Bundle to bytecode
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Bytecode to runtime
ShiftExpression : ShiftExpression >> AdditiveExpression
- Let lref be the result of evaluating ShiftExpression.
- Let lval be ? GetValue(lref).
- Let rref be the result of evaluating AdditiveExpression.
- Let rval be ? GetValue(rref).
- Let lnum be ? ToInt32(lval).
- Let rnum be ? ToUint32(rval).
- Let shiftCount be the result of masking out all but the least significant 5 bits of rnum, that is, compute rnum & 0x1F.
- Return the result of performing a sign-extending right shift of lnum by shiftCount bits. The most significant bit is propagated. The result is a signed 32-bit integer.
In the first six steps we convert the operands (the values on either side of the >>) into 32-bit integers, and then we perform the actual shift operation. If you squint, it looks a bit like a recipe. If you really squint, you might see the beginnings of a syntax-directed interpreter.
If an object doesn’t have an entry for a given string key, then we need to look for that key in the prototype. We repeat this operation until we either find the key that we’re looking for or get to the end of the prototype chain.
That’s potentially a lot of work to perform every time we want to get a property value out of an object!
Now, imagine that we are following a series of bytecode instructions written on a scroll. The next instruction tells us to get the value of the property named “x” from some object. You grab that object, turn it over in your hands a few times to figure out where “x” is stored, and find out that it is stored in the object’s second data slot.
It occurs to you that any object with this same shape will have an “x” property in its second data slot. You pull out your quill and make a note on your bytecode scroll indicating the shape of the object and the location of the “x” property. The next time you see this instruction you’ll simply check the shape of the object. If the shape matches what you’ve recorded in your bytecode notes, you’ll know exactly where the data is located without having to inspect the object. You’ve just implemented what’s known as a monomorphic inline cache!
But what happens if the shape of the object doesn’t match our bytecode notes? We can get around this problem by drawing a small table with a row for each shape we’ve seen. When we see a new shape, we use our quill to add a new row to the table. We now have a polymorphic inline cache. It’s not quite as fast as the monomorphic cache, and it takes up a little more space on the scroll, but if there aren’t too many rows, it works quite well.
If we end up with a table that’s too big, we’ll want to erase the table, and make a note to remind ourselves to not worry about inline caching for this instruction. In compiler terms, we have a megamorphic callsite.
In general, monomorphic code is very fast, polymorphic code is almost as fast, and megamorphic code tends to be rather slow. Or, in haiku form:
Interpreter to just-in-time (JIT)
The great thing about an interpreter is that it can start executing code quickly, and for code that is run only once or twice, this “software CPU” performs acceptably fast. But for “hot code” (functions that are run hundreds, thousands, or millions of times) what we really want is to execute machine instructions directly on the actual hardware. We want just-in-time (JIT) compilation.
When you open the next scroll of bytecode, you notice that this one is “hot.” You’ve executed it dozens of times, and you think it would run much faster in machine code. Fortunately, there are two rooms full of scribes that are ready to perform the translation for you. The scribes in the first room, a brightly lit open office, can translate bytecode into machine code quite fast. The code that they produce is of good quality and is concise, but it’s not as efficient as it could be. The scribes in the second room, dark and misty with incense, work more carefully and take a bit longer to finish. The code that they produce, however, is highly optimized and about as fast as possible.
In compiler-speak, we refer to these different rooms as JIT compilation tiers. Different engines have different numbers of tiers depending on the tradeoffs they’ve chosen to make.
You decide to send the bytecode to the first room of scribes. After working on it for a bit, using your carefully recorded notes, they produce a new scroll containing machine instructions and place it on the correct shelf alongside the original bytecode version. The next time you need to execute the function, you can use this faster set of instructions.
The only problem is that the scribes made quite a few assumptions when they translated our scroll. Perhaps they assumed that a variable would always hold an integer. What happens if one of those assumptions is invalidated?
In that case we must perform what’s known as a bailout. We pull the original bytecode scroll from the shelf, and figure out which instruction we should start executing from. The machine code scroll disappears in a puff of smoke and the process starts again.
To infinity and beyond