Task mapping method, task processing method, processing core and electronic equipment

Abstract

The invention provides a task mapping method which comprises the following steps: determining multiple layers of second matrixes according to a first matrix, and dividing each layer of second matrix into at least one second sub-matrix; wherein elements in the (N + 1) th layer of second matrix are in one-to-one correspondence with a plurality of Nth layer of second sub-matrixes, and the first layer of second sub-matrixes are sub-matrixes of the first matrix; determining an operation instruction corresponding to each second sub-matrix; mapping at least one second sub-matrix in each layer of second matrix and the operation instruction corresponding to the second sub-matrix to a plurality of processing cores in the many-core system, each processing core corresponding to one second sub-matrix and the corresponding operation instruction, the processing core calculates the transpose of the corresponding second sub-matrix according to the corresponding operation instruction and stores the calculation result; wherein N is a positive integer. The invention further provides a task processing method, a processing core and electronic equipment.

Description

technical field [0001] The present disclosure relates to the field of computer technology, and in particular, to a task mapping method, a task processing method, a processing core, and an electronic device. Background technique [0002] A sparse matrix refers to a matrix in which the number of zero elements is much larger than the number of non-zero elements, and the distribution of non-zero elements is irregular. Sparse matrices are widely used in real life. In particular, sparse matrices often appear in high-performance computing and machine learning, such as data containing counts, data encoding mapping categories, and machines such as natural language processing (NLP). subfield of study. [0003] When performing operations such as transposing a sparse matrix to store a sparse matrix, it is necessary to store the value of each element in the sparse matrix and its coordinates in the sparse matrix. However, in some related technologies, when the scale of the sparse matrix...

AI Technical Summary

Problems solved by technology

However, in some related technologies, when the scale of the sparse matrix is ​​large, the sparse matrix needs to occupy more storage space, and the amount of calculation is large, and the efficiency of the operation involving the sparse matrix is ​​low.

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