计算平台的快速发展、日益复杂和变化多端对科学计算提出了一个核心问题：如何用合适的代价实现可移植的最优性能？为了实现乘加融合结构向量处理器的向量化代码自动生成、性能最优化和平台自适应，本项目拟通过张量积理论设计数字信号变换算法的特定数学结构和代数规则，研究FMA变换算法、并行和向量化算法的一般方法，以及向量化代码的自动生成、自动调优和评估算法。设计和综合出一套基于张量积的自适应、自动调优和高效能的向量化代码生成机制和实现算法，并通过实验测试和理论分析的手段评价和比较所设计机制与算法的性能。

Fast changing, increasingly complex, and diverse computing platforms pose central problems in scientific computing: How to achieve, with reasonable effort, portable optimal performance? In order to achieve the automatic vectorization code generation, performance optimization and platform adaption for vector processor of fused multiply-add architecture, this project intends to design specific mathematical structures and algebraic rules of digital signal transform algorithm by the tensor product, studys the general method to convert any transform algorithm into an FMA algorithm, parallelization and vectorization algorithms,as well as to automatically generate vectorization code, automatic tuning and evaluation algorithm. Design and synthesize a set of adaptive automatic tuning and high performance vectorization code generation mechanism and implementation algorithm based on tensor product, and evaluate and compare the performance of the mechanisms and algorithms by experimental tests and theoretical analysis.