In my last post about ParallelJS, I discussed the ForkJoin() intrinsic and showed how it was used to implement the parallel map operation. Today I want to write about the high-level changes to IonMonkey that are needed to support ForkJoin(). IonMonkey, of course, is our JavaScript engine.

Parallel execution mode

To support ParallelJS, we introduce a second mode of compilation called parallel execution mode. JavaScript compiled in this mode produces executable code that is suitable to be run in parallel. To accommodate this new mode, each JSScript* potentially contains pointers to two IonScript* data structures, one for standard sequential mode and one for parallel mode.

Execution normally stays confined within one mode. So if you are running a function f in sequential mode and it invokes another function g, then we will run the sequential mode version of g. But if you are running f in parallel mode, it will call the parallel version of g. The only place where we move between modes is in the ForkJoin intrinsic, which invokes the parallel mode script for the first time.

You may wonder why we permit each script to be compiled in both modes simultaneously. The reason is that it is possible to have helper functions and code that runs in both sequential and parallel mode. Imagine, for example, that you have a helper function for searching an array to find the object with a given name:

function findObject(list, name) {
    for (var i = 0; i < list.length; i++) {
        if (list[i].name === name)
            return list[i];
    }
    throw Error("No object with name " + name + " found!");
}

It is perfectly reasonable to want to invoke this helper function both from sequential and from parallel code. If we only permitted a function to be compiled in one mode or the other, we would always be recompiling findObject each time we started or finished a parallel operation.

Differences between parallel and sequential execution mode

The biggest difference between parallel and sequential mode is that code executing in parallel mode is guaranteed to be pure. That is, it can never write to any shared state that might be visible from other threads. This purity requirement generally includes not only user-visible JavaScript state but also internal engine details. For example, in sequential mode code, after we have done several property lookups on an object that has a large number of properties, we will “hashify” the property chain, meaning that we convert it from an array into a dictionary to make later lookups faster. This hashification operation is not visible to the JavaScript user (except insofar as subsequent property lookups are faster), but it is still disallowed in parallel execution mode because it would cause data races.

There are some exceptions to the purity requirement. The first and most obvious is the UnsafeSetElement intrinsic I discussed in part one, which is used to track the progress of parallel work. The second exception is that it is ok to modify internal engine details so long as those modifications are threadsafe. For example, in bug 846111, Shu has implemented threadsafe inline-caching, which is of course a mutation to shared state.

Generally speaking, though, when you call a parallel mode function you can be sure that it will either complete successfully or bailout. In either case, you know that it has no lasting effects that are visible to end-user JavaScript code, except those that might have occurred via the UnsafeSetElement intrinsic (which of course is only usable from self-hosted code and which must be carefully audited).

Changes to the Ion compilation process

There are two major changes when compiling in ParallelExecutionMode: The first change is the so-called “parallel array analysis”, which analyzes the actions that the scripts take and modifies them as needed to ensure that each action is either threadsafe (and pure) or else that the script bails out. The second change is that do not compile a single script in isolation but rather attempt to compile the transitive closure of a starting script and all scripts that it may call.

Parallel array analysis

The parallel array analysis can be found in js/src/ion/ParallelArrayAnalysis.cpp, in the function ParallelCompileContext::analyzeAndGrowWorklist(). It runs after the normal suite of optimizations have taken place. Its primary goal is to ensure that the parallel code will be pure and threadsafe.

To that end, it performs a walk of the control-flow graph and examines each MIR instruction using a visitor. The MIR instructions are categorized into one of several categories, as follows:

  • Safe operations are operations that can be safely executed in parallel without changes, such as Constant or Box.
  • Write-guarded operations are operations that are safe as long as the value being modified is not shared. To verify this, we insert a write guard before the operation in question. The write guard will cause a bailout should the object be shared (more on the details of this check to come in a later post). N.B.—write guards are not to be confused with write barriers, which have to do with incremental and generational garbage collection.
  • Specialized operations are numeric operations that are safe so long as they are operating over scalar data, such as Add, Mul, etc.
  • Unsafe operations are operations that are just plain disallowed in parallel execution, generally because we have not made an equivalent threadsafe path. An example is RegExp.
  • Custom operations are, well, everything else. Generally speaking these are operations that are not safe by default in parallel mode, but where there exists an alternative version that is safe, such as NewArray or NewObject.

The categorization of instructions is done using macros. The visitor expects one method per MIR instruction type. There are various macros for each of the above categories, and the macro expands into a pre-canned method definition (in the case of custom operations, the macro expands to an out-of-line method, and the method body appears later in the file).

I’ll talk a little bit more about the safe and unsafe operations now, and I’ll cover the other cases (write guards, memory allocation, etc) in later posts.

Safe operations are simply left unchanged, and they execute just as they would in sequential mode (though in some cases there are checks in the CodeGenerator so that the MIR behaves somewhat differently).

When an unsafe operation is encountered, the basic block in which it resides is removed from the graph along with its dominated subtree. In its place, we add a bailout block that will cause parallel execution to bailout should it ever execute. This ensures that unsafe operations that never execute do not prohibit safe code from running.

Transitive compilation

In normal sequential mode, if we encounter a call to a script that is not compiled, we just invoke the interpreter. In parallel mode this option is not available. So what we do instead is to take advantage of the information that TI makes available and, when compiling a script x, collect all scripts that x might call. Then, once we have compiled x, we go on and compile those scripts. The process is transitive, meaning that we will then continue on to compile the scripts that x’s callees might call and so forth until we reach a fixed point.

Note that we do not monitor for hot paths, as we do in sequential mode. That is, we don’t care if the script has been called 10 times or 100 times before. This is for two reasons: one, we assume that parallel paths are going to be hot, since they are going to be called over all the entries in a large array. Two, seeing as we will have to bailout if we encounter a call to an uncompiled script, it’s worth erring on the side of more compilation rather than less. We do however check that the use count of the script is at least one, so as to avoid compiling things that never run.

At runtime, when we see a call to a JavaScript function, we check whether it has been compiled for parallel execution. If so, we can simply call it as normal and carry on. This is the expected case, of course.

If we encounter a call to an uncompiled script, which can happen either because our transitive compilation was incomplete or because the callee was invalidated or garbage-collected in the mean-time, we bailout with an “uncompiled script” error. At this point, control returns to the ForkJoin function I described in my previous post. Presuming that we haven’t encountered too many bailouts yet, ForkJoin will cycle around and try to compile the uncompiled script.

When compiling an uncompiled script, we also set a flag on all the currently executing scripts in the stack trace. This flag is a warning that execution of that script is likely to encounter an uncompiled script. The purpose for this flag is to notify later callers that while the script itself is valid, it likely has callees that have not been compiled, so before running the script in parallel we should re-walk the transitive closure of things it might call and check for anything that is missing.