浅层神经网络前向与反向传播的GPU并行化策略：基于CUDA的对比研究

Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2606.30497v1 (cs) [Submitted on 29 Jun 2026]

Title:GPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative Study

Authors:Rania Zitouni, Nadine Bousdjira, Sarah Hasnaoui, Amel Sadoun, Fatma Salhi View a PDF of the paper titled GPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative Study, by Rania Zitouni and 4 other authors View PDF

Abstract:We present a comparative study of CUDA optimization strategies applied to forward and backward propagation in a shallow neural network. Three stacked optimizations are evaluated: (1) tiled shared memory with bank-conflict elimination via +1-column padding, (2) pre-transposed weight matrices for coalesced global memory access, and (3) a fused MatMul+ReLU kernel that eliminates intermediate global-memory round-trips. Experiments on an NVIDIA Tesla T4 (CUDA 13.0) across three dataset sizes show that the fully optimized implementation achieves a 1.41x speedup over the baseline CUDA version on the large dataset (25,600 samples), reducing execution time from 21.0s to 14.8s. Results are compared against a sequential CPU baseline and an OpenMP parallel implementation, demonstrating the effectiveness of memory-access optimization in GPU-accelerated deep learning primitives.

Comments:	7 pages, 5 figures. Technical report, ESI Algiers, 2025--2026
Subjects:	Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
MSC classes:	68W10, 68T07
ACM classes:	C.1.4; I.2.6
Cite as:	arXiv:2606.30497 [cs.DC]
	(or arXiv:2606.30497v1 [cs.DC] for this version)
	https://doi.org/10.48550/arXiv.2606.30497 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Rania Zitouni [view email]
[v1] Mon, 29 Jun 2026 16:02:10 UTC (1,206 KB)
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