76 citations · 213 across the 8 of their papers we have counts for
8 papers
A Comprehensive Survey on Distributed Training of Graph Neural Networks
Haiyang Lin, Mingyu Yan, Xiaochun Ye +4
Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN tra…
Mat2Stencil: A Modular Matrix-Based DSL for Explicit and Implicit Matrix-Free PDE Solvers on Structured Grid
Huanqi Cao, Shizhi Tang, Qianchao Zhu +2
Partial differential equation (PDE) solvers are extensively utilized across numerous scientific and engineering fields. However, achieving high performance and scalability often ne…
DFOGraph: An I/O- and Communication-Efficient System for Distributed Fully-out-of-Core Graph Processing
Jiping Yu, Wei Qin, Xiaowei Zhu +4
With the magnitude of graph-structured data continually increasing, graph processing systems that can scale-out and scale-up are needed to handle extreme-scale datasets. While exis…
TADOC: Text Analytics Directly on Compression
Feng Zhang, Jidong Zhai, Xipeng Shen +5
This article provides a comprehensive description of Text Analytics Directly on Compression (TADOC), which enables direct document analytics on compressed textual data. The article…
AIPerf: Automated machine learning as an AI-HPC benchmark
Zhixiang Ren, Yongheng Liu, Tianhui Shi +6
The plethora of complex artificial intelligence (AI) algorithms and available high performance computing (HPC) power stimulates the expeditious development of AI components with he…
RisGraph: A Real-Time Streaming System for Evolving Graphs to Support Sub-millisecond Per-update Analysis at Millions Ops/s
Guanyu Feng, Zixuan Ma, Daixuan Li +4
Evolving graphs in the real world are large-scale and constantly changing, as hundreds of thousands of updates may come every second. Monotonic algorithms such as Reachability and…
LiveGraph: A Transactional Graph Storage System with Purely Sequential Adjacency List Scans
Xiaowei Zhu, Guanyu Feng, Marco Serafini +5
The specific characteristics of graph workloads make it hard to design a one-size-fits-all graph storage system. Systems that support transactional updates use data structures with…
Bridging the Gap Between Neural Networks and Neuromorphic Hardware with A Neural Network Compiler
Yu Ji, YouHui Zhang, WenGuang Chen +1
Different from developing neural networks (NNs) for general-purpose processors, the development for NN chips usually faces with some hardware-specific restrictions, such as limited…