Gemini: A Computation-Centric Distributed Graph Processing System

By Xiaowei Zhu, Wenguang Chen, Weimin Zheng, Xiaosong Ma. USENIX Symposium on Operating Systems Design and Implementation, 2016

441 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3231d62bec8e8cc1d837e85893889855767c3b13

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Da Zheng
2. Chien-chin Huang
3. Minjie Wang
4. Zheng Zhang
5. Chao Ma
6. George Karypis
7. Rong Chen
8. Jiaxin Shi

Citations in the corpus

• DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs (2020, background): “Gemini [30] shows that previous distributed graph processing framework has significant overhead in a single machine.”
• PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs (2019, methodology): “We believe that PowerLyra can further improve the performance for both in-memory and out-ofcore graphs by adopting the novel techniques of single-machine systems, such as aNUMA-aware access strategy [92, 100].”
• Supporting Very Large Models using Automatic Dataflow Graph Partitioning (2018, background): “They can be partitioned in a “model parallel” manner by distributed graph computation frameworks [47, 48, 49, 50, 51, 52].”