Vectorizing optimization method based on MIC (Many Integrated Core) architecture processor

Abstract

The invention provides a vectorizing optimization method based on an MIC architecture processor, which relates to three main steps, i.e. data dependence analysis for an algorithm, vectorizing adjustment and optimization of the algorithm and vectorizing compilation. The specific content includes data dependence analysis for the algorithm, the vectorizing optimization and adjustment of the algorithm, an automatic vectorization technique of a compiler, a user-intervened vectorizing optimization method and the like. The method provided by the invention is applicable to the software optimization of an MIC architecture processor platform, and can direct software developers to rapidly and efficiently carry out the vectorizing optimization of existing software, particularly core algorithms, within the shortest development cycles and with the lowest costs, consequently, the computing resources of vector processors can be utilized by software to the max, the running time of software can be shortened to the max, the hardware resource utilization rate can be remarkably increased, and the computing efficiency and overall performance of software can be increased.

Description

technical field [0001] The invention relates to the field of computer high-performance computing and the field of scientific computing, in particular to a vectorization optimization method based on an MIC architecture processor. Background technique [0002] Since Intel integrated MMX on the Pentium processor in 1996, more and more general-purpose processors have integrated SIMD (Single Instruction Multiple Data, Single Instruction Multiple Data) hardware extensions. processor, called a vector processor. The application of vector processors is also becoming more and more extensive, from the initial multimedia application to various application fields, especially in the field of high-performance computing, massive data and large-scale parallel processing requirements pose severe challenges to the computing power of processors, vectorization Parallel processing can effectively improve parallel processing efficiency and computing density, improve hardware resource utilization,...

AI Technical Summary

Problems solved by technology

[0004] With the promotion of MIC architecture processors, its powerful SIMD extension technology will be widely used, which not only provides new ways to solve problems and improve performance for high-performance computing, but also brings a new problem - how to quickly, Efficiently implement reliable vectorized parallel processing to fully unleash the computing potential of MIC processors? This is the real challenge facing software engineers

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