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PLDS: Partitioning linked data structures for parallelism

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Rajiv GuptaAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 8, Issue 4

Article No.: 38, Pages 1 - 21

https://doi.org/10.1145/2086696.2086717

Published: 26 January 2012 Publication History

Abstract

Recently, parallelization of computations in the presence of dynamic data structures has shown promising potential. In this paper, we present PLDS, a system for easily expressing and efficiently exploiting parallelism in computations that are based on dynamic linked data structures. PLDS improves the execution efficiency by providing support for data partitioning and then distributing computation across threads based on the partitioning. Such computations often require the use of speculation to exploit dynamic parallelism. PLDS supports a conditional speculation mechanism that reduces the cost of speculation. PLDS can be employed in the context of different forms of parallelism, which to cover a wide range of parallel applications. PLDS provides easy-to-use compiler directives, using enabling programmers to choose from among a variety of data partitionings to distribute computation across threads in a partitioning-sensitive fashion, and to use conditional speculation when required. We evaluate our implementation of PLDS using ten benchmarks, of which six are parallelized using speculation. PLDS achieves 1.3x--6.9x speedups on an 8-core machine.

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Cited By

Chou YNowicki TAamodt T(2023)Treelet Prefetching For Ray TracingProceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3613424.3614288(742-755)Online publication date: 28-Oct-2023
https://dl.acm.org/doi/10.1145/3613424.3614288
Vasiladiotis CLozano RCole MFranke BLee J(2021)Loop parallelization using dynamic commutativity analysisProceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO51591.2021.9370319(150-161)Online publication date: 27-Feb-2021
https://dl.acm.org/doi/10.1109/CGO51591.2021.9370319
Estebanez ALlanos DGonzalez-Escribano A(2016)A Survey on Thread-Level Speculation TechniquesACM Computing Surveys10.1145/293836949:2(1-39)Online publication date: 30-Jun-2016
https://dl.acm.org/doi/10.1145/2938369
Show More Cited By

Index Terms

PLDS: Partitioning linked data structures for parallelism
1. Computing methodologies
  1. Concurrent computing methodologies
    1. Concurrent programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Concurrent programming languages

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Published In

cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 8, Issue 4

Special Issue on High-Performance Embedded Architectures and Compilers

January 2012

765 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/2086696

Issue’s Table of Contents

Copyright © 2012 ACM.

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Publication History

Published: 26 January 2012

Accepted: 01 November 2011

Revised: 01 October 2011

Received: 01 July 2011

Published in TACO Volume 8, Issue 4

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Cited By

Chou YNowicki TAamodt T(2023)Treelet Prefetching For Ray TracingProceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3613424.3614288(742-755)Online publication date: 28-Oct-2023
https://dl.acm.org/doi/10.1145/3613424.3614288
Vasiladiotis CLozano RCole MFranke BLee J(2021)Loop parallelization using dynamic commutativity analysisProceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO51591.2021.9370319(150-161)Online publication date: 27-Feb-2021
https://dl.acm.org/doi/10.1109/CGO51591.2021.9370319
Estebanez ALlanos DGonzalez-Escribano A(2016)A Survey on Thread-Level Speculation TechniquesACM Computing Surveys10.1145/293836949:2(1-39)Online publication date: 30-Jun-2016
https://dl.acm.org/doi/10.1145/2938369
Cheng LMalik AKotoulas SWard TTheodoropoulos G(2016)Fast Compression of Large Semantic Web Data Using X10IEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2015.249657927:9(2603-2617)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1109/TPDS.2015.2496579

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