Microsoft-3D-Movie-Maker VS blog

https://github.com/microsoft/Microsoft-3D-Movie-Maker Microsoft open sourced it just a few months ago.

This isn’t exactly legacy windows kernel code but it’s likely similar in terms of structure and idioms https://github.com/microsoft/Microsoft-3D-Movie-Maker

It is flawed for (otherwise) veterans too if they are trying to do something that they haven't done before so do not know what they do not know.

This really only works if you have done the same or something very very similar before and so you have practically no unknowns. Notice that the developer who did that commented above that they had already done similar work at the past.

Also related this quote about how Joe Armstrong (of Erlang fame) approached problems (from [0]):

> Joe wrote amazingly simple programs and he did so in a peculiar way. First he wrote down the program any old way just to get it out of his head. Then once it worked he would then immediately create a new directory program2 and write it again. He would repeat this process five or six times (program5, program6, ...) and each time he would understand the problem a little better and sense which parts of the program were essential enough to re-type. He thought this was the most natural thing in the world: of course you throw away the first few implementations, you didn't understand the problem when you wrote those!

[0] https://github.com/lukego/blog/issues/32

Eh, with the benefit of 14 years of hindsight, I want to push back on some of the things in that talk. (Context: I work on SpiderMonkey.)

First, all the stuff about tracing and trace trees is kind of obsolete. SpiderMonkey abandoned TraceMonkey a long time ago. (To the best of my knowledge, V8 never implemented a tracing JIT at all.) The problem with tracing is that you can get really good performance when everything goes right, but it's brittle. There's a reference in the talk to how implementations of the Game of Life can have exponential blow-up, for example. You can usually fix any individual pathological case, but the inherently exponential number of possible paths through a program make it difficult to completely eliminate weird performance cliffs.

If your goal is to maximize performance on a known set of benchmarks, go wild. If you want a robust engine that can handle whatever weird code the web is throwing at you, tracing JITs are (as far as I can tell) a dead end.

(Counterpoint: LuaJIT seems to be doing alright with tracing, although it may just solve the problem by punting to the programmer: https://github.com/lukego/blog/issues/29. That's more feasible when you don't have multiple engines with performance cliffs in subtly different places.)

Second, the idea that JIT-compiled code can be faster than AOT-compiled code has been floating around for a long time, but I don't think it really holds in the general case. Doing work at runtime isn't free: not just time spent compiling, but also time spent profiling and validating that your speculative optimizations continue to be correct.

SpiderMonkey had a top-tier optimizing compiler, IonMonkey, that got pretty darn close to native code on hot benchmark loops. We tracked whole-program information to ensure that type checks could be elided in inner loops. (For example, if the `x` property of a certain set of objects only ever contained 32-bit integers, then we could unbox it without checking the type. If any code elsewhere ever stored a non-integer value in that property, we would notice and invalidate the optimized code.)

We threw IonMonkey away, because it was too brittle. In practice, real-world code falls off the happy path often enough that we got better performance by accepting that even your highly optimized JS code will include some runtime checks. Invalidation and recompilation are real costs. So is the upkeep of all the global data necessary to support Ion. There's an engineering tradeoff between pushing up the performance ceiling and bringing up the performance floor; we've been happy with our choice to shift focus more towards the latter. Our numbers are down on artificial benchmarks, but it seems to have paid off in real-world performance. (Also, bugs in the optimizing compiler are significantly less likely to be exploitable.)

A lot of really smart people have done some incredible work on the JVM. Nevertheless, I'm still not aware of any code written in Java instead of (say) C++ or Rust because Java was faster. I think it's more accurate to say that JIT compilation can be fast enough that the other advantages of the language can make it the right choice.