GraphLab框架允许用户指定优先级对在T中的顶点,所以许多MLDM应用程序从优先级受益。GraphLab运行时可能会使用这些优先级结合系统级目标来优化顶点的执行顺序。 4.1 可串行化执行 GraphLab为了防止数据竞争以及方便程序的调试、运行。GraphLab支持顶点程序的可串行化执行,也就是说防止相邻顶点同时运行顶点程序。 一个实现可...
Distribut- ed GraphLab: a framework for machine learning and data mining in the cloud. In: Proceedings of the VLDB Endowment; 2012 Aug 27-31; Istanbul, Turkey; 2012;5(8): 716-27.Low Y, Gonzalez J, Kyrola A, Bickson D, Guestrin C, Hellerstein JM. Distributed graphlab: a framework...
我们也介绍GraphLab容错,这个容错使用了经典的抽象Chandy-Lamport快照算法,并展示它如何能轻易利用实现的GraphLab抽象本身。最后,我们评估我们的分布式GraphLab框架,在Amazon EC2部署和展示1 - 2个数量级在Hadoop-based实现收益的性能。 1简介 指数增长的机器学习和数据挖掘(MLDM,即Machine Learning and Data Mining)问题...
GraphLab: A New Framework For Parallel Machine Learning1 / 23
GraphLab is a graph-based, high performance, distributed computation framework written in C++. The GraphLab project started in 2009 to develop a new parallel computation abstraction tailored to machine learning. GraphLab 1.0 represents our first shared memory design, and in GraphLab 2.1, we complet...
machine-learning deep-learning graph-algorithms tensorflow pagerank pytorch gcn graph-classification graph-neural-networks gnn Updated Dec 9, 2024 Jupyter Notebook TrustAGI-Lab / graph_datasets Star 286 Code Issues Pull requests A Repository of Benchmark Graph Datasets for Graph Classification (...
GraphLab是一个面向大规模机器学习/图计算的分布式内存计算框架,由CMU在2009年开始的一个C++项目,这里的内容是基于论文 Low, Yucheng, et al. "Distributed GraphLab: A Framework for Machine Learning in the Cloud" Proceedings of the VLDB Endowment 5.8 (2012)[ppt] ...
it is crucial for the system to be robust to network disruption since it is distributed over the internet. We have implemented measures to ensure that agents deployed in the lab can handle internet cut-offs and resume operations once back online. To minimise downtime during reconnection, future...
Construction of graph partitions at cluster machines concludes GraphLab's initialization phase, and the execution phase begins.Execution phaseAs shown in Figure 5, each cluster machine runs an instance of the GraphLab engine, which incorporates two main parts: the data graph, and the user-define...
This repository represents the efforts of theMaterials Virtual Labin developing graph networks for machine learning in materials science. It is a work in progress and the models we have developed thus far are only based on our best efforts. We welcome efforts by anyone to build and test models...