Pyston Continues Working On Performance Optimizations

Pyston, the Dropbox-backed open-source Python implementation that leverages LLVM for greater performance, is continuing to tweak its implementation for maximum performance potential.

In a blog post published last week, Marius Wachtler covered their work on landing caching object code into Pyston. Marius explained, "A lot of people are under the impression that LLVM is not ready to be used as a JIT because of the main focus as a static compiler where fast code generation time is not as important as in the JIT usage. While I agree that an LLVM JIT is quite expensive compared to baseline JIT tiers in other projects we expect to partly mitigate this and at the same time still take advantage of the good code quality and advanced features LLVM provides....We noticed that from the 1.4secs JITing functions about 1.1secs are spend on optimizing and lowering the LLVM IR to machine code (instruction selection, register allocation, etc) and only a much smaller amount of time is spend generating the LLVM IR. We then thought that the best solution is to cache the generated machine code to disk in order to reuse it the next time we encounter the same function (e.g. on the next startup)."

At the end of this latest optimization effort, "The result is that we cut the time to JIT the functions down to 350ms (was 1.4secs) of those merely 60ms are actually spend hashing the IR, decompressing and loading the object code and relocating the pointers (down from 1.1secs)."

More details via the Pyston blog.