GPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative Study
arXiv:2606.30497v1 Announce Type: cross 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.
Rapid advancements in AI and increasing computational demands necessitate continuous optimization of core ML operations on specialized hardware like GPUs to maintain performance gains.
This research provides practical methods for improving the efficiency of AI model training, directly impacting the speed and energy consumption of developing and deploying advanced AI systems.
The demonstrated CUDA optimization strategies offer concrete pathways for developers to achieve higher performance in neural network computations, potentially accelerating the development cycle for AI applications.
- · AI developers
- · GPU manufacturers
- · Data centers
- · AI-driven industries
- · Inefficient AI training practices
- · Non-optimized legacy AI systems
Faster training times for shallow neural networks on NVIDIA GPUs.
Reduced operational costs and energy consumption for AI development and deployment due to increased efficiency.
Accelerated progress in AI research and application, as computational bottlenecks are eased.
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Read at arXiv cs.LG