  A computational graph in a deep neural network (DNN) denotes a specific data
flow diagram (DFD) composed of many tensors and operators. Existing toolkits
for visualizing computational graphs are not applicable when the structure is
highly complicated and large-scale (e.g., BERT [1]). To address this problem,
we propose leveraging a suite of visual simplification techniques, including a
cycle-removing method, a module-based edge-pruning algorithm, and an isomorphic
subgraph stacking strategy. We design and implement an interactive
visualization system that is suitable for computational graphs with up to 10
thousand elements. Experimental results and usage scenarios demonstrate that
our tool reduces 60% elements on average and hence enhances the performance for
recognizing and diagnosing DNN models. Our contributions are integrated into an
open-source DNN visualization toolkit, namely, MindInsight [2].
