tutorial

Palirria: Accurate On-line Parallelism Estimation for Adaptive Work-Stealing

Authors:

Georgios Varisteas,

Mats BrorssonAuthors Info & Claims

PMAM'14: Proceedings of Programming Models and Applications on Multicores and Manycores

Pages 120 - 131

https://doi.org/10.1145/2578948.2560687

Published: 07 February 2014 Publication History

Abstract

We present Palirria, a self-adapting work-stealing scheduling method for nested fork/join parallelism that can be used to estimate the number of utilizable workers and self-adapt accordingly. The estimation mechanism is optimized for accuracy, minimizing the requested resources without degrading performance. We implemented Palirria for both the Linux and Barrelfish operating systems and evaluated it on two platforms: a 48-core NUMA multiprocessor and a simulated 32-core system. Compared to state-of-the-art, we observed higher accuracy in estimating resource requirements. This leads to improved resource utilization and performance on par or better to executing with fixed resource allotments.

References

[1]

Adapteva Inc. Epiphany Architecture Reference (G3), 2011.

[2]

K. Agrawal, Y. He, W. J. Hsu, and C. Leiserson. Adaptive scheduling with parallelism feedback. In Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming - PPoPP '06, pages 100--109, New York, New York, USA, 2006. ACM Press.

Digital Library

[3]

K. Agrawal, C. Leiserson, Y. He, and W. J. Hsu. Adaptive work-stealing with parallelism feedback. ACM Transactions on Computer Systems, 26(3):1--32, Sept. 2008.

Digital Library

[4]

N. S. Arora, R. D. Blumofe, and C. G. Plaxton. Thread Scheduling for Multiprogrammed Multiprocessors. Theory of Computing Systems, 34(2):115--144, Jan. 2001.

[5]

G. Banga, P. Druschel, and J. Mogul. Resource containers: A new facility for resource management in server systems. In OSDI '99 Proceedings of the third symposium on Operating systems design and implementation, pages 45--58. USENIX Association, 1998.

Digital Library

[6]

A. Baumann, P. Barham, P. Dagand, T. Harris, R. Isaacs, S. Peter, T. Roscoe, A. Schupbach, and A. Singhania. The multikernel: a new OS architecture for scalable multicore systems. In Proceedings of the ACM SIGOPS 22nd symposium on Operating Systems Principles, pages 29--44. ACM, 2009.

Digital Library

[7]

A. Baumann, S. Peter, A. Schupbach, A. Singhania, T. Roscoe, P. Barham, and R. Isaacs. Your computer is already a distributed system. why isn't your OS? In Proceedings of the 12th conference on Hot topics in operating systems, page 12. USENIX Association, 2009.

Digital Library

[8]

R. Blumofe and C. Leiserson. Scheduling multithreaded computations by work stealing. In 35th Annual Symposium on Foundations of Computer Science, 1994 Proceedings., pages 356--368. IEEE Comput. Soc. Press, 1994.

Digital Library

[9]

Y. Cao, H. Sun, D. Qian, and W. Wu. Stable Adaptive Work-Stealing for Concurrent Multi-core Runtime Systems. In 2011 IEEE International Conference on High Performance Computing and Communications, pages 108--115. Ieee, Sept. 2011.

Digital Library

[10]

Q. Chen, M. Guo, and Z. Huang. CATS: cache aware task-stealing based on online profiling in multi-socket multi-core architectures. In ICS '12 Proceedings of the 26th ACM international conference on Supercomputing, pages 163--172, 2012.

Digital Library

[11]

Q. Chen, Z. Huang, M. Guo, and J. Zhou. CAB: Cache Aware Bi-tier Task-Stealing in Multi-socket Multi-core Architecture. In 2011 International Conference on Parallel Processing, pages 722--732. Ieee, Sept. 2011.

Digital Library

[12]

J. Colmenares, S. Bird, and H. Cook. Resource management in the tessellation manycore os. In HotPar10. USENIX Association, 2010.

