Optimizing Distributed Training Approaches for Scaling Neural Networks

This paper presents a comparative analysis of distributed training strategies for large-scale neural networks, focusing on data parallelism, model parallelism, and hybrid approaches. We evaluate these strategies on image classification tasks using the CIFAR-100 dataset, measuring training time, convergence rate, and model accuracy. Our experimental results demonstrate that hybrid parallelism achieves a 3.2x speedup compared to single-device training while maintaining comparable accuracy. We propose an adaptive scheduling algorithm that dynamically switches between parallelism strategies based on network characteristics and available computational resources, resulting in an additional 18% improvement in training efficiency.