In numerous technologically important areas, such as aerodynamics (vehicle design), electrodynamics (semiconductor device design), magnetohydrodynamics (fusion energy device design), and porous media (petroleum recovery), production runs on expensive, high-end systems last for hours or days, and a major portion of the execution time is usually spent inside of numerical routines, such as for the solution of large-scale nonlinear and linear systems that derive from discretized systems of nonlinear partial differential equations. These numerical parts of the code can contain a large number of tuning parameters, the choice of which greatly influences the efficiency of the total code, or can even make the difference between obtaining a solution and obtaining none.

Such numerical concerns, however, are artifactual from the perspective of the scientific and engineering users, who are usually more concerned with modeling and discretization issues. The classic response to numerics was to encode the requisite mathematical, algorithmic and programming expertise into libraries that could be easily reused by a broad spectrum of domain scientists. However, in high-performance computing this solution is no longer sufficient. There is typically more than one algorithm for the stated purpose, and since several levels of algorithms are needed in a large-scale application; the different algorithms can have interlocking parameter settings, and the availability of parallel computing platforms influences algorithmic decisions. Since the difference in performance between an optimal choice of algorithm and hardware, and a less than optimal one, can span orders of magnitude, it is unfortunate that selecting the right solution strategy requires specialized knowledge of both numerical analysis and of computing platform characteristics. Our sans system aims to assist the application user to navigate this maze of computational possibilities.

This discrepancy in expertises can of course be bridged with manpower based solution: a large enough project can afford to hire a post-doc from computational science, or have a post-doc of its own cross-train in computational science. We believe, however, that it is possible have meta-software --~software operating on software~-- to deliver as good a solution, without the wasted manpower. Self-adapting Numerical Software (sans) systems have several levels on which they automate the computational choices for the application scientist: the algorithmic, network, and kernel levels. The *Self-Adapting Large-scale Solver Architecture (SALSA)* project tackles the algorithmic level, specifically for linear and nonlinear system solving.