article

Free access

Factoring: a method for scheduling parallel loops

Authors:

Susan Flynn Hummel,

Edith Schonberg,

Lawrence E. FlynnAuthors Info & Claims

Communications of the ACM, Volume 35, Issue 8

Pages 90 - 101

https://doi.org/10.1145/135226.135232

Published: 01 August 1992 Publication History

PDF eReader

References

[1]

Allen, F., Burke, M., Charles, P. Cyti'on. R. and Ferrante, J. An overview of the PTRAN analysis system tor multiprocessing. J, Par. Distrub Camput. 5. 5 (Oct. i988), 617-640.

Digital Library

Google Scholar

[2]

BBN Systems and Technologies Corporation, inside the Buttofly GP1000. 10 Moulton St., Cambridge, Mass. 02238, June 1985.

Google Scholar

[3]

Bryant, R. The RP3 parallel computing envirm.nnent, in Proceedings of the USENIX Supercomputing Workshop, I988, 69-85.

Google Scholar

[4]

Flvnn. L,E. and Hummel, S.F. Scheduling variable-length parallel subtasks. IBM Research Report RC 15492, Feb. 1990.

Google Scholar

[5]

Gottlieb, A., Lubachevsky, B.D. and Rudolph. L. Basic: techniques for the efficient coordinsation of very hrge numkers of cooperating se quential prtessors. ACM Trans. Prog. Lang.st.5, 2 (Apr. I983), 164-i89.

Digital Library

Google Scholar

[6]

Gumbel, E.J. The maxima of the mean of she largest value of the range. Ann. Math. Statist. 25 (1954), 76-84.

Crossref

Google Scholar

[7]

Hart!ey, H.O. and Dahd, H.A. Uni-. versal bounds fbr mean range and extrema observations. Ann. Math. Smti, st. 25' (1954), 85-99.

Google Scholar

[8]

Hummel, S.F. and Schonlxrg, E. Low-overhead heduling of nested parallelism. IBM Jour. of Res. D'. (Nov, 1991),

Digital Library

Google Scholar

[9]

Kimelma:n, D. and Ngo, T. The RP3 program visualization environment. IBM Jour. Res Dev. (Nov. 199 I).

Digital Library

Google Scholar

[10]

Kruskal, C. and Weiss, A, Aikcating independent subtasks on parallel processors, IEEE TYam'. Softw. Eng., SE-I1, I0 (Oct. 1985):,

Digital Library

Google Scholar

[11]

Pfister, G.F. et al. The IBM research, parallel processor prototype (RP3): lntroduction and architecture, in Proceedings of the International Conference on Parallel Procesing, 19t45, pp. 764-771.

Google Scholar

[12]

Polychronopoulos, C. Loop coalescing: A compiler transfi)rmation for parallel machine.s, In Proceedings of the International confernce on Parallel Processing, 1987, pp. 235-242.

Google Scholar

[13]

Polychronopoulos, C. and Kuck, D. Guided self-scheduling: A practical schedutiing scheme for parallel computers. IEEE Trans. CompuL C-36, 12 (Dec. 1987)

Digital Library

Google Scholar

[14]

Tzen, T.H. and Ni, L.M. Dynamic loop scheduling for shared-memory multiprocessors. In Proceeding, lnternational Confere.nr.e on Parallel Processing, Vo.I. II, (1991), pp. 247-250.

Google Scholar

[15]

Sarkar, V. Determining average program execution times and their variance/ In Proceedings of the SIGPLAN Conference on Programming Language Design and Implementation, July 1989, pp. 298-312.

Digital Library

Google Scholar

Cited By

View all

Booth JLane P(2024)A NUMA-Aware Version of an Adaptive Self-Scheduling Loop SchedulerACM Transactions on Architecture and Code Optimization10.1145/368054921:4(1-22)Online publication date: 26-Jul-2024
https://dl.acm.org/doi/10.1145/3680549
Mantelos SDimakopoulos V(2023)Tagging for Flexible Loop Scheduling in OpenMP2023 22nd International Symposium on Parallel and Distributed Computing (ISPDC)10.1109/ISPDC59212.2023.00014(23-30)Online publication date: Jul-2023
https://doi.org/10.1109/ISPDC59212.2023.00014
Eleliemy ACiorba F(2023)DaphneSched: A Scheduler for Integrated Data Analysis Pipelines2023 22nd International Symposium on Parallel and Distributed Computing (ISPDC)10.1109/ISPDC59212.2023.00013(53-60)Online publication date: Jul-2023
https://doi.org/10.1109/ISPDC59212.2023.00013
Show More Cited By

