Monday, July 20th, 2009
How do you get type info in dynamic type land?
So let’s turn the problem around. If we let the program run for a bit in an interpreter, the engine can directly observe the types of values. Then, the engine can use those types to compile fast type-specialized code. Finally, the engine can start running the type-specialized code, and it will run much faster.
There are a few key details about this idea. First, when the program runs, even if there are many if statements and other branches, the program always goes only one way. So the engine doesn’t get to observe types for a whole method — the engine observes types through the paths, which we call traces, that the program actually takes. Thus, while standard compilers compile methods, TraceMonkey compiles traces. One side benefit of trace-at-a-time compilation is that function calls that happen on a trace are inlined, making traced function calls very fast.
Second, compiling type-specialized code takes time. If a piece of code is going to run only one or a few times, which is common with web code, it can easily take more time to compile and run the code than it would take to simply run the code in an interpreter. So it only pays to compile hot code (code that is executed many times). In TraceMonkey, we arrange this by tracing only loops. TraceMonkey initially runs everything in the interpreter, and starts recording traces through a loop once it gets hot (runs more than a few times).
Tracing only hot loops has an important consequence: code that runs only a few times won’t speed up in TraceMonkey. Note that this usually doesn’t matter in practice, because code that runs only a few times usually runs too fast to be noticeable. Another consequence is that paths through a loop that are not taken at all never need to be compiled, saving compile time.
Finally, above we said that TraceMonkey figures out the types of values by observing execution, but as we all know, past performance does not guarantee future results: the types might be different the next time the code is run, or the 500th next time. And if we try to run code that was compiled for numbers when the values are actually strings, very bad things will happen. So TraceMonkey must insert type checks into the compiled code. If a check doesn’t pass, TraceMonkey must leave the current trace and compile a new trace for the new types. This means that code with many branches or type changes tends to run a little slower in TraceMonkey, because it takes time to compile the extra traces and jump from one to another.
Then it gets practical, with examples of tracing in action, and even discussing what items are not traced yet:
- Recursion. TraceMonkey doesn’t see repetition that occurs through recursion as a loop, so it doesn’t try to trace it. Refactoring to use explicit
whileloops will generally give better performance.
- Getting or setting a DOM property. (DOM method calls are fine.) Avoiding these constructs is generally impossible, but refactoring the code to move DOM property access out of hot loops and performance-critical segments should help.
Before you optimize for that…. there is already work on the way to trace here too. Finally, we end up with a comparison of various JIT approaches, for example how Nitro/V8 do their thang.
A really nice article.
Posted by Dion Almaer at 1:06 pm