3 papers
cs.DC2023
Layph: Making Change Propagation Constraint in Incremental Graph Processing by Layering Graph
Song Yu, Shufeng Gong, Yanfeng Zhang +7
Real-world graphs are constantly evolving, which demands updates of the previous analysis results to accommodate graph changes. By using the memoized previous computation state, in…
cs.DC2022
HyTGraph: GPU-Accelerated Graph Processing with Hybrid Transfer Management
Qiange Wang, Xin Ai, Yanfeng Zhang +2
Processing large graphs with memory-limited GPU needs to resolve issues of host-GPU data transfer, which is a key performance bottleneck. Existing GPU-accelerated graph processing…
cs.LG2022
Learning Rate Perturbation: A Generic Plugin of Learning Rate Schedule towards Flatter Local Minima
Hengyu Liu, Qiang Fu, Lun Du +4
Learning rate is one of the most important hyper-parameters that has a significant influence on neural network training. Learning rate schedules are widely used in real practice to…