机器学习和深度学习的并行训练方法

Parallel Training Methods for Machine Learning and Deep Learning

并行计算技术广泛用于对一些特定问题进行更进一步的优化,从而突破性地降低算法的时间消耗。近年来,随着大数据和人工智能的快速发展,在进行大规模深度学习模型的训练时,时间消耗成为一个重要的考虑因素。在模型的训练过程中,由于各个样本之间互不相关的性质,使得模型的训练过程可以利用并行技术来很好地优化。本文以最基础的线性回归作为模型的任务,测试了并行化方法在深度学习模型中的可行性,并对比了不同节点下的性能提升幅度。本文所提出的并行训练方法的时间复杂度为O(m/k×P+k×ϵ),根据该时间复杂度,可以合理地根据待解决问题的规模来选择合适的并行化策略。

Parallel computing techniques are widely used for further optimization of specific problems,enabling breakthrough improvements of algorithm time complexity.In recent years,with the rapid development of big data and artificial intelligence,time consumption has become an important consideration when training large-scale deep learning models.The training process of the model can be optimized by parallel techniques due to the uncorrelated nature of the samples.In this paper,we test the feasibility of parallelization methods in deep learning models with the most basic linear regression as the task of the model,and compare the per⁃formance improvement under different nodes.The time complexity of the parallel training method proposed is O(m/k×P+k×ϵ).Based on this time complexity,it is reasonable to choose the appropriate parallelization strategy based on the size of the problem to be solved.