Joseph E. Gonzalez

TL;DR: This open source computing framework unifies streaming, batch, and interactive big data workloads to unlock new applications.

TL;DR: This paper describes the challenges of computation on natural graphs in the context of existing graph-parallel abstractions and introduces the PowerGraph abstraction which exploits the internal structure of graph programs to address these challenges.

TL;DR: GraphLab as discussed by the authors extends the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees to reduce network congestion and mitigate the effect of network latency in the shared-memory setting.

TL;DR: This paper introduces GraphX, an embedded graph processing framework built on top of Apache Spark, a widely used distributed dataflow system and demonstrates that GraphX achieves an order of magnitude performance gain over the base dataflow framework and matches the performance of specialized graph processing systems while enabling a wider range of computation.

TL;DR: The expressiveness of the GraphLab framework is demonstrated by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing and it is shown that using GraphLab the authors can achieve excellent parallel performance on large scale real-world problems.