[13]

P. C. Diniz and M. C. Rinard. Dynamic feedback: An effective technique for adaptive computing. ACM SIGPLAN Notices, 32(5):71--84, 1997.

Digital Library

[14]

A. Duran, X. Teruel, and R. Ferrer. Barcelona OpenMP tasks suite: A set of benchmarks targeting the exploitation of task parallelism in OpenMP. In ICPP '09 Proceedings of the 2009 International Conference on Parallel Processing, pages 124--131. IEEE Computer Society Press, 2009.

Digital Library

[15]

G. Edjlali, G. Agrawal, A. Sussman, J. Humphries, and J. Saltz. Compiler and Runtime Support for Programming in Adaptive parallel environments. Scientific Programming, 6(2):215--227, 1997.

[16]

K. Faxen and J. Ardelius. Manycore work stealing. In Proceedings of the 8th ACM International Conference on Computing Frontiers, page 1, New York, New York, USA, 2011. ACM Press.

Digital Library

[17]

K.-F. Faxen. Efficient Work Stealing for Fine Grained Parallelism. In 2010 39th International Conference on Parallel Processing, pages 313--322. Ieee, Sept. 2010.

Digital Library

[18]

D. Feitelson. Job scheduling in multiprogrammed parallel systems (extended version). IBM Research Report RC19790 (87657) 2nd Revision, 16(1):104--113, May 1997.

[19]

P. Gschwandtner. Performance analysis and benchmarking of the intel scc. In Proceedings of the 2011 IEEE International Conference on Cluster Computing (Cluster 2011), pages 139--149. Ieee, Sept. 2011.

Digital Library

[20]

M. Hall and J. Anderson. Maximizing multiprocessor performance with the SUIF compiler. Computer, 29(12):84--89, 1996.

Digital Library

[21]

M. W. Hall and M. Martonosi. Adaptive parallelism in compiler-parallelized code. Concurrency: Practice and Experience, 10(14):1235--1250, 1998.

[22]

K. Klues, B. Rhoden, and Y. Zhu. Processes and resource management in a scalable many-core OS. In HotPar'10: Proc. 2nd Workshop on Hot Topics in Parallelism. USENIX Association, 2010.

[23]

M. Kulkarni, M. Burtscher, R. Inkulu, K. Pingali, and C. Casçaval. How much parallelism is there in irregular applications? ACM SIGPLAN Notices, 44(4):3, Feb. 2009.

Digital Library

[24]

Tilera LTD. Tile processor architecturetechnology brief {OL}. httt IWWW. tilera, 2007.

[25]

P. S. Magnusson, M. Christensson, J. Eskilson, D. Forsgren, G. Hå llberg, J. Högberg, F. Larsson, A. Moestedt, and B. Werner. Simics: A Full System Simulation Platform. Computer, 35(2):50--58, Feb. 2002.

Digital Library

[26]

M. McCool, J. Reinders, and A. Robison. Structured Parallel Programming: Patterns for Efficient Computation. Morgan Kaufmann Publishers Inc, 1 edition, 2012.

Digital Library

[27]

D. A. Padua, R. Eigenmann, J. Hoeflinger, P. Petersen, P. Tu, S. Weatherford, and K. Faigin. Polaris: A new-generation parallelizing compiler for MPPs. In In CSRD Rept. No. 1306. Univ. of Illinois at Urbana-Champaign, pages 1--77, 1993.

[28]

A. Podobas and M. Brorsson. A Comparison of some recent Task-based Parallel Programming Models. In Proceedings of the 3rd Workshop on Programmability Issues for Multi-Core Computers, (MULTIPROG'2010), Jan 2010, Pisa, pages 1--14, Pisa, 2010.

[29]

A. Schupbach, S. Peter, A. Baumann, T. Roscoe, P. Barham, T. Harris, and R. Isaacs. Embracing diversity in the Barrelfish manycore operating system. In Proceedings of the Workshop on Managed Many-Core Systems. Citeseer, 2008.

