Compiling method, compiling device, electronic equipment, storage medium and program product

Abstract

The invention provides a compiling method, a compiling device, electronic equipment, a storage medium and a program product, which can be used in the fields of source file compiling, deep learning and the like. The method comprises the following steps: acquiring a source file set, wherein the source file set comprises at least one type of source file; obtaining a directory set of an object set associated with the source file set; for a first directory in the directory set, combining source files associated with a first object set into a first source file subset on the basis that the first directory comprises the first object set associated with the source files of the first type; compiling the first source file subset as a whole. By utilizing the method, the compiling efficiency can be effectively improved and the compiling time consumption can be effectively reduced by reducing the number of times of compiling the source file, so that the user experience can be improved.

Description

technical field [0001] The present disclosure relates to computer technology, and more specifically, to a compilation method, a compilation device, an electronic device, a computer-readable storage medium, and a computer program product, which can be used in the fields of source file compilation and deep learning. Background technique [0002] Deep learning is a key technology in the field of artificial intelligence, and developers usually carry out related work based on the deep learning framework. The basis of deep learning is artificial neural network, and the core of artificial neural network is matrix operation, so the core of a deep learning framework is matrix operation. The deep learning system is a computing-intensive system, which often involves a large number of highly complex matrix calculations. The smooth operation of the system often requires high processing speed. The central processing unit cannot even meet the needs of matrix calculations. To achieve fast ...

AI Technical Summary

Problems solved by technology

However, due to the complex compilation process of CUDA, CUDA compilation in large-scale projects is often time-consuming, which affects R&D efficiency

[0004] However, traditional techniques for compiling source files either need to be compiled separately for each source file, or can only merge parts of the source files and compile them very roughly, and basically cannot handle CUDA source files

Therefore, the number of compilations required for projects using CUDA source files is large, and cannot be applied to large projects with many source files

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