From what I understand, the purpose of the project was to amass an enormous database of code patterns and their SMT-proven more optimal replacement code. Is there also a project to apply the harvested optimizations against code?
Also, couldn't the same principle apply to compilers that emit LLVM bitcode, like GHC and rustc?
The immediate goal is simply to inspire LLVM hackers to improve the instruction combiner.
But yes, these results can be made persistent and used directly to optimize code. We are working on it.
Applying this tool to code emitted by GHC or rustc is trivial, it's all LLVM bitcode. One of my ideas is that these languages are a good use case for a superoptimizer since the LLVM passes aren't tuned for them, but we'll have to see how that plays out.
What's neat is that not all of these end up needing the InstCombine pass to get optimized, some of them can be handled using InstSimplify. InstSimplify isn't a pass, it's an analysis that get's called upon many times in the middle of other passes, strengthening them.
> Finally C-Reduce has missed some opportunities to fold constants, so for example we see ~1 instead of -2 in the 2nd example from the top.
Do you compile the code using clang -O<level> or optimize using opt? I would think that InstCombine would canonicalize that for you so Souper would have less work to do.
Sorry if I was unclear. The missing folds are at the C level. At the LLVM level we definitely run InstCombine before Souper (otherwise we would find lots of optimizations that are not actually missing).
I remember a paper on "Superoptimization" back in the 80's. It worked by starting with an algorithm, and doing an exhaustive search of every instruction sequence to see if the sequence implemented the algorithm. Of course, it only worked for very small algorithms, but it produced some very interesting results that were promptly incorporated into about every compiler.
SMT solvers sound interesting and this Python example looks very approachable.
Anyone know if there are other cookbook-type "here's how you solve practical problem x with SMT" recipes that might serve as a cargo cult-type (theory-oblivious) dive into SMT?
So, I find compiler construction and optimisation a fascinating topic, but have nowhere near enough knowledge to understand this beyond the surface... However, "Superoptimiser" is such a cool name.
Would it be possible to use these techniques on the various JITs that are used in Firefox/Cheomium? How hard would that be? The work you folks are doing is wonderful.
I did my Master's dissertation on superoptimization and the JVM. Tl;Dr managed to find shorter versions of simple Java math functions fairly easily, didn't prove they were any faster or better.
Also, couldn't the same principle apply to compilers that emit LLVM bitcode, like GHC and rustc?