research-article

Automatic Kernel Fusion for Image Processing DSLs

Authors:

Jürgen TeichAuthors Info & Claims

SCOPES '18: Proceedings of the 21st International Workshop on Software and Compilers for Embedded Systems

Pages 76 - 85

https://doi.org/10.1145/3207719.3207723

Published: 28 May 2018 Publication History

Abstract

Programming image processing algorithms on hardware accelerators such as graphics processing units (GPUs) often exhibits a trade-off between software portability and performance portability. Domain-specific languages (DSLs) have proven to be a promising remedy, which enable optimizations and generation of efficient code from a concise, high-level algorithm representation.

The scope of this paper is an optimization framework for image processing DSLs in the form of a source-to-source compiler. To cope with the inter-kernel communication bound via global memory for GPU applications, kernel fusion is investigated as a primary optimization technique to improve temporal locality. In order to enable automatic kernel fusion, we analyze the fusibility of each kernel in the algorithm, in terms of data dependencies, resource utilization, and parallelism granularity. By combining the obtained information with the domain-specific knowledge captured in the DSL, a method to automatically fuse the suitable kernels is proposed and integrated into an open source DSL framework. The novel kernel fusion technique is evaluated on two filter-based image processing applications, for which speedups of up to 1.60 are obtained for an NVIDIA Geforce 745 graphics card target.

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Cited By

Bastidas Fuertes APérez MMeza Hormaza J(2023)Transpilers: A Systematic Mapping Review of Their Usage in Research and IndustryApplied Sciences10.3390/app1306366713:6(3667)Online publication date: 13-Mar-2023
https://doi.org/10.3390/app13063667
Jiang LXu PZhu QLi XYan SZhang XLin DMa WLi ZLiu JMa JJin MYang C(2022)EasyView: Enabling and Scheduling Tensor Views in Deep Learning CompilersProceedings of the 51st International Conference on Parallel Processing10.1145/3545008.3545037(1-11)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1145/3545008.3545037
Fukuhara JTakimoto MGrosser TLee K(2022)Automated kernel fusion for GPU based on code motionProceedings of the 23rd ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems10.1145/3519941.3535078(151-161)Online publication date: 14-Jun-2022
https://dl.acm.org/doi/10.1145/3519941.3535078
Show More Cited By

Index Terms

Automatic Kernel Fusion for Image Processing DSLs
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Graphics processors
    2. Image manipulation
      1. Image processing
2. Software and its engineering
  1. Software notations and tools
    1. Context specific languages
      1. Domain specific languages

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cover image ACM Other conferences

SCOPES '18: Proceedings of the 21st International Workshop on Software and Compilers for Embedded Systems

May 2018

120 pages

ISBN:9781450357807

DOI:10.1145/3207719

Copyright © 2018 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].

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EDAA: European Design Automation Association
SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 May 2018

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SCOPES '18

SCOPES '18: 21st International Workshop on Software and Compilers for Embedded Systems

May 28 - 30, 2018

Sankt Goar, Germany

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Overall Acceptance Rate 38 of 79 submissions, 48%

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Cited By

Bastidas Fuertes APérez MMeza Hormaza J(2023)Transpilers: A Systematic Mapping Review of Their Usage in Research and IndustryApplied Sciences10.3390/app1306366713:6(3667)Online publication date: 13-Mar-2023
https://doi.org/10.3390/app13063667
Jiang LXu PZhu QLi XYan SZhang XLin DMa WLi ZLiu JMa JJin MYang C(2022)EasyView: Enabling and Scheduling Tensor Views in Deep Learning CompilersProceedings of the 51st International Conference on Parallel Processing10.1145/3545008.3545037(1-11)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1145/3545008.3545037
Fukuhara JTakimoto MGrosser TLee K(2022)Automated kernel fusion for GPU based on code motionProceedings of the 23rd ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems10.1145/3519941.3535078(151-161)Online publication date: 14-Jun-2022
https://dl.acm.org/doi/10.1145/3519941.3535078
Zheng ZYang XZhao PLong GZhu KZhu FZhao WLiu XYang JZhai JSong SLin WFalsafi BFerdman MLu SWenisch T(2022)AStitch: enabling a new multi-dimensional optimization space for memory-intensive ML training and inference on modern SIMT architecturesProceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3503222.3507723(359-373)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3503222.3507723
Sioutas SStuijk SBasten TCorporaal HSomers L(2020)Schedule Synthesis for Halide Pipelines on GPUsACM Transactions on Architecture and Code Optimization10.1145/340611717:3(1-25)Online publication date: 3-Aug-2020
https://dl.acm.org/doi/10.1145/3406117
Qiao BReiche OÖzkan MTeich JHannig FCorporaal H(2020)Efficient parallel reduction on GPUs with HipaccProceedings of the 23th International Workshop on Software and Compilers for Embedded Systems10.1145/3378678.3391885(58-61)Online publication date: 25-May-2020
https://dl.acm.org/doi/10.1145/3378678.3391885
Qiao BReiche OTeich JHannig FJog AKayiran OPattnaik A(2020)Unveiling kernel concurrency in multiresolution filters on GPUs with an image processing DSLProceedings of the 13th Annual Workshop on General Purpose Processing using Graphics Processing Unit10.1145/3366428.3380773(11-20)Online publication date: 23-Feb-2020
https://dl.acm.org/doi/10.1145/3366428.3380773
Kessler CLitzinger SKeller J(2020)Static Scheduling of Moldable Streaming Tasks with Task Fusion for Parallel Systems with DVFSIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.3013054(1-1)Online publication date: 2020
https://doi.org/10.1109/TCAD.2020.3013054
Liu ZLeng JChen QLi CZheng WLi LGuo M(2020)DLFusion: An Auto-Tuning Compiler for Layer Fusion on Deep Neural Network Accelerator2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00041(118-127)Online publication date: Dec-2020
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00041
Qiao BReiche OHannig FTeich JKandemir MJimborean AMoseley T(2019)From loop fusion to kernel fusion: a domain-specific approach to locality optimizationProceedings of the 2019 IEEE/ACM International Symposium on Code Generation and Optimization10.5555/3314872.3314901(242-253)Online publication date: 16-Feb-2019
https://dl.acm.org/doi/10.5555/3314872.3314901
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