Decision trees and tree ensembles are popular supervised learning models on tabular data. Two recent research trends on tree models stand out: (1) bigger and deeper models with many trees, and (2) scalable distributed training frameworks. However, existing implementations on distributed systems are IO-bound leaving CPU cores underutilized. They also only find best node-splitting conditions approximately due to row-based data partitioning scheme. In this paper, we target the exact training of tree models by effectively utilizing the available CPU cores. The resulting system called TreeServer adopts a column-based data partitioning scheme to minimize communication, and a node-centric task-based engine to fully explore the CPU parallelism. Experiments show that TreeServer is up to 10× faster than models in Spark MLlib. We also showcase TreeServer's high training throughput by using it to build big “deep forest” models.

Mining from a big graph those subgraphs that satisfy certain conditions is useful in many applications such as community detection and subgraph matching. These problems have a high time complexity, but existing systems to scale them are all IO-bound in execution. We propose the first truly CPU-bound distributed framework called G-thinker that adopts a user-friendly subgraph-centric vertex-pulling API for writing distributed subgraph mining algorithms. To utilize all CPU cores of a cluster, G-thinker features (1) a highly-concurrent vertex cache for parallel task access and (2) a lightweight task scheduling approach that ensures high task throughput. These designs well overlap communication with computation to minimize the CPU idle time. Extensive experiments demonstrate that G-thinker achieves orders of magnitude speedup compared even with the fastest existing subgraph-centric system, and it scales well to much larger and denser real network data. G-thinker is open-sourced at http://bit.ly/gthinker with detailed documentation.

Many computationally expensive problems are solved by a divide-and-conquer algorithm: a problem over a big dataset can be recursively divided into independent tasks over smaller subsets of the dataset. We present a distributed general-purpose framework called T-thinker which effectively utilizes the CPU cores in a cluster by properly decomposing an expensive problem into smaller independent tasks for parallel computation. T-thinker well overlaps CPU processing with network communication, and its superior performance is verified over a re-engineered graph mining system G-thinker available at http://cs.uab.edu/yanda/gthinker/.