多核数字信号处理卷积算法并行优化
作者:
作者单位:

(1. 国防科技大学 计算机学院, 湖南 长沙 410073;2. 国防科技大学 并行与分布计算全国重点实验室, 湖南 长沙 410073)

作者简介:

许金伟(1990—),男,河南淮阳人,助理研究员,博士,E-mail:xujinwei13@nudt.edu.cn

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中图分类号:

TP391

基金项目:

国家自然科学基金资助项目(61732018)


Parallel optimization of convolution algorithm on multi-core DSP
Author:
Affiliation:

(1. College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China;2. National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Changsha 410073, China)

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    摘要:

    针对国防科技大学自主研发的异构多核数字信号处理(digital signal processing, DSP)芯片的特征以及卷积算法自身特点,提出了一种面向多核DSP架构的高性能多核并行卷积实现方案。针对1×1卷积提出了特征图级多核并行方案;针对卷积核大于1的卷积提出了窗口级多核并行优化设计,同时提出了逐元素向量化计算的核内并行优化实现。实验结果表明,所提并行优化方法实现单核计算效率最高能达到64.95%,在带宽受限情况下,多核并行扩展效率可达到48.36%~88.52%,在典型网络ResNet50上的执行性能与E5-2640 CPU相比,获得了5.39倍性能加速。

    Abstract:

    According to the characteristics of the heterogeneous multi-core DSP(digital signal processing) chip independently developed by National University of Defense Technology and the characteristics of the convolution algorithm, a high-performance multi-core parallel convolution implementation scheme for multi-core DSP architecture was proposed. A feature graph level multi-core parallel scheme is proposed for 1×1 convolution. For convolutions with kernels larger than 1, a window level multi-core parallel optimization design was proposed, and an element-wise vectorization based intra-core parallel optimization implementation was proposed. The experimental results show that the proposed parallel optimization method can reach a maximum single core computing efficiency of 64.95%. When the bandwidth is limited, the parallel expansion efficiency of multi-core can still reach 48.36% ~ 88.52%. Compared with E5-2640 CPU, the execution performance on the typical network ResNet50 achieves 5.39x performance acceleration.

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引用本文

许金伟,王庆林,李娅琳,等.多核数字信号处理卷积算法并行优化[J].国防科技大学学报,2024,46(1):103-112.
XU Jinwei, WANG Qinglin, LI Yalin, et al. Parallel optimization of convolution algorithm on multi-core DSP[J]. Journal of National University of Defense Technology,2024,46(1):103-112.

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  • 收稿日期:2022-09-20
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  • 在线发布日期: 2024-01-28
  • 出版日期: 2024-02-28
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