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The pattern matching optimization of Polly detects and optimizes dense general matrix-matrix multiplication. The generated code is close to high performance implementations of matrix-matrix multiplications, which are contained in manually tuned libraries. The described pattern matching optimization is a particular case of tensor contraction optimization, which was introduced in [1]. This patch generalizes the pattern matching to the case of tensor contractions using the form of data dependencies and memory accesses produced by tensor contractions [1]. Optimization of tensor contractions will be added in the next patch. Following the ideas introduced in [2], it will logically represent tensor contraction operands as matrix multiplication operands and use an approach for optimization of matrix-matrix multiplications. [1] - Gareev R., Grosser T., Kruse M. High-Performance Generalized Tensor Operations: A Compiler-Oriented Approach // ACM Transactions on Architecture and Code Optimization (TACO). 2018. Vol. 15, no. 3. P. 34:1–34:27. DOI: 10.1145/3235029. [2] - Matthews D. High-Performance Tensor Contraction without BLAS // SIAM Journal on Scientific Computing. 2018. Vol. 40, no. 1. P. C 1—C 24. DOI: 110.1137/16m108968x. Reviewed By: Meinersbur Differential Revision: https://reviews.llvm.org/D114336