Abstract: A method for generating a computation flow graph scheduling scheme includes grouping original vertexes in an original computation flow graph, so as to obtain first computation flow graphs; determining the number N of computing units required to process a single batch of computation data in parallel; copying N first computation flow graphs, so as to obtain second computation flow graphs; adding auxiliary vertexes to the second computation flow graphs, so as to obtain third computation flow graphs; constructing integer linear programming according to the third computation flow graphs; and solving the integer linear programming, so as to obtain a scheduling scheme for the third computation flow graphs. The method converts an original computation flow graph into third computation flow graphs and integer linear programming is constructed to obtain a scheduling scheme.

Abstract: An SIMD instruction generation and processing method and a related device are provided. The method may include: obtaining a length of each loop dimension of a first tensor formula; selecting, from a plurality of groups of information about a first SIMD instruction model based on the length of each loop dimension of a first tensor formula, information about a second SIMD instruction model matching the first tensor formula; generating, based on a length of at least one loop dimension of the first tensor formula and the second SIMD instruction model, a first SIMD instruction obtained after the first tensor formula is converted. The information about a second SIMD instruction model is selected from the plurality of groups of information about a first SIMD instruction model based on the length of each loop dimension of the tensor formula.

Abstract: An intermediate representation construction method is provided. The method includes: obtaining a first intermediate representation (IR), where the first IR includes a computing statement, the computing statement includes a tensor and an operator, an operation represented by the operator is performed by a computing unit, the computing unit is configured to perform an operation on data that is migrated through a first storage location and a second storage location sequentially, and the tensor is data that is used when the operation represented by the operator is performed; and generating a second IR based on the computing statement, where the second IR includes first data migration information and data segmentation information.

Abstract: An SIMD instruction generation and processing method and a related device are provided. The method may include: obtaining a length of each loop dimension of a first tensor formula; selecting, from a plurality of groups of information about a first SIMD instruction model based on the length of each loop dimension of a first tensor formula, information about a second SIMD instruction model matching the first tensor formula; generating, based on a length of at least one loop dimension of the first tensor formula and the second SIMD instruction model, a first SIMD instruction obtained after the first tensor formula is converted. The information about a second SIMD instruction model is selected from the plurality of groups of information about a first SIMD instruction model based on the length of each loop dimension of the tensor formula.

Abstract: The present disclosure relates to neural network model processing methods. One example method includes obtaining an operation process of a neural network model, where the operation process is represented by at least one first-type operator and a plurality of second-type operators, and obtaining a first computation graph of the neural network model based on the operation process. In the operation process, the first-type operator includes a boundary identifier, and computational logic of the first-type operator is represented by a group of second-type operators. For any first-type operator, a range of second-type operators included in the any first-type operator is indicated by a boundary identifier in the any first-type operator.

Abstract: An intermediate representation construction method is provided. The method includes: obtaining a first intermediate representation (IR), where the first IR includes a computing statement, the computing statement includes a tensor and an operator, an operation represented by the operator is performed by a computing unit, the computing unit is configured to perform an operation on data that is migrated through a first storage location and a second storage location sequentially, and the tensor is data that is used when the operation represented by the operator is performed; and generating a second IR based on the computing statement, where the second IR includes first data migration information and data segmentation information.