Dorylus: Affordable, scalable, and accurate {GNN} training with distributed {CPU} servers and serverless threads
A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …
A survey on distributed graph pattern matching in massive graphs
S Bouhenni, S Yahiaoui… - ACM Computing …, 2021 - dl.acm.org
Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it
impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed …
impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed …
Sisa: Set-centric instruction set architecture for graph mining on processing-in-memory systems
Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
Graph processing and machine learning architectures with emerging memory technologies: a survey
X Qian - Science China Information Sciences, 2021 - Springer
This paper surveys domain-specific architectures (DSAs) built from two emerging memory
technologies. Hybrid memory cube (HMC) and high bandwidth memory (HBM) can reduce …
technologies. Hybrid memory cube (HMC) and high bandwidth memory (HBM) can reduce …
Dimmining: pruning-efficient and parallel graph mining on near-memory-computing
Graph mining, which finds specific patterns in the graph, is becoming increasingly important
in various domains. We point out that accelerating graph mining suffers from the following …
in various domains. We point out that accelerating graph mining suffers from the following …
Peregrine: a pattern-aware graph mining system
K Jamshidi, R Mahadasa, K Vora - Proceedings of the Fifteenth …, 2020 - dl.acm.org
Graph mining workloads aim to extract structural properties of a graph by exploring its
subgraph structures. General purpose graph mining systems provide a generic runtime to …
subgraph structures. General purpose graph mining systems provide a generic runtime to …
Automine: harmonizing high-level abstraction and high performance for graph mining
D Mawhirter, B Wu - Proceedings of the 27th ACM Symposium on …, 2019 - dl.acm.org
Graph mining algorithms that aim at identifying structural patterns of graphs are typically
more complex than graph computation algorithms such as breadth first search. Researchers …
more complex than graph computation algorithms such as breadth first search. Researchers …
Accelerating graph sampling for graph machine learning using GPUs
Representation learning algorithms automatically learn the features of data. Several
representation learning algorithms for graph data, such as DeepWalk, node2vec, and Graph …
representation learning algorithms for graph data, such as DeepWalk, node2vec, and Graph …
Pangolin: An efficient and flexible graph mining system on cpu and gpu
There is growing interest in graph pattern mining (GPM) problems such as motif counting.
GPM systems have been developed to provide unified interfaces for programming …
GPM systems have been developed to provide unified interfaces for programming …
Graphpi: High performance graph pattern matching through effective redundancy elimination
Graph pattern matching, which aims to discover structural patterns in graphs, is considered
one of the most fundamental graph mining problems in many real applications. Despite …
one of the most fundamental graph mining problems in many real applications. Despite …