GPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative Study
Score
53Why selected
Systematic experimental design with clear speedup results, but limited to shallow network architecture and lacks in-depth discussion of scalability; overall quality is above average.
AI commentary
「This study provides a systematic benchmark for practical optimization of training shallow neural networks on GPUs, revealing that memory access optimization can improve performance more than simply increasing computational parallelism, offering direct guidance for accelerating production-level deep learning training.」
Quick overview
This paper presents a comparative study of three CUDA optimization strategies for forward and backward propagation in shallow neural networks: tiled shared memory with +1-column padding to eliminate bank conflicts, pre-transposed weight matrices for coalesced global memory access, and a fused MatMul+ReLU kernel to reduce intermediate global memory round-trips. Experiments on an NVIDIA Tesla T4 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.
- Proposes three CUDA optimization strategies: tiled shared memory, pre-transposed weight matrices, and fused MatMul+ReLU kernel.
- Achieves 1.41x speedup on large dataset, reducing execution time from 21.0s to 14.8s.
- Compared with CPU and OpenMP baselines, validates the critical role of memory-access optimization in GPU acceleration.
Full text
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 PDFAbstract: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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