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基于参数服务器的分布式学习系统及其在阿里巴巴和蚂蚁金服中的应用KunPeng: Parameter Server based Distributed Learning Systems and Its Applications in Alibaba and Ant Financial |
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| 课程网址: | http://videolectures.net/kdd2017_li_distributed_learning/ |
| 主讲教师: | Xiaolong Li |
| 开课单位: | 蚂蚁金服 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In recent years, due to the emergence of Big Data (terabytes or petabytes) and Big Model (tens of billions of parameters), there has been an ever-increasing need of parallelizing machine learning (ML) algorithms in both academia and industry. Although there are some existing distributed computing systems, like Hadoop and Spark, for parallelizing ML algorithms, they only provide synchronous and coarse-grained operators (e.g., Map, Reduce, and Join, etc.), which may hinder developers from implementing more efficient algorithms. This motivated us to design a universal distributed platform termed KunPeng, that combines both distributed systems and parallel optimization algorithms to deal with the complexities that arise from large-scale ML. Specifically, KunPeng not only encapsulates the characteristics of data/model parallelism, load balancing, model sync-up, sparse representation, industrial fault-tolerance, etc., but also provides easy-to-use interface to empower users to focus on the core ML logics. Empirical results on terabytes of real datasets with billions of samples and features demonstrate that, such a design brings compelling performance improvements on ML programs ranging from Follow-the-Regularized-Leader Proximal algorithm to Sparse Logistic Regression and Multiple Additive Regression Trees. Furthermore, KunPeng’s encouraging performance is also shown for several real-world applications including the Alibaba’s Double 11 Online Shopping Festival. |
| 关 键 词: | 机器学习; 计算系统; 鲲鹏平台 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-13：chenxin01 |
| 最后编审: | 2023-05-17：chenxin01 |
| 阅读次数: | 21 |