel computers has enhanced the ability of the operations research community to solve laborious computational problems. But many researchers argue that the lack of portability of parallel algorithms is a major drawback to utilizing parallel computers. This paper studies the performance of a portable parallel unconstrained non-gradient optimization algorithm, when executed in various shared-memory multiprocessor systems, compared with its non-portable code. Analysis of covariance is used to analyse how the algorithm's performance is affected by several factors of interest. The results yield more insights into the parallel computing.

UR - http://www.scopus.com/inward/record.url?scp=34249770111&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34249770111&partnerID=8YFLogxK

U2 - 10.1007/BF00156748

DO - 10.1007/BF00156748

M3 - Article

VL - 4

SP - 253

EP - 258

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 4

ER -