Tamarin Tracing, Intro to Tracing JITs

I’ve been porting Tamarin-Tracing‘s code generator to AMD64. The idea behind Tamarin-Tracing is that you have an interpreter for some dynamically typed language, or an environment where a fully optimizing JIT would be too expensive.

As the interpreter runs, it will decide to start tracing sequences of code that could be “hot paths” for performance. For example, loops are good candidates. Once tracing begins, the interpreter captures the state of every instruction and emits it as a “trace.” A trace represents the exact sequence that was taken through a particular control path.

A good example could be a method call. For example, “obj.crab()” in languages such as JavaScript or Python requires a dynamic lookup, since the type of “obj” is only known at run-time. You can emit a trace like this:

LET x = TYPEOF(obj)
TRACE:
   IF TYPEOF(obj) != x
      EXIT
   ELSE
      CALL x::crab ON obj

This trace says, “Assume object is type X. If it’s not, we’ll have to recompute it, but if it is, we can optimize this to a direct method call.” Later, the trace will be analyzed, optimized, and then compiled to assembly. Next time the interpreter needs to call the method, it will see that a compiled trace exists. The compiled trace will first check that the type of “obj” matches the path it originally took. If not, it will exit back to the interpreter. This check is called a “guard” and the exit is called a “side exit.” Otherwise, the optimized method call is invoked. A side exit can later turn into another trace; these branches form a “trace tree.”

Anything can be traced. For example, one side of an “if” branch can be traced. The resulting compiled code would be for the “hot” side of the branch. If the “cold” side is detected, it would jump back to the interpreter and a new trace might branch off. Another case is addition for weakly typed languages. For example, “5 + '3'” is valid in JavaScript, and a trace might optimize numeric conversion paths.

One of the most surprising features is that the compilation granularity is much finer. SourceMod compiles entire scripts to native code (known as “ahead of time” compilation). Sun and Microsoft’s JITs compile methods at a time, and thus compilation is deferred until a method is needed. A tracing JIT, however, is capable of compiling only pieces of code that are deemed as important. It can trace through anything it wants, including method calls, and return control back to the interpreter when the hard work is done.

This is a pretty new concept that seems to be the future of optimizing dynamic languages. JITs for these languages typically hit performance boundaries because the native code must either perform run-time conversion itself or exit back to interpreter functions for resolving types. There is supposedly a LuaJIT in the works for possibly my least favorite language (LUA), and someone on IRC mentioned a tracing JIT in the PyPy project (though I have not looked into it).

Unfortunately, benchmarks have continually shown that Tamarin Tracing is just plain slow. Some people are wondering why we’re even bothering with it. What’s important to recognize is that the speed of the tracer is bound to the speed of the interpreter. Tamarin Tracing is highly experimental, and the interpreter Adobe packaged with it is not optimized. The tracing engine, however, can be easily detached from the interpreter. Since Mozilla already has a reasonably fast interpreter (SpiderMonkey), our new project is to couple the two together, as “TraceMonkey,” so the interpreter can emit traces.

A trace is a set of low-level, abstracted instructions, internally called “LIR.” Each “word” of LIR is 32-bits — an 8-bit opcode and three optional 8-bit operands. For example, encoding a 32-bit immediate value requires an empty LIR_imm32 word and another word for the value. These LIR opcodes are designed to be fairly platform agnostic, but more importantly, they are intrinsically in SSA form. This makes liveness analysis, common sub-expression elimination, and other optimizations much easier.

Since TT’s interpreter is not 64-bit safe, I’ve been testing my port by writing programs directly in trace LIR. For my first function I implemented XKCD’s getRandomInt() { return 4; } function. Each label is the word/instruction number.

1: imm64 4      ; immediate value of 4
4: imm64, &addr ; immediate value, address from C code
7: sti 1, #0(4) ; take the pointer value of instruction 4, store value in instruction 1 at offset 0.
8: x            ; exit the trace

These traces are compiled backwards. In this example, the epilogue is generated first, and the last instruction to be generated is the first instruction of the prologue. Generation is a single pass and thus code size is not computed beforehand. Executable pages are allocated one at a time. If there is not enough space to encode a full instruction in the current page, a new one is allocated and a JMP is generated to bridge the code.

That’s it for my rough overview. If you’re interested in details of the tracer, including LIR and everything after, I highly recommend Dave Mandelin’s Tamarin Tracing Internals articles (parts 3, 4, and 5). He provides some excellent insight into the optimizations it makes, and I’ve been using the articles as a guide while reading the code.

4 thoughts on “Tamarin Tracing, Intro to Tracing JITs

Leave a Reply

Your email address will not be published.