Recently, Deep Neural Networks (DNNs) have recorded great success in handling medical and other complex classification tasks. However, as the sizes of a DNN model and the available dataset increase, the training process becomes more complex and computationally intensive, which usually takes a longer time to complete. In this work, we have proposed a generic full end-to-end hybrid parallelization approach combining both model and data parallelism for efficiently distributed and scalable training of DNN models. We have also proposed a Genetic Algorithm based heuristic resources allocation mechanism (GABRA) for optimal distribution of partitions on the available GPUs for computing performance optimization. We have applied our proposed approach to a real use case based on 3D Residual Attention Deep Neural Network (3D-ResAttNet) for efficient Alzheimer Disease (AD) diagnosis on multiple GPUs. The experimental evaluation shows that the proposed approach is efficient and scalable, which achieves almost linear speedup with little or no differences in accuracy performance when compared with the existing non-parallel DNN models.

翻译：最近,深神经网络(DNN)在处理医疗和其他复杂分类任务方面取得了巨大成功,然而,随着DNN模型的大小和现有数据集的增加,培训过程变得更加复杂和计算密集,通常需要较长时间才能完成。在这项工作中,我们提议了将模型和数据平行的通用全端对端混合法,结合模型和数据平行法,以便对DNN模型进行有效的分布和可扩缩的培训。我们还提议了基于超常的遗传算法资源分配机制(GABRA),以便最佳分配现有GPU的分区,用于计算业绩优化。我们根据3D残余注意力深神经网络(D-ResAttNet)的建议,对基于多个GPU的高效阿尔茨海默氏病诊断(AD)的一个实际应用了我们的方法。实验性评估表明,拟议的方法既高效又可扩缩,与现有的非平行DNNN模型相比,几乎可以实现线性加速,准确性能几乎没有差异。