research-article

OpenMPIR: Implementing OpenMP Tasks with Tapir

Authors:

William S. Moses,

Stephen L. Olivier,

Patrick McCormickAuthors Info & Claims

LLVM-HPC'17: Proceedings of the Fourth Workshop on the LLVM Compiler Infrastructure in HPC

Article No.: 3, Pages 1 - 12

https://doi.org/10.1145/3148173.3148186

Published: 12 November 2017 Publication History

Abstract

Optimizing compilers for task-level parallelism are still in their infancy. This work explores a compiler front end that translates OpenMP tasking semantics to Tapir, an extension to LLVM IR that represents fork-join parallelism. This enables analyses and optimizations that were previously inaccessible to OpenMP codes, as well as the ability to target additional runtimes at code generation. Using a Cilk runtime back end, we compare results to existing OpenMP implementations. Initial performance results for the Barcelona OpenMP task suite show performance improvements over existing implementations.

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Cited By

Moses WIvanov IDomke JEndo TDoerfert JZinenko ODehnavi MKulkarni MKrishnamoorthy S(2023)High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel ConstructsProceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3572848.3577475(119-134)Online publication date: 25-Feb-2023
https://dl.acm.org/doi/10.1145/3572848.3577475
Ma JWang WNelson ACuevas MHomerding BLiu CHuang ZCampanoni SHale KDinda Pde Supinski BHall MGamblin T(2021)Paths to OpenMP in the kernelProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476183(1-17)Online publication date: 14-Nov-2021
https://dl.acm.org/doi/10.1145/3458817.3476183
Nie KZhou QQian HPang JXu JLi Y(2021)Parallel Region Reconstruction Technique for Sunway High-Performance Multi-core ProcessorsData Science10.1007/978-981-16-5940-9_13(163-179)Online publication date: 10-Sep-2021
https://doi.org/10.1007/978-981-16-5940-9_13
Show More Cited By

OpenMPIR: Implementing OpenMP Tasks with Tapir
1. Computing methodologies
  1. Parallel computing methodologies
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types

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cover image ACM Conferences

LLVM-HPC'17: Proceedings of the Fourth Workshop on the LLVM Compiler Infrastructure in HPC

November 2017

106 pages

ISBN:9781450355650

DOI:10.1145/3148173

Copyright © 2017 ACM.

© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 12 November 2017

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SC '17: The International Conference for High Performance Computing, Networking, Storage and Analysis

November 12 - 17, 2017

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LLVM-HPC'17 Paper Acceptance Rate 9 of 10 submissions, 90%;

Overall Acceptance Rate 16 of 22 submissions, 73%

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Cited By

Moses WIvanov IDomke JEndo TDoerfert JZinenko ODehnavi MKulkarni MKrishnamoorthy S(2023)High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel ConstructsProceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3572848.3577475(119-134)Online publication date: 25-Feb-2023
https://dl.acm.org/doi/10.1145/3572848.3577475
Ma JWang WNelson ACuevas MHomerding BLiu CHuang ZCampanoni SHale KDinda Pde Supinski BHall MGamblin T(2021)Paths to OpenMP in the kernelProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476183(1-17)Online publication date: 14-Nov-2021
https://dl.acm.org/doi/10.1145/3458817.3476183
Nie KZhou QQian HPang JXu JLi Y(2021)Parallel Region Reconstruction Technique for Sunway High-Performance Multi-core ProcessorsData Science10.1007/978-981-16-5940-9_13(163-179)Online publication date: 10-Sep-2021
https://doi.org/10.1007/978-981-16-5940-9_13
Ghosh SCuevas MCampanoni SDinda PCuicchi CQualters IKramer W(2020)Compiler-based timing for extremely fine-grain preemptive parallelismProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.5555/3433701.3433771(1-15)Online publication date: 9-Nov-2020
https://dl.acm.org/doi/10.5555/3433701.3433771
Ghosh SCuevas MCampanoni SDinda P(2020)Compiler-Based Timing For Extremely Fine-Grain Preemptive ParallelismSC20: International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC41405.2020.00057(1-15)Online publication date: Nov-2020
https://doi.org/10.1109/SC41405.2020.00057
Schardl TMoses WLeiserson C(2019)TapirACM Transactions on Parallel Computing10.1145/33656556:4(1-33)Online publication date: 17-Dec-2019
https://dl.acm.org/doi/10.1145/3365655
Doerfert JFinkel H(2019)Compiler Optimizations for Parallel ProgramsLanguages and Compilers for Parallel Computing10.1007/978-3-030-34627-0_9(112-119)Online publication date: 13-Nov-2019
https://doi.org/10.1007/978-3-030-34627-0_9

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