In the JS world, tracing was abandoned because it didn't scale to real world code.
JS JITs (the production ones, like JSC's) have no such thing as trace blockers that prevent the surrounding code from being optimized. You might have an operation (like a call to some wacky native function) that is itself not optimized, but that won't have any impact on the JIT's ability to optimize the code surrounding that operation.
Tracing is just too much of a benchmark hack overall IMO. Tracing would only be a good idea in a world where it's too expensive to run a real optimizing JIT. But the JS, Java, and .NET experiences show that a real optimizing JIT - with all of its compile time costs - is exactly what you want because it results in predictable speed-ups
Pypy's tracing JIT has worked pretty well for years, so that doesn't seem universal. I admit I have fairly low expectations on a JIT succeeding (in any meaningful fashion) in cpython given the constraints that it currently has, but I'm generally a skeptic, so maybe i'm being overly pessimistic.
What does “worked pretty well” really mean though?
When we talk about JS or Java JITs working well, we are making statements based on intense industry competition where if a JIT had literally any shortcoming then a competitor would highlight it in competitive benchmarking and blog posts. So, the competition forced aggressive improvements and created a situation where the top JITs deliver reliable perf across lots of workloads.
OTOH PyPy is awesome but just hasn’t had to face that kind of competitive challenge. So we probably can’t know how far off from JS JITs it is.
One thing I can say is when I compared it to JSC by writing the same benchmark in both Python and JS, JSC beat it by 4x or so.
I think the Java JITs are a better comparison because the workload is more similar: JavaScript is weird for how it’s expected to start in a fraction of a second and soak up a huge bolus of code which may substantially never be used whereas most of the performance-sensitive Python code stabilizes quickly and loads what it uses really early on.
The Java JIT and most other Javascript jits are essentially operating the same way. The core difference is the java language spec sets up a whole bunch of requirements that need to be figured out at startup and are easy to trigger.
For example, static initialization on classes. The JDK has a billion different classes and on startup a not insignificant fraction of those end up getting loaded for all but the simplest applications.
Essentially, Java and the JS jits are both initially running everything interpreted and when a hot method is detected they progressively start spending the time sending those methods and their statistics to more aggressive JIT compilers.
A non-insignificant amount of time is being spent to try and make java start faster and a key portion of that is resolving the class loading problem.
Yes, my thought was that the lifecycle is different. The average JVM is probably running for days on average so a huge percentage of the total runtime is in code which had been aggressively optimized by the JIT whereas a lot of JavaScript isn’t used enough to reach that point so their respective developers are going to have different tuning goals. I’d expect Python to be closer to Java in that regard, with some harder to optimize features than Java but less than JavaScript owing to the richer language and better typing.
I don't know how JSC handles it, but in SM `eval` has significant negative effects on surrounding code. (We also decline to optimize functions containing `with` statements, but that's less because it's impossible and more because nobody uses them.)
Last I saw (and I admit this is pretty dated) V8 was doing the same thing. try/catch at one point in V8 would cause the surrounding method to be deoptimized.
Yeah, SM will compile functions with try/catch/finally, but we don't support unwinding directly into optimized code, so the catch block itself will not be optimized.
From what I understand, Julia doesn’t do any tracing at all, it just compiles each function based on the types it receives. Obviously Python doesn’t have multiple dispatch, but that actually might make compilation easier. Swap out the LLVM step with python's IR and they could probably expect a pretty substantial performance improvement. That said I don’t know anything about compilers, I just use both Python and Julia.
This is called a method based JIT, and is generally the more common approach to JIT compilation. Tracing JIT is a deliberate design choice that is quite different from method based JITs
IIRC Julia's is a particularly simple method-based JIT for a dynamically-typed language.
I'm not sure exactly how it differs from most JavaScript JITs, but I believe it just compiles each method once for each set of function argument types - for example, it doesn't try to dynamically determine the types of local variables.
> Knowledge transfer worked in both ways: I learned a lot about the internal details of CPython's JIT, and conversely I shared with them some of the experience, pain points and gut feelings which I got by working many years on PyPy.
Can cross fertilization between PyPy and CPython JIT efforts help already fast PyPy to get even faster? Like, did CPython JIT team try something PyPy developers didn't attempt before?
In the JS world, tracing was abandoned because it didn't scale to real world code.
JS JITs (the production ones, like JSC's) have no such thing as trace blockers that prevent the surrounding code from being optimized. You might have an operation (like a call to some wacky native function) that is itself not optimized, but that won't have any impact on the JIT's ability to optimize the code surrounding that operation.
Tracing is just too much of a benchmark hack overall IMO. Tracing would only be a good idea in a world where it's too expensive to run a real optimizing JIT. But the JS, Java, and .NET experiences show that a real optimizing JIT - with all of its compile time costs - is exactly what you want because it results in predictable speed-ups
Pypy's tracing JIT has worked pretty well for years, so that doesn't seem universal. I admit I have fairly low expectations on a JIT succeeding (in any meaningful fashion) in cpython given the constraints that it currently has, but I'm generally a skeptic, so maybe i'm being overly pessimistic.
What does “worked pretty well” really mean though?
When we talk about JS or Java JITs working well, we are making statements based on intense industry competition where if a JIT had literally any shortcoming then a competitor would highlight it in competitive benchmarking and blog posts. So, the competition forced aggressive improvements and created a situation where the top JITs deliver reliable perf across lots of workloads.
OTOH PyPy is awesome but just hasn’t had to face that kind of competitive challenge. So we probably can’t know how far off from JS JITs it is.
One thing I can say is when I compared it to JSC by writing the same benchmark in both Python and JS, JSC beat it by 4x or so.
I think the Java JITs are a better comparison because the workload is more similar: JavaScript is weird for how it’s expected to start in a fraction of a second and soak up a huge bolus of code which may substantially never be used whereas most of the performance-sensitive Python code stabilizes quickly and loads what it uses really early on.
The Java JIT and most other Javascript jits are essentially operating the same way. The core difference is the java language spec sets up a whole bunch of requirements that need to be figured out at startup and are easy to trigger.
For example, static initialization on classes. The JDK has a billion different classes and on startup a not insignificant fraction of those end up getting loaded for all but the simplest applications.
Essentially, Java and the JS jits are both initially running everything interpreted and when a hot method is detected they progressively start spending the time sending those methods and their statistics to more aggressive JIT compilers.
A non-insignificant amount of time is being spent to try and make java start faster and a key portion of that is resolving the class loading problem.
Yes, my thought was that the lifecycle is different. The average JVM is probably running for days on average so a huge percentage of the total runtime is in code which had been aggressively optimized by the JIT whereas a lot of JavaScript isn’t used enough to reach that point so their respective developers are going to have different tuning goals. I’d expect Python to be closer to Java in that regard, with some harder to optimize features than Java but less than JavaScript owing to the richer language and better typing.
I don't know how JSC handles it, but in SM `eval` has significant negative effects on surrounding code. (We also decline to optimize functions containing `with` statements, but that's less because it's impossible and more because nobody uses them.)
Last I saw (and I admit this is pretty dated) V8 was doing the same thing. try/catch at one point in V8 would cause the surrounding method to be deoptimized.
Yeah, SM will compile functions with try/catch/finally, but we don't support unwinding directly into optimized code, so the catch block itself will not be optimized.
JSC will still JIT optimize functions that use eval.
It’s true that there are some necessary pessimizations but nothing as severe as failing to optimize the code at all
I wonder how much Julia’s JIT could help inform Python’s.
Diagram: https://docs.julialang.org/en/v1/devdocs/img/compiler_diagra...
Documentation: https://docs.julialang.org/en/v1/devdocs/eval/
From what I understand, Julia doesn’t do any tracing at all, it just compiles each function based on the types it receives. Obviously Python doesn’t have multiple dispatch, but that actually might make compilation easier. Swap out the LLVM step with python's IR and they could probably expect a pretty substantial performance improvement. That said I don’t know anything about compilers, I just use both Python and Julia.
This is called a method based JIT, and is generally the more common approach to JIT compilation. Tracing JIT is a deliberate design choice that is quite different from method based JITs
IIRC Julia's is a particularly simple method-based JIT for a dynamically-typed language.
I'm not sure exactly how it differs from most JavaScript JITs, but I believe it just compiles each method once for each set of function argument types - for example, it doesn't try to dynamically determine the types of local variables.
> Knowledge transfer worked in both ways: I learned a lot about the internal details of CPython's JIT, and conversely I shared with them some of the experience, pain points and gut feelings which I got by working many years on PyPy.
Can cross fertilization between PyPy and CPython JIT efforts help already fast PyPy to get even faster? Like, did CPython JIT team try something PyPy developers didn't attempt before?
PyPy is awesome, btw.
If you can't do it , let the experts do that job and adopt it. pypy had done that for ages and works very well