RI: Small: GraphLab 2: An Abstraction and System for Large-Scale Parallel Machine Learning on Natural Graphs

RI：小型：GraphLab 2：自然图上大规模并行机器学习的抽象和系统

• 批准号: 1218756
• 负责人: Carlos Guestrin
• 金额: $ 45万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2012-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218756&HistoricalAwards=false
• 关键词: RI; Small; GraphLab; Abstraction; System

With the growth of the Web and improvements in data collection technology in Science, datasets have been rapidly increasing in size and complexity, necessitating comparable scaling of machine learning algorithms. However, designing and implementing efficient parallel machine learning algorithms is challenging and time consuming. To address this challenge, we recently released GraphLab, a framework providing an expressive and efficient high-level abstraction satisfying the needs of a broad range of machine learning algorithms. The performance of our system has attracted significant attention, receiving thousands of downloads from many universities and companies.Currently, GraphLab only addresses batch processing in multicore settings. In this project, we are developing GraphLab 2: addressing the much more challenging online and distributed settings, tackling: 1) Cloud-based distributed machine learning. 2) Natural graphs, with very high-degree vertices that are not amenable to graph partitioning methods. 3) Online tasks, where data and queries are streaming over time. 4) Off-core computation, since huge problems may not fit into memory, even across the cloud.One of the key contributions of the project is the continual dissemination and transfer of our technology. Our open-source software releases will continue to enable large-scale machine learning applications in science and engineering.Our ambitious broader impact goals, beyond theory and systems, include the development of a new curriculum focused on preparing students for the industrial and scientific needs in this field. Our proposed courses include "Machine Learning on the Web" and "Cloud Computing for Big Machine Learning and Data Mining."

随着网络的增长和科学数据收集技术的改进，数据集的大小和复杂性迅速增加，需要对机器学习算法进行可比的比例。但是，设计和实施高效的并行机器学习算法是具有挑战性且耗时的。为了应对这一挑战，我们最近发布了Graphlab，该框架提供了一种表现力，高效的高级抽象，满足了广泛的机器学习算法的需求。 我们系统的性能引起了极大的关注，从许多大学和公司中获得了数千种下载。目前，GraphLab仅在多层设置中解决批处理处理。 在这个项目中，我们正在开发Graphlab 2：解决更具挑战性的在线和分布式设置，解决：1）基于云的分布式机器学习。 2）自然图，具有非常高的顶点，这些顶点不适合图形分配方法。 3）在线任务，数据和查询随着时间的流逝而流式传输。 4）偏外计算，因为即使在整个云中，巨大的问题也可能不适合记忆。项目的关键贡献之一是我们技术的持续传播和转移。 我们的开源软件发行版将继续在科学和工程中启用大型机器学习应用。我们雄心勃勃的广泛影响目标，包括理论和系统，包括开发一项新的课程，重点是为学生在该领域的工业和科学需求做好准备。 我们提出的课程包括“网络上的机器学习”和“用于大型机器学习和数据挖掘的云计算”。