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Unifying fixed code and fixed data mapping of load-imbalanced pipelined loops

Published: 27 February 2016 Publication History

Abstract

Some loops with cross-iteration dependences can execute in parallel by pipelining. The loop body is partitioned into stages such that the data dependences are not violated and then the stages are mapped onto threads. Two well-known mapping techniques are fixed code and fixed data; they achieve high performance for load-balanced loops, but they fail to perform well for load-imbalanced loops. In this article, we present a novel hybrid mapping that eliminates drawbacks of both prior mapping techniques and enables dynamic scheduling of stages.

References

[1]
C. Bienia and K. Li. Characteristics of Workloads Using the Pipeline Programming Model. Revised selected papers from the 3rd Workshop on Emerging Applications and Many-Core Architecture, held in conjunction with ISCA '10, pages 161--171, 2012.
[2]
M. Kamruzzaman, S. Swanson, and D. M. Tullsen. Load-balanced Pipeline Parallelism. Proc. SC '13, pages 1--12, 2013.
[3]
A. Navarro, R. Asenjo, S. Tabik, and C. Cascaval. Analytical Modeling of Pipeline Parallelism. Proc. PACT '09, pages 281--290, 2009.
[4]
E. Raman, G. Ottoni, A. Raman, M. J. Bridges, and D. I. August. Parallel-Stage Decoupled Software Pipelining. Proc. CGO '08, pages 114--123, 2008.

Cited By

View all
  • (2019)Chunking for Dynamic Linear PipelinesACM Transactions on Architecture and Code Optimization10.1145/336381516:4(1-25)Online publication date: 18-Nov-2019
  • (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
  • (2018)Understanding Parallelization Tradeoffs for Linear PipelinesProceedings of the 9th International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3178442.3178443(1-10)Online publication date: 24-Feb-2018

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Published In

cover image ACM Conferences
PPoPP '16: Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
February 2016
420 pages
ISBN:9781450340922
DOI:10.1145/2851141
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].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 February 2016

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Author Tags

  1. dynamic scheduling
  2. mapping
  3. multi-threading
  4. pipeline model

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  • Research-article

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  • SNF

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PPoPP '16
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Overall Acceptance Rate 230 of 1,014 submissions, 23%

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Cited By

View all
  • (2019)Chunking for Dynamic Linear PipelinesACM Transactions on Architecture and Code Optimization10.1145/336381516:4(1-25)Online publication date: 18-Nov-2019
  • (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
  • (2018)Understanding Parallelization Tradeoffs for Linear PipelinesProceedings of the 9th International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3178442.3178443(1-10)Online publication date: 24-Feb-2018

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