research-article

Performance-oriented Optimizations for OpenCL Streaming Kernels on the FPGA

Authors:

Hal FinkelAuthors Info & Claims

IWOCL '18: Proceedings of the International Workshop on OpenCL

Article No.: 1, Pages 1 - 8

https://doi.org/10.1145/3204919.3204920

Published: 14 May 2018 Publication History

Abstract

When Field-programmable gate arrays (FPGAs) can implement streaming applications efficiently and high-level synthesis (HLS) tools allow people, who have little hardware design knowledge, to evaluate an application on FPGAs, there is a need to understand where OpenCL and FPGA can play in the streaming domains. To this end, we explore the implementation space and discuss the techniques of optimizing the performance of the streaming kernels using the Intel OpenCL SDK for FPGA. On the Nallatech 385A FPGA platform that features an Arria 10 GX1150 FPGA, the experimental results show that FPGA resources, such as block RAMs and DSPs, can limit the performance of a kernel before the constraint of memory bandwidth takes effect. Kernel vectorization and compute unit duplication are practical optimization techniques that can improve the kernel performance by a factor of 2.8 to 10. The combination of the two techniques can improve the performance by a factor of 3.3 to 16, achieving the highest performance. To improve the performance of streaming kernels with compute unit duplication, the local work size needs to be tuned. The optimal value can increase the performance of a duplicated kernel without tuning by a factor of 3 to 70.

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Cited By

Jin ZFinkel H(2020)Performance Evaluation of the Vectorizable Binary Search Algorithms on an FPGA Platform2020 IEEE/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3)10.1109/IA351965.2020.00014(63-67)Online publication date: Nov-2020
https://doi.org/10.1109/IA351965.2020.00014
Cabrera AChamberlain R(2020)Design and Performance Evaluation of Optimizations for OpenCL FPGA Kernels2020 IEEE High Performance Extreme Computing Conference (HPEC)10.1109/HPEC43674.2020.9286221(1-7)Online publication date: 22-Sep-2020
https://doi.org/10.1109/HPEC43674.2020.9286221
Jin ZFinkel HShi XBuck MMa JVeltri P(2019)Simulation of Random Network of Hodgkin and Huxley Neurons with Exponential Synaptic Conductances on an FPGA PlatformProceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3307339.3343460(653-657)Online publication date: 4-Sep-2019
https://dl.acm.org/doi/10.1145/3307339.3343460
Show More Cited By

Index Terms

Performance-oriented Optimizations for OpenCL Streaming Kernels on the FPGA
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel programming languages
2. Hardware
  1. Integrated circuits
    1. Reconfigurable logic and FPGAs

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Published In

cover image ACM Other conferences

IWOCL '18: Proceedings of the International Workshop on OpenCL

May 2018

108 pages

ISBN:9781450364393

DOI:10.1145/3204919

Copyright © 2018 ACM.

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

In-Cooperation

Huawei Technologies Co. Ltd.: Huawei Technologies Co. Ltd.
Khronos: Khronos Group
Xilinx: Xilinx Inc.
Codeplay: Codeplay Software Ltd.
Intel: Intel
The University of Bristol: The University of Bristol

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2018

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IWOCL '18

IWOCL '18: International Workshop on OpenCL

May 14 - 16, 2018

Oxford, United Kingdom

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IWOCL '18 Paper Acceptance Rate 16 of 33 submissions, 48%;

Overall Acceptance Rate 84 of 152 submissions, 55%

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Cited By

Jin ZFinkel H(2020)Performance Evaluation of the Vectorizable Binary Search Algorithms on an FPGA Platform2020 IEEE/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3)10.1109/IA351965.2020.00014(63-67)Online publication date: Nov-2020
https://doi.org/10.1109/IA351965.2020.00014
Cabrera AChamberlain R(2020)Design and Performance Evaluation of Optimizations for OpenCL FPGA Kernels2020 IEEE High Performance Extreme Computing Conference (HPEC)10.1109/HPEC43674.2020.9286221(1-7)Online publication date: 22-Sep-2020
https://doi.org/10.1109/HPEC43674.2020.9286221
Jin ZFinkel HShi XBuck MMa JVeltri P(2019)Simulation of Random Network of Hodgkin and Huxley Neurons with Exponential Synaptic Conductances on an FPGA PlatformProceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3307339.3343460(653-657)Online publication date: 4-Sep-2019
https://dl.acm.org/doi/10.1145/3307339.3343460
Liu YHuang LWu MCui HLv FFeng XXue JAmaral JKulkarni M(2019)PPOpenCL: a performance-portable OpenCL compiler with host and kernel thread code fusionProceedings of the 28th International Conference on Compiler Construction10.1145/3302516.3307350(2-16)Online publication date: 16-Feb-2019
https://dl.acm.org/doi/10.1145/3302516.3307350
Jin ZFinkel H(2019)Base64 Encoding on Heterogeneous Computing Platforms2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP)10.1109/ASAP.2019.00014(247-254)Online publication date: Jul-2019
https://doi.org/10.1109/ASAP.2019.00014
Jin ZFinkel H(2018)Bob Jenkins Lookup3 Hash Function on OpenCL FPGA Platform2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8621960(4736-4741)Online publication date: Dec-2018
https://doi.org/10.1109/BigData.2018.8621960

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