Superoptimization is the process where a compiler automatically finds the optimal sequence for a loop-free sequence of instructions. Real-world compilers generally cannot produce genuinely optimal code, and while most standard compiler optimizations only improve code partly, a superoptimizer's goal is to find the optimal sequence, the canonical form. Superoptimizers can be used to improve conventional optimizers by highlighting missed opportunities so a human can write additional rules.
The term superoptimization was first coined by Alexia Massalin in the 1987 paper Superoptimizer: A Look at the Smallest Program. [1] The label "program optimization" has been given to a field that does not aspire to optimize but only to improve. This misnomer forced Massalin to call her system a superoptimizer, which is actually an optimizer to find an optimal program. [2]
In 1992, the GNU Superoptimizer (GSO) was developed to integrate into the GNU Compiler Collection (GCC). [3] [4] Later work further developed and extended these ideas.
Traditionally, superoptimizing is performed via exhaustive brute-force search in the space of valid instruction sequences. This is a costly method, and largely impractical for general-purpose compilers. Yet, it has been shown to be useful in optimizing performance-critical inner loops. It is also possible to use a SMT solver to approach the problem, vastly improving the search efficiency (although inputs more complex than a basic block remains out of reach). [5]
In 2001, goal-directed superoptimizing was demonstrated in the Denali project by Compaq research. [6] In 2006, answer set declarative programming was applied to superoptimization in the Total Optimisation using Answer Set Technology (TOAST) project [7] at the University of Bath. [8] [9]
Superoptimization can be used to automatically generate general-purpose peephole optimizers. [10]
Several superoptimizers are available for free download.
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