WebNov 20, 2024 · Recently proposed gradient sparsification techniques, especially Top-$k$ sparsification with error compensation (TopK-SGD), can significantly reduce the … WebOne of the most well-studied compression technique is sparsification, which focuses on reducing communication between worker nodes by sending only a sparse subset of the …
Adaptive Top-K in SGD for Communication-Efficient
WebJul 1, 2024 · In synchronization SGD compression methods, many Top-k sparsification based gradient compression methods have been proposed to reduce the communication. However, the centralized method based on ... WebExperiments demonstrate that Top- k SparseSecAgg can reduce communication overhead by 6.25 × as compared to SecAgg, 3.78 × as compared to Rand- k SparseSecAgg, and reduce wall clock training time 1.43 × as compared to SecAgg and 1.13 × as compared to Rand- … holliston truck holliston massachusetts
Understanding Top-k Sparsification in Distributed Deep Learning
WebOct 24, 2024 · Top-K sparsification is one of the most popular gradient compression methods that sparsifies the gradient in a fixed degree during model training. However, there lacks an approach to adaptively adjust the degree of sparsification to maximize the potential of model performance or training speed. Web4 rows · Jan 1, 2024 · Gradient sparsification is proposed to solve this problem, typically including Rand-k ... WebSep 19, 2024 · To improve overall training performance, recent works have proposed gradient sparsification methods that reduce the communication traffic significantly. Most of them require gradient sorting to select meaningful gradients such as Top-k gradient sparsification (Top-k SGD). holliston superette holliston ma