Index Terms

Factoring: a method for scheduling parallel loops

Recommendations

Understanding Parallelization Tradeoffs for Linear Pipelines
PMAM'18: Proceedings of the 9th International Workshop on Programming Models and Applications for Multicores and Manycores

Pipelining techniques execute some loops with cross-iteration dependences in parallel, by partitioning the loop body into a sequence of stages such that the data dependences are not violated. Obtaining good performance for all kinds of loops is ...
A performance-based parallel loop scheduling on grid environments
Abstract
The effectiveness of loop self-scheduling schemes has been shown on traditional multiprocessors in the past and computing clusters in the recent years. However, parallel loop scheduling has not been widely applied to computing grids, which are ...
A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates

Flexible job shop scheduling problem (FJSSP) is generalization of job shop scheduling problem (JSSP), in which an operation may be processed on more than one machine each of which has the same function. Most previous researches on FJSSP assumed that all ...

Comments

Information & Contributors

Information

Published In

Communications of the ACM Volume 35, Issue 8

Aug. 1992

116 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/135226

Editor:
Peter J. Denning
NASA Ames Research Center, Moffett Field, CA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 1992

Published in CACM Volume 35, Issue 8

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

282
Total Citations
View Citations
1,478
Total Downloads

Downloads (Last 12 months)146
Downloads (Last 6 weeks)23

Reflects downloads up to 19 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Booth JLane P(2024)A NUMA-Aware Version of an Adaptive Self-Scheduling Loop SchedulerACM Transactions on Architecture and Code Optimization10.1145/368054921:4(1-22)Online publication date: 26-Jul-2024
https://dl.acm.org/doi/10.1145/3680549
Mantelos SDimakopoulos V(2023)Tagging for Flexible Loop Scheduling in OpenMP2023 22nd International Symposium on Parallel and Distributed Computing (ISPDC)10.1109/ISPDC59212.2023.00014(23-30)Online publication date: Jul-2023
https://doi.org/10.1109/ISPDC59212.2023.00014
Eleliemy ACiorba F(2023)DaphneSched: A Scheduler for Integrated Data Analysis Pipelines2023 22nd International Symposium on Parallel and Distributed Computing (ISPDC)10.1109/ISPDC59212.2023.00013(53-60)Online publication date: Jul-2023
https://doi.org/10.1109/ISPDC59212.2023.00013
Barcelo AQueralt ACortes T(2023)Enhancing iteration performance on distributed task-based workflowsFuture Generation Computer Systems10.1016/j.future.2023.07.032149(359-375)Online publication date: Dec-2023
https://doi.org/10.1016/j.future.2023.07.032
Kyriakopoulos CGallopoulos EVenetis I(2023)Hierarchical dynamic workload scheduling on heterogeneous clusters for grid search of inverse problemsThe Journal of Supercomputing10.1007/s11227-023-05306-z79:15(16720-16772)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1007/s11227-023-05306-z
Cañizares PNúñez AFilgueira Rde Lara J(2023)Parallel mutation testing for large scale systemsCluster Computing10.1007/s10586-023-04074-y27:2(2071-2097)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04074-y
Estebanez ALlanos DOrden DPalop B(2022)On the choice of the best chunk size for the speculative execution of loopsPLOS ONE10.1371/journal.pone.026760217:5(e0267602)Online publication date: 17-May-2022
https://doi.org/10.1371/journal.pone.0267602
Mohammed AKorndorfer JEleliemy ACiorba F(2022)Automated Scheduling Algorithm Selection and Chunk Parameter Calculation in OpenMPIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.318927033:12(4383-4394)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TPDS.2022.3189270
Korndorfer JEleliemy AMohammed ACiorba F(2022)LB4OMP: A Dynamic Load Balancing Library for Multithreaded ApplicationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.310777533:4(830-841)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TPDS.2021.3107775
Delamaro MAndrade Sde Souza Sde Souza P(2021)Parallel Execution of Programs as a Support for Mutation Testing: A Replication StudyInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402150012131:03(337-380)Online publication date: 31-Mar-2021
https://doi.org/10.1142/S0218194021500121
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

References

Cited By

Index Terms

Recommendations

Understanding Parallelization Tradeoffs for Linear Pipelines

A performance-based parallel loop scheduling on grid environments

A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Login options

Full Access

Share

Share this Publication link

Share on social media

Affiliations