research-article

Ocelot: a dynamic optimization framework for bulk-synchronous applications in heterogeneous systems

Authors:

Gregory Frederick Diamos,

Andrew Robert Kerr,

Sudhakar Yalamanchili,

Nathan ClarkAuthors Info & Claims

PACT '10: Proceedings of the 19th international conference on Parallel architectures and compilation techniques

Pages 353 - 364

https://doi.org/10.1145/1854273.1854318

Published: 11 September 2010 Publication History

Abstract

Ocelot is a dynamic compilation framework designed to map the explicitly data parallel execution model used by NVIDIA CUDA applications onto diverse multithreaded platforms. Ocelot includes a dynamic binary translator from Parallel Thread eXecution ISA (PTX) to many-core processors that leverages the Low Level Virtual Machine (LLVM) code generator to target x86 and other ISAs. The dynamic compiler is able to execute existing CUDA binaries without recompilation from source and supports switching between execution on an NVIDIA GPU and a many-core CPU at runtime. It has been validated against over 130 applications taken from the CUDA SDK, the UIUC Parboil benchmarks [1], the Virginia Rodinia benchmarks [2], the GPU-VSIPL signal and image processing library [3], the Thrust library [4], and several domain specific applications.

This paper presents a high level overview of the implementation of the Ocelot dynamic compiler highlighting design decisions and trade-offs, and showcasing their effect on application performance. Several novel code transformations are explored that are applicable only when compiling explicitly parallel applications and traditional dynamic compiler optimizations are revisited for this new class of applications. This study is expected to inform the design of compilation tools for explicitly parallel programming models (such as OpenCL) as well as future CPU and GPU architectures.

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Cited By

Han RChen JGarg BZhou XLu JYoung JSim JKim H(2024)CuPBoP: Making CUDA a Portable LanguageACM Transactions on Design Automation of Electronic Systems10.1145/365994929:4(1-25)Online publication date: 23-Apr-2024
https://dl.acm.org/doi/10.1145/3659949
Darche SDagenais M(2024)Low-Overhead Trace Collection and Profiling on GPU Compute KernelsACM Transactions on Parallel Computing10.1145/364951011:2(1-24)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/10.1145/3649510
Yan KShi YYan YChen QHuang ZSi M(2023)Exploring OpenMP GPU Offloading for Implementing Convolutional Neural NetworksProceedings of the 14th International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3582514.3582523(60-69)Online publication date: 25-Feb-2023
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Index Terms

Ocelot: a dynamic optimization framework for bulk-synchronous applications in heterogeneous systems
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
      1. Single instruction, multiple data
2. Software and its engineering
  1. Software notations and tools
    1. Compilers

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cover image ACM Conferences

PACT '10: Proceedings of the 19th international conference on Parallel architectures and compilation techniques

September 2010

596 pages

ISBN:9781450301787

DOI:10.1145/1854273

General Chair:
Valentina Salapura
IBM TJ Watson Research Center
,
Program Chairs:
Michael Gschwind
IBM Systems & Technology Group
,
Jens Knoop
Technische Universit�t Wien

Copyright � 2010 ACM.

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IFIP WG 10.3: IFIP working group 10.3 on concurrent systems
IEEE CS TCPP: IEEE-CS technical committee on parallel processing
SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE CS TCAA: IEEE CS technical committee on architectural acoustics

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Association for Computing Machinery

New York, NY, United States

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Published: 11 September 2010

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IFIP WG 10.3
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PACT '10: International Conference on Parallel Architectures and Compilation Techniques

September 11 - 15, 2010

Vienna, Austria

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Cited By

Han RChen JGarg BZhou XLu JYoung JSim JKim H(2024)CuPBoP: Making CUDA a Portable LanguageACM Transactions on Design Automation of Electronic Systems10.1145/365994929:4(1-25)Online publication date: 23-Apr-2024
https://dl.acm.org/doi/10.1145/3659949
Darche SDagenais M(2024)Low-Overhead Trace Collection and Profiling on GPU Compute KernelsACM Transactions on Parallel Computing10.1145/364951011:2(1-24)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/10.1145/3649510
Yan KShi YYan YChen QHuang ZSi M(2023)Exploring OpenMP GPU Offloading for Implementing Convolutional Neural NetworksProceedings of the 14th International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3582514.3582523(60-69)Online publication date: 25-Feb-2023
https://dl.acm.org/doi/10.1145/3582514.3582523
Hao YJain NVan der Wijngaart RSaxena NFan YLiu XVieira MCardellini VDi Marco ATuma P(2023)DrGPU: A Top-Down Profiler for GPU ApplicationsProceedings of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578244.3583736(43-53)Online publication date: 15-Apr-2023
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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
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Fukuhara JTakimoto M(2022)Scalar Replacement Considering Branch DivergenceJournal of Information Processing10.2197/ipsjjip.30.16430(164-178)Online publication date: 2022
https://doi.org/10.2197/ipsjjip.30.164
Doerfert JJasper MHuber JAbdelaal KGeorgakoudis GScogland TParasyris KKloeckner AMoreira J(2022)Breaking the Vendor LockProceedings of the International Conference on Parallel Architectures and Compilation Techniques10.1145/3559009.3569687(494-504)Online publication date: 8-Oct-2022
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Han RLee JSim JKim H(2022)COX : Exposing CUDA Warp-level Functions to CPUsACM Transactions on Architecture and Code Optimization10.1145/355473619:4(1-25)Online publication date: 16-Sep-2022
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Skaletsky ALevit-Gurevich KBerezalsky MKuznetcova YYakov H(2022)Flexible Binary Instrumentation Framework to Profile Code Running on Intel GPUs2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS55109.2022.00011(109-120)Online publication date: May-2022
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Levit-Gurevich KSkaletsky ABerezalsky MKuznetcova YYakov H(2022)Profiling Intel Graphics Architecture with Long Instruction Traces2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS55109.2022.00001(1-11)Online publication date: May-2022
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