Dec 6, 2017 · We proposed a novel node-level parallelization, conditional independent parallelization, of the forward and backward propagations to improve the ...
We proposed a novel node-level parallelization, conditional independent parallelization, of the forward and backward propagations to improve the level of ...
Node-level parallelization Deep neural networks Conditional independent graph OpenMP Concurrent kernels. ISSN号, 0925-2312. DOI, 10.1016/j.neucom.2017.06.002.
In GNNs, if data samples are independent graphs, then mini-batch parallelism is similar to traditional deep learn- ing. First, one mini-batch is a set of ...
Missing: conditional | Show results with:conditional
ABSTRACT. Existing deep learning systems commonly parallelize deep neural network (DNN) training using data or model parallelism, but these strategies often ...
Duration: 1:30:56
Posted: Dec 18, 2022
Posted: Dec 18, 2022
Missing: Node- level conditional independent
In this paper, we propose a network-agnostic and convergence-invariant light-weight parallelization framework, namely GLP4NN, to accelerate the training of ...
Jun 8, 2024 · In this article, we design TLPGNN, a lightweight two-level parallelism paradigm for GNN computation. First, we conduct a systematic analysis of the hardware ...
Missing: conditional | Show results with:conditional
Good practice in node-level tasks is to create an MLP baseline that is applied to each node independently. This way we can verify whether adding the graph ...
Missing: conditional | Show results with:conditional
In GNNs, if data samples are independent graphs, then mini- batch parallelism is similar to traditional deep learning. First, one mini-batch is a set of ...
Missing: conditional | Show results with:conditional