[30]

S. Sen. Dynamic processor allocation for adaptively parallel work-stealing jobs. PhD thesis, Massachusetts Institute of technology, 2004.

[31]

G. Varisteas and M. Brorsson. Automatic Adaptation of Resources to Workload Requirements in Nested Fork-join Programming Models. Technical report, KTH Royal Institute of Technology, 2013.

[32]

M. Voss and R. Eigenmann. ADAPT: Automated de-coupled adaptive program transformation. In International Conference on Parallel Processing, 2000., pages 163--170. IEEE, 2000.

Digital Library

[33]

D. Wentzlaff and A. Agarwal. Factored operating systems (fos). ACM SIGOPS Operating Systems Review, 43(2):76, Apr. 2009.

Digital Library

Cited By

Finnerty PKamada TOhta C(2021)A self‐adjusting task granularity mechanism for the Java lifeline‐based global load balancer library on many‐core clustersConcurrency and Computation: Practice and Experience10.1002/cpe.622434:2Online publication date: 18-Feb-2021
https://doi.org/10.1002/cpe.6224
Finnerty PKamada TOhta C(2020)Self-adjusting task granularity for Global load balancer library on clusters of many-core processorsProceedings of the Eleventh International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3380536.3380539(1-10)Online publication date: 22-Feb-2020
https://dl.acm.org/doi/10.1145/3380536.3380539

Index Terms

Palirria: Accurate On-line Parallelism Estimation for Adaptive Work-Stealing
1. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        Process management

Recommendations

Palirria: accurate on-line parallelism estimation for adaptive work-stealing

We present Palirria, a self-adapting work-stealing scheduling method for nested fork/join parallelism that can be used to estimate the number of utilizable workers and self-adapt accordingly. The estimation mechanism is optimized for accuracy, ...
Palirria: Accurate On-line Parallelism Estimation for Adaptive Work-Stealing
PMAM'14: Proceedings of Programming Models and Applications on Multicores and Manycores

We present Palirria, a self-adapting work-stealing scheduling method for nested fork/join parallelism that can be used to estimate the number of utilizable workers and self-adapt accordingly. The estimation mechanism is optimized for accuracy, ...
A cluster for CS education in the manycore era
SIGCSE '11: Proceedings of the 42nd ACM technical symposium on Computer science education

Traditional Beowulf clusters have been homogeneous platforms for distributed-memory MIMD parallelism. However, the shift to multicore architectures has made shared-memory MIMD parallelism increasingly important, and inexpensive manycore GPGPUs have ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

PMAM'14: Proceedings of Programming Models and Applications on Multicores and Manycores

February 2014

156 pages

ISBN:9781450326575

DOI:10.1145/2578948

Conference Chairs:
Pavan Balaji
Argonne National Laboratory, USA
,
Minyi Guo
Shanghai Jiao Tong, University, China
,
Zhiyi Huang
University of Otago, New Zealand

Copyright © 2014 ACM.

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 the author(s) 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].

Sponsors

SIGPLAN: ACM Special Interest Group on Programming Languages

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Tutorial
Research
Refereed limited

Conference

PPoPP '14

Sponsor:

SIGPLAN

PPoPP '14: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 15 - 19, 2014

FL, Orlando, USA

Acceptance Rates

Overall Acceptance Rate 53 of 97 submissions, 55%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
121
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Finnerty PKamada TOhta C(2021)A self‐adjusting task granularity mechanism for the Java lifeline‐based global load balancer library on many‐core clustersConcurrency and Computation: Practice and Experience10.1002/cpe.622434:2Online publication date: 18-Feb-2021
https://doi.org/10.1002/cpe.6224
Finnerty PKamada TOhta C(2020)Self-adjusting task granularity for Global load balancer library on clusters of many-core processorsProceedings of the Eleventh International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3380536.3380539(1-10)Online publication date: 22-Feb-2020
https://dl.acm.org/doi/10.1145/3380536.3380539

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten