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: 1258741
• 负责人: Carlos Guestrin
• 金额: $ 45万
• 依托单位: University of Washington
• 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 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1258741&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."

随着网络的发展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，需要机器学习算法的可比扩展。然而，设计和实现高效的并行机器学习算法具有挑战性且耗时。为了应对这一挑战，我们最近发布了 GraphLab，这是一个提供富有表现力且高效的高级抽象的框架，可以满足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，收到了来自许多大学和公司的数千次下载。目前，GraphLab 仅解决多核设置中的批处理问题。 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，不适合图划分方法。 3) 在线任务，其中数据和查询随着时间的推移而流动。 4) 离核计算，因为巨大的问题可能无法放入内存中，即使跨云也是如此。该项目的关键贡献之一是我们技术的持续传播和转移。 我们的开源软件版本将继续支持科学和工程领域的大规模机器学习应用。我们雄心勃勃的超越理论和系统的更广泛影响目标包括开发新课程，重点帮助学生为该领域的工业和科学需求做好准备。 我们建议的课程包括“网络机器学习”和“用于大机器学习和数据挖掘的云计算”。