718 citations · 918 across the 6 of their papers we have counts for
6 papers
Polyjuice: High-Performance Transactions via Learned Concurrency Control
Jiachen Wang, Ding Ding, Huan Wang +4
Concurrency control algorithms are key determinants of the performance of in-memory databases. Existing algorithms are designed to work well for certain workloads. For example, opt…
Scalable Graph Neural Networks for Heterogeneous Graphs
Lingfan Yu, Jiajun Shen, Jinyang Li +1
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature s…
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye +12
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and im…
On the parallels between Paxos and Raft, and how to port optimizations
Zhaoguo Wang, Changgeng Zhao, Shuai Mu +2
In recent years, Raft has overtaken Paxos as the consensus algorithm of choice. [53] While many have pointed out similarities between the two protocols, no one has formally mapped…
Supporting Very Large Models using Automatic Dataflow Graph Partitioning
Minjie Wang, Chien-chin Huang, Jinyang Li
This paper presents Tofu, a system that partitions very large DNN models across multiple GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow gr…
Unifying Data, Model and Hybrid Parallelism in Deep Learning via Tensor Tiling
Minjie Wang, Chien-chin Huang, Jinyang Li
Deep learning systems have become vital tools across many fields, but the increasing model sizes mean that training must be accelerated to maintain such systems' utility. Current s…