research-article

Expressing pipeline parallelism using TBB constructs: a case study on what works and what doesn't

Authors:

Ralph E. JohnsonAuthors Info & Claims

SPLASH '11 Workshops: Proceedings of the compilation of the co-located workshops on DSM'11, TMC'11, AGERE! 2011, AOOPES'11, NEAT'11, & VMIL'11

Pages 133 - 138

https://doi.org/10.1145/2095050.2095074

Published: 23 October 2011 Publication History

Abstract

Task-based libraries such as Intel's Threading Building Blocks (TBB) provide higher levels of abstraction than threads for parallel programming. Work remains, however, to determine how straightforward it is to use these libraries to express various patterns of parallelism. This case study focuses on a particular pattern: pipeline parallelism. We attempted to transform three representative pipeline applications - content-based image retrieval, compression and video encoding - to use the pipeline constructs in TBB. We successfully converted two of the three applications. In the successful cases we discuss our transformation process and contrast the expressivity and performance of our implementations to existing Pthreads versions; in the unsuccessful case, we detail what the challenges were and propose possible solutions.

References

[1]

Intel Threading Building Blocks. http://www.threadingbuildingblocks.org/.

[2]

TPL Dataflow. http://msdn.microsoft.com/en-us/devlabs/gg585582.

[3]

x264. http://www.videolan.org/developers/x264.html.

[4]

C. Bienia, S. Kumar, J. P. Singh, and K. Li. The parsec benchmark suite: Characterization and architectural implications. Technical Report TR-811-08, Princeton University, January 2008.

Digital Library

[5]

I. Buck, T. Foley, D. Horn, J. Sugerman, K. Fatahalian, M. Houston, and P. Hanrahan. Brook for gpus: stream computing on graphics hardware. In SIGGRAPH '04.

Digital Library

[6]

A. J. Dios, R. Asenjo, A. Navarro, F. Corbera, and E. L. Zapata. Wavefront template implementation based on the task programming model. Technical report, University of Malaga, 2011.

[7]

H. Hoffman, A. Agarwal, and S. Devadas. Partitioning Strategies: Spatiotemporal Patterns of Program Decomposition. In ICPADS '10.

[8]

U. J. Kapasi, S. Rixner, W. J. Dally, B. Khailany, J. H. Ahn, P. Mattson, and J. D. Owens. Programmable stream processors. Computer, 36: 54--62, August 2003.

Digital Library

[9]

E. A. Lee. The Problem with Threads. Computer, 39: 33--42, May 2006.

Digital Library

[10]

D. Leijen, W. Schulte, and S. Burckhardt. The Design of a Task Parallel Library. In OOPSLA '09.

Digital Library

[11]

Q. Lv, W. Josephson, Z. Wang, M. Charikar, and K. Li. Ferret: a toolkit for content-based similarity search of feature-rich data. In EuroSys '06.

Digital Library

[12]

S. MacDonald, D. Szafron, and J. Schaeffer. Rethinking the Pipeline as Object-oriented States with Transformations. In HIPS '04.

[13]

T. G. Mattson, B. A. Sanders, and B. L. Massingill. Patterns for Parallel Programming. Addison-Wesley Professional, 2004.

Digital Library

[14]

A. Navarro, R. Asenjo, S. Tabik, and C. Cascaval. Analytical Modeling of Pipeline Parallelism. In PACT '09.

Digital Library

[15]

I. E. Richardson. The H.264 Advanced Video Compression Standard. Wiley, 2010.

Digital Library

[16]

S. Rul, H. Vandierendonck, and K. De Bosschere. A profile-based tool for finding pipeline parallelism in sequential programs. Parallel Comput., 36: 531--551, September 2010.

Digital Library

[17]

W. Thies and S. Amarasinghe. An empirical characterization of stream programs and its implications for language and compiler design. In PACT '10.

Digital Library

[18]

W. Thies, V. Chandrasekhar, and S. Amarasinghe. A Practical Approach to Exploiting Coarse-Grained Pipeline Parallelism in C Programs. In MICRO '07.

Digital Library

[19]

W. Thies, M. Karczmarek, and S. P. Amarasinghe. StreamIt: A Language for Streaming Applications. In CC '02. Springer-Verlag.

Digital Library

Cited By

Weber MJin BLederman GShoukry YLee ESeshia SSangiovanni-Vincentelli A(2020)GordianACM Transactions on Cyber-Physical Systems10.1145/33865684:4(1-27)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1145/3386568
Palmer DBommes DSolomon J(2020)Algebraic Representations for Volumetric Frame FieldsACM Transactions on Graphics10.1145/336678639:2(1-17)Online publication date: 5-Apr-2020
https://dl.acm.org/doi/10.1145/3366786
Andelfinger PXu YEckhoff DCai WKnoll A(2020)Fidelity and Performance of State Fast-forwarding in Microscopic Traffic SimulationsACM Transactions on Modeling and Computer Simulation10.1145/336601930:2(1-26)Online publication date: 10-Apr-2020
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Index Terms

Expressing pipeline parallelism using TBB constructs: a case study on what works and what doesn't
1. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Patterns
  2. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Client-server architectures
      2. Software architectures

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cover image ACM Conferences

SPLASH '11 Workshops: Proceedings of the compilation of the co-located workshops on DSM'11, TMC'11, AGERE! 2011, AOOPES'11, NEAT'11, & VMIL'11

October 2011

358 pages

ISBN:9781450311830

DOI:10.1145/2095050

General Chair:
Crista Videira Lopes
University of California, Irvine

Copyright © 2011 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 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]

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Publication History

Published: 23 October 2011

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SPLASH '11: Conference on Systems, Programming, and Applications: Software for Humanity

October 23 - 24, 2011

Oregon, Portland, USA

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Cited By

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https://dl.acm.org/doi/10.1145/3386568
Palmer DBommes DSolomon J(2020)Algebraic Representations for Volumetric Frame FieldsACM Transactions on Graphics10.1145/336678639:2(1-17)Online publication date: 5-Apr-2020
https://dl.acm.org/doi/10.1145/3366786
Andelfinger PXu YEckhoff DCai WKnoll A(2020)Fidelity and Performance of State Fast-forwarding in Microscopic Traffic SimulationsACM Transactions on Modeling and Computer Simulation10.1145/336601930:2(1-26)Online publication date: 10-Apr-2020
https://dl.acm.org/doi/10.1145/3366019
Rahman AKemper P(2020)Simulation Study to Identify the Characteristics of Markov Chain PropertiesACM Transactions on Modeling and Computer Simulation10.1145/336174430:2(1-26)Online publication date: 20-Mar-2020
https://dl.acm.org/doi/10.1145/3361744
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Mastoras AGross T(2019)Efficient and Scalable Execution of Fine-Grained Dynamic Linear PipelinesACM Transactions on Architecture and Code Optimization10.1145/330741116:2(1-26)Online publication date: 18-Apr-2019
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Griebler DHoffmann RDanelutto MFernandes L(2019)High-Level and Productive Stream Parallelism for Dedup, Ferret, and Bzip2International Journal of Parallel Programming10.1007/s10766-018-0558-x47:2(253-271)Online publication date: 1-Apr-2019
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Majo ZGross T(2017)A Library for Portable and Composable Data Locality Optimizations for NUMA SystemsACM Transactions on Parallel Computing10.1145/30402223:4(1-32)Online publication date: 2-Mar-2017
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