i=4.
On this iteration, TraceMonkey calls
T
16
. Because
i=4
, the
if
statement on line 2 is taken. This branch was not taken in the
original trace, so this causes
T
16
to fail a guard and take a side exit.
The exit is not yet hot, so TraceMonkey returns to the interpreter,
which executes the continue statement.
i=5.
TraceMonkey calls
T
16
, which in turn calls the nested trace
T
45
.
T
16
loops back to its own header, starting the next iteration
without ever returning to the monitor.
i=6.
On this iteration, the side exit on line 2 is taken again. This
time, the side exit becomes hot, so a trace
T
23
,
1
is recorded that
covers line 3 and returns to the loop header. Thus, the end of
T
23
,
1
jumps directly to the start of
T
16
. The side exit is patched so that
on future iterations, it jumps directly to
T
23
,
1
.
At this point, TraceMonkeyhas compiled enough traces to cover
the entire nested loop structure, so the rest of the program runs
entirely as native code.
3. Trace Trees
In this section, we describe traces, trace trees, and how they are
formed at run time. Although our techniques apply to any dynamic
language interpreter, we will describe them assuming a bytecode
interpreter to keep the exposition simple.
3.1 Traces
A
trace
is simply a program path, which may cross function call
boundaries. TraceMonkey focuses on
loop traces
, that originate at
a loop edge and represent a single iteration through the associated
loop.
Similar to an extended basic block, a trace is only entered at
the top, but may have many exits. In contrast to an extended basic
block, a trace can contain join nodes. Since a trace always only
follows one single path through the original program, however
, join
nodes are not recognizable as such in a trace and have a single
predecessor node like regular nodes.
A
typed trace
is a trace annotated with a type for every variable
(including temporaries) on the trace. A typed trace also has an entry
type map
giving the required types for variables used on the trace
before they are defined. For example, a trace could have a type map
(x: int, b: boolean)
, meaning that the trace may be entered
only if the value of the variable
x
is of type
int
and the value of
b
is of type
boolean
. The entry type map is much like the signature
of a function.
In this paper, we only discuss typed loop traces, and we will
refer to them simply as “traces”. The key property of typed loop
traces is that they can be compiled to efficient machine code using
the same techniques used for typed languages.
In TraceMonkey, traces are recorded in trace-flavored SSA
LIR
(low-level intermediate representation). In trace-flavored SSA (or
TSSA), phi nodes appear only at the entry point, which is reached
both on entry and via loop edges. The important LIR primitives
are constant values, memory loads and stores (by address and
offset), integer operators, floating-point operators, function calls,
and conditional exits. Type conversions, such as integer to double,
are represented by function calls. This makes the LIR used by
TraceMonkey independent of the concrete type system and type
conversion rules of the source language. The LIR operations are
generic enough that the backend compiler is language independent.
Figure 3 shows an example LIR trace.
Bytecode interpreters typically represent values in a various
complex data structures (e.g., hash tables) in a boxed format (i.e.,
with attached type tag bits). Since a trace is intended to represent
efficient code that eliminates all that complexity
, our traces oper-
ate on unboxed values in simple variables and arrays as much as
possible.
A trace records all its intermediate values in a small activation
record area. To make variable accesses fast on trace, the trace also
imports local and global variables by unboxing them and copying
them to its activation record. Thus, the trace can read and write
these variables with simple loads and stores from a native activation
recording, independently of the boxing mechanism used by the
interpreter. When the trace exits, the VM boxes the values from
this native storage location and copies them back to the interpreter
structures.
For every control-flow branch in the source program, the
recorder generates conditional exit LIR instructions. These instruc-
tions exit from the trace if required control flow is different from
what it was at trace recording, ensuring that the trace instructions
are run only if they are supposed to. We call these instructions
guard
instructions.
Most of our traces represent loops and end with the special
loop
LIR instruction. This is just an unconditional branch to the top of
the trace. Such traces return only via guards.
Now, we describe the key optimizations that are performed as
part of recording LIR. All of these optimizations reduce complex
dynamic language constructs to simple typed constructs by spe-
cializing for the current trace. Each optimization requires guard in-
structions to verify their assumptions about the state and exit the
trace if necessary.
Type specialization.
All LIR primitives apply to operands of specific types. Thus,
LIR traces are necessarily type-specialized, and a compiler can
easily produce a translation that requires no type dispatches. A
typical bytecode interpreter carries tag bits along with each value,
and to perform any operation, must check the tag bits, dynamically
dispatch, mask out the tag bits to recover the untagged value,
perform the operation, and then reapply tags. LIR omits everything
except the operation itself.
A potential problem is that some operations can produce values
of unpredictable types. For example, reading a property from an
object could yield a value of any type, not necessarily the type
observed during recording. The recorder emits guard instructions
that conditionally exit if the operation yields a value of a different
type from that seen during recording. These guard instructions
guarantee that as long as execution is on trