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Look up work by Stefan Brunthaler, like this overview paper ("Virtual-Machine Abstraction and Optimization Techniques"): https://www.sba-research.org/wp-content/uploads/publications...

Make sure to check out the references. I especially like "Ertl, M. A. and D. Gregg, The structure and performance of efficient interpreters, Journal of Instruction-Level Parallelism 5 (2003)"

There goes the weekend...

Do you know of any recent efforts to combine ML and VM optimizations ?

(I read about the VS compiler and profile-based optimizations)

This one is good: http://www.tara.tcd.ie/bitstream/handle/2262/64047/Optimizin...

Though you might not think of hill climbing as "machine learning".

Those techniques are a relatively niche thing. People don't write bytecode interpreters daily. Couple of months ago I wrote a few articles on the topic. Unfortunately, they are in Russian. I'll probably translate 'em at some but for now...

the repo and performance measurements are on Github[1]. It includes a couple of dispatch techniques, register caching, etc.

[1]: https://github.com/vkazanov/bytecode-interpreters-post

article below contains an overview and more pointers.

http://realityforge.org/code/virtual-machines/2011/05/19/int...