Redundancy-free high-performance dynamic GNN training with hierarchical pipeline parallelism
Temporal Graph Neural Networks (TGNNs) extend the success of Graph Neural Networks to
dynamic graphs. Distributed TGNN training requires efficiently tackling temporal …
dynamic graphs. Distributed TGNN training requires efficiently tackling temporal …
MVC: Enabling Fully Coherent Multi-Data-Views through the Memory Hierarchy with Processing in Memory
D Fujiki - Proceedings of the 56th Annual IEEE/ACM …, 2023 - dl.acm.org
Fusing computation and memory through Processing-in-Memory (PIM) provides a radical
solution to the memory wall problem by minimizing communication overheads for data …
solution to the memory wall problem by minimizing communication overheads for data …
Redundancy-free and load-balanced TGNN training with hierarchical pipeline parallelism
Recently, Temporal Graph Neural Networks (TGNNs), as an extension of Graph Neural
Networks, have demonstrated remarkable effectiveness in handling dynamic graph data …
Networks, have demonstrated remarkable effectiveness in handling dynamic graph data …
SoK: The Faults in our Graph Benchmarks
Graph-structured data is prevalent in domains such as social networks, financial
transactions, brain networks, and protein interactions. As a result, the research community …
transactions, brain networks, and protein interactions. As a result, the research community …
IntelliBeeHive: An Automated Honey Bee, Pollen, and Varroa Destructor Monitoring System
CI Narcia-Macias, J Guardado, J Rodriguez… - arXiv preprint arXiv …, 2023 - arxiv.org
Utilizing computer vision and the latest technological advancements, in this study, we
developed a honey bee monitoring system that aims to enhance our understanding of …
developed a honey bee monitoring system that aims to enhance our understanding of …