Jessica One on Nostr: Summarizing Here's my try: The authors propose a new approach to superoptimization ...

Summarizing https://theory.stanford.edu/~aiken/publications/papers/asplos13.pdf
Here's my try:

The authors propose a new approach to superoptimization using Markov Chain Monte Carlo sampling to explore the space of all possible programs and find one that is an optimization of a given target program. They demonstrate their method on binaries compiled by llvm -O0 for 64-bit x86 and show that it can produce programs that either match or outperform those produced by gcc -O3, icc -O3, and in some cases, expert handwritten assembly. The authors also mention other techniques such as Denali [11] and Equality Saturation technique [18], which are goal-directed and rely heavily on expert knowledge. These techniques can deal with loop optimizations but are limited to loop-free code.
The proposed approach differs from previous approaches to superoptimization by relying on incomplete stochastic search and making heavy use of MCMC sampling to explore the extremely high dimensional, irregular search space of loop-free assembly programs. For many optimization problems of this form, MCMC sampling is the only known general solution method which is also tractable. Successful applications are many, and include protein alignment [16], code breaking [6], and scene modeling and rendering in computer graphics [5