面向机器学习的分布式并行计算关键技术及应用

当前机器学习等算法的计算、迭代过程日趋复杂, 充足的算力是保障人工智能应用落地效果的关键。本文首先提出一种适应倾斜数据的分布式异构环境下的任务时空调度算法，有效提升机器学习模型训练等任务的平均效率；其次，提出分布式异构环境下高效的资源管理系统与节能调度算法，实现分布式异构环境下基于动态预测的跨域计算资源迁移及电压/频率的动态调节，节省了系统的整体能耗；然后构建了适应于机器学习/深度学习算法迭代的分布式异构优化环境，提出了面向机器学习/图迭代算法的分布式并行优化基本方法。最后，本文研发了面向领域应用的智能分析系统，并在制造、交通、教育、医疗等领域推广应用，解决了在高效数据采集、存储、清洗、融合与智能分析等过程中普遍存在的性能瓶颈问题。

Key technologies and applications of distributed parallel computing for machine learning

At present, the calculation and iteration process of algorithms such as machine learning is becoming more and more complex. Sufficient computational power is the key to ensure the landing effect of artificial intelligence application. In view of this, this paper first puts forward a task space-time scheduling algorithm adapted to the distributed heterogeneous environment of skew data, which effectively improves the average efficiency of tasks such as machine learning model training. Then, the high-efficiency resource management system and energy-saving scheduling algorithm in distributed heterogeneous environment are proposed to realize the dynamic prediction based cross-domain computing resource migration and voltage/frequency dynamic regulation in distributed heterogeneous environment, which saves the overall energy consumption of the system, and then, the distributed heterogeneous optimization environment adapted to the iteration of machine learning/deep learning algorithm is constructed, and the basic method of distributed parallel optimization for machine learning/graph iteration algorithm is proposed. Finally, the intelligent analysis system for field-oriented applications is researched and developed, and popularized in manufacturing, transportation, education, medical and other fields, which solves the performance bottleneck problems that are common in the process of high-efficiency data collection, storage, cleaning, fusion and intelligent analysis.