自1980年以来，DSP芯片的不断发展使其在多个领域得以成熟应用。而在实际工业应用中，一个好的算法往往需要经过大量优化才能满足系统日益提高的性能要求。本文先使用Linux平台下的性能分析工具gprof对代码整体结构进行剖析，再基于分析结果进行有针对性有侧重点的优化。其中所选优化方法适用于大部分DSP编程环境，包括编译器选项优化、Intrinsic函数优化、循环体优化和存储空间分配优化。以混合激励线性预测（ MELP ）语音编解码算法为例进行说明，由实验结果可以看出，这种优化方法比普遍优化效率更高，效果更好。%At present, the sustainable development of DSP chip has made it been applied in many fields maturely since 1980. Inpractical industrial applications, a good algorithm always tends to go through lots of system optimization to meet the ever⁃increasingperformance requirements. Performance analysis tool gprof of Linux platform is used to analyze the overall structure of the programfirstly, and then based on the analysis result more focused and targeted optimization is implemented. The optimization methodsinclude complier option optimization, instrinsic functions optimization, loop optimization and storage space allocation optimization.In this paper, MELP(Mixed⁃Excitation Linear Prediction) speech codec is taken as an example to illustrate. As it can be seen fromthe experimental results, this kind of optimization method has higher efficiency and better effects compared with general optimization.
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