High-performance architectures have complex features so that reliable production of parallel software is beyond the reach of many Computer Science graduates. Compilers alone cannot guarantee the highest performance and multiple APIs with complex performance features are difficult to master. As a first step towards more comprehensive solutions we are building key elements of a pre-compiler system that will automatically produce predictable, scalable and high-performance code from declarative tensor expressions. In this paper we summarize and analyze a large set of timing experiments of matrix multiplication variants that are mapped to vectorized and multithread code. The analysis covers two high-end target architectures and exhaust a whole space of code, compiler, pragma and parallelism parameters. Our analysis shows how the best choice of parameters is produced from a small set of tests that can converge in a matter of seconds and then predict performance of larger instances to within 25% or much less. Inefficient choices of parameters is also shown to be reliably predicted from small tests, so that our design for a precompiler is guaranteed to be a realistic and portable tool. The generality of our Mathematics of Arrays tensor algebra, and very broad applicability of tensor operations (signal processing, scientific computing, AI, etc.) supports our claim that these experiments and design can be generalized to a general purpose parallel programming tool.