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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 blocke
by pizlonator 1y ago
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
- marky1991 1y agoPypy'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.
- pizlonator 1y agoWhat 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.
- acdha 1y agoI 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.
- cogman10 1y agoThe 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.
- acdha 1y agoYes, 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.
- cogman10 1y agoSure. I guess I'd point out that even when the long lifecycle, the optimization on live paths happens quick. It doesn't take too many invocations before the jvm optimizes. That's similar to how js does things. Java does have a "client" optimization mode for more short lived operations (like guis for example) and AFAIK it's basically unused at this point. The more aggressive "server" optimizations are faster than ever and get triggered pretty aggressively now. The nature of the jvm is also changing. With fast scaling and containerization, a slow start and long warmup aren't good. That's why part of the jdk dev has been dedicated to resolve that.
- pjmlp 1y ago
- IainIreland 1y agoI 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.)
- cogman10 1y agoLast 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.
- IainIreland 1y agoYeah, 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.
- pizlonator 1y agoJSC 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
- ksec 1y agoI remember Mozilla tried Tracing JIT with TraceMonkey and ultimately loss to v8. But LuaJIT is also Tracing JIT which seems to work well enough.
- pizlonator 1y agoLuaJIT has no V8-like competitor that runs Lua Ive heard that LuaJIT has Pre stable perf than Mozilla’s tracing JIT had and I’ve heard plenty of stories about how flaky LuaJIT’s performance is. But we can’t know how good it really is due to lack of competitors