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Data Parallel C++: Enhancing SYCL Through Extensions for Productivity and Performance

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Jason SewallAuthors Info & Claims

IWOCL '20: Proceedings of the International Workshop on OpenCL

Article No.: 7, Pages 1 - 2

https://doi.org/10.1145/3388333.3388653

Published: 27 April 2020 Publication History

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Abstract

SYCL™ is a heterogeneous programming framework built on top of modern C++. Data Parallel C++, recently introduced as part of Intel's oneAPI project, is an implementation of SYCL. Data Parallel C++ (DPC++) is being developed as an open-source project on top of Clang and LLVM. It combines C++, SYCL, and new extensions to improve programmer productivity when writing highly performant code for heterogeneous architectures.

This technical presentation will describe several extensions that DPC++ has proposed and implemented on top of SYCL. While many of the extensions can help to improve application performance, all of them work to improve programmer productivity by integrating easily into existing C++ applications, and by simplifying common patterns found in SYCL and C++. DPC++ is a proving ground where the value of extensions can be demonstrated before being proposed for inclusion in future versions of the SYCL specification.

References

[1]

Intel Corporation. 2020. DPC++ Extension Proposals. https://github.com/intel/llvm/tree/sycl/sycl/doc/extensions.

Google Scholar

[2]

Intel Corporation. 2020. Intel Data Parallel C++ Compiler. https://github.com/intel/llvm.

Google Scholar

[3]

Khronos SYCL Working Group. 2019. The SYCL 1.2.1 Specification. https://www.khronos.org/registry/SYCL/.

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Cited By

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Thomadakis PChrisochoides N(2024)Runtime support for CPU-GPU high-performance computing on distributed memory platformsFrontiers in High Performance Computing10.3389/fhpcp.2024.14170402Online publication date: 19-Jul-2024
https://doi.org/10.3389/fhpcp.2024.1417040
Thoman PKnorr FCrisci L(2024)SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and ConformanceProceedings of the 12th International Workshop on OpenCL and SYCL10.1145/3648115.3648136(1-12)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3648115.3648136
Crisci LCarpentieri LThoman PAlpay AHeuveline VCosenza B(2024)SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUsProceedings of the 12th International Workshop on OpenCL and SYCL10.1145/3648115.3648120(1-12)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3648115.3648120
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Index Terms

Data Parallel C++: Enhancing SYCL Through Extensions for Productivity and Performance
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel programming languages
2. Software and its engineering
  1. Software notations and tools
    1. Compilers
    2. General programming languages
      1. Language types
        Parallel programming languages
  2. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        Memory management

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Information

Published In

IWOCL '20: Proceedings of the International Workshop on OpenCL

April 2020

104 pages

ISBN:9781450375313

DOI:10.1145/3388333

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

Khronos: Khronos Group
Codeplay: Codeplay Software Ltd.
Intel: Intel
The University of Bristol: The University of Bristol
Tech Univ of Munich: Technical University of Munich

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 April 2020

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Extended-abstract
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Refereed limited

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IWOCL '20

IWOCL '20: International Workshop on OpenCL

April 27 - 29, 2020

Munich, Germany

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IWOCL '20 Paper Acceptance Rate 21 of 30 submissions, 70%;

Overall Acceptance Rate 84 of 152 submissions, 55%

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Cited By

View all

Thomadakis PChrisochoides N(2024)Runtime support for CPU-GPU high-performance computing on distributed memory platformsFrontiers in High Performance Computing10.3389/fhpcp.2024.14170402Online publication date: 19-Jul-2024
https://doi.org/10.3389/fhpcp.2024.1417040
Thoman PKnorr FCrisci L(2024)SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and ConformanceProceedings of the 12th International Workshop on OpenCL and SYCL10.1145/3648115.3648136(1-12)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3648115.3648136
Crisci LCarpentieri LThoman PAlpay AHeuveline VCosenza B(2024)SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUsProceedings of the 12th International Workshop on OpenCL and SYCL10.1145/3648115.3648120(1-12)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3648115.3648120
Vasileska ITomšič PKos LBogdanović L(2024)Unveiling Performance Insights and Portability Achievements Between CUDA and SYCL for Particle-in-Cell Codes on Different GPU Architectures2024 47th MIPRO ICT and Electronics Convention (MIPRO)10.1109/MIPRO60963.2024.10569866(1115-1120)Online publication date: 20-May-2024
https://doi.org/10.1109/MIPRO60963.2024.10569866
Crisci LCarpentieri LCosenza BAccordi GGadioli DVitali EPalermo GBeccari A(2024)Enabling performance portability on the LiGen drug discovery pipelineFuture Generation Computer Systems10.1016/j.future.2024.03.045158:C(44-59)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.future.2024.03.045
Subramanian HGurumurthy BDurand GBroneske DSaake G(2023)Out-of-the-box library support for DBMS operations on GPUsDistributed and Parallel Databases10.1007/s10619-023-07431-341:3(489-509)Online publication date: 10-May-2023
https://doi.org/10.1007/s10619-023-07431-3
Sakiotis IArumugam KPaterno MRanjan DTerzić BZubair M(2023)Porting Numerical Integration Codes from CUDA to oneAPI: A Case StudyHigh Performance Computing10.1007/978-3-031-32041-5_18(339-358)Online publication date: 10-May-2023
https://doi.org/10.1007/978-3-031-32041-5_18
Ghiglio PDolinsky UGoli MNarasimhan K(2023)Improving performance of SYCL applications on CPU architectures using LLVM‐directed compilation flowConcurrency and Computation: Practice and Experience10.1002/cpe.781035:27Online publication date: 30-May-2023
https://doi.org/10.1002/cpe.7810
Sasidharan A(2022)Performance Models for Heterogeneous Iterative ProgramsInternational Journal of Networking and Computing10.15803/ijnc.12.1_13112:1(131-163)Online publication date: 2022
https://doi.org/10.15803/ijnc.12.1_131
Baratta IRichardson CWells G(2022)Performance analysis of matrix-free conjugate gradient kernels using SYCLProceedings of the 10th International Workshop on OpenCL10.1145/3529538.3529993(1-10)Online publication date: 10-May-2022
https://dl.acm.org/doi/10.1145/3529538.3529993
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On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing

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