图 书 馆
分布式计算机器语言：一个关于并行复用分析快速实现的基础设施Hadoop-ML: An Infrastructure for the Rapid Implementation of Parallel Reusable Analytics |
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| 课程网址: | http://videolectures.net/nipsworkshops09_pednault_hmli/ |
| 主讲教师: | Edwin Pednault |
| 开课单位: | 沃森研究中心 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Hadoop is an open-source implementation of Google's Map-Reduce programming model. Over the past few years, it has evolved into a popular platform for parallelization in industry and academia. Furthermore, trends suggest that Hadoop will likely be the analytics platform of choice on forthcoming Cloud-based systems. Unfortunately, implementing parallel machine learning/data mining (ML/DM) algorithms on Hadoop is complex and time consuming. To address this challenge, we present Hadoop-ML, an infrastructure to facilitate the implementation of parallel ML/DM algorithms on Hadoop. Hadoop-ML has been designed to allow for the specification of both task-parallel and data-parallel ML/DM algorithms. Furthermore, it supports the composition of parallel ML/DM algorithms using both serial as well as parallel building blocks -- this allows one to write reusable parallel code. The proposed abstraction eases the implementation process by requiring the user to only specify computations and their dependencies, without worrying about scheduling, data management, and communication. As a consequence, the codes are portable in that the user never needs to write Hadoop-specific code. This potentially allows one to leverage future parallelization platforms without rewriting one's code. |
| 关 键 词: | 编程模型; 并行化平台; 云计算; 并行机器学习; 基础设施 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-03：wuyq |
| 阅读次数: | 44 |