Zhejiang Lab

Aimin Pan

Hongsheng Wang

Guang Chen

Lingfang Zeng

Disclosed are an intermediate representation method and apparatus for parallel execution of graph computation. The method includes the following steps: S: compiling a neural network into a computational graph on a computer; S: defining branch states of tensor variables in the computational graph; S: defining a data dependency relationship of the tensor variables in the computational graph; S: defining a control dependency relationship of the tensor variables in the computational graph; S: building a data dependency relationship graph of the tensor variables in the computational graph; S: building a control dependency relationship graph of the tensor variables in the computational graph; and S: transforming control dependencies into data dependencies. The present application derives, based on the dependency relationship, a parallel computing method that can execute the branch threads in parallel in the global computational graph, and optimizes the compilation efficiency of the computational graph.

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Hangzhou, China

Aimin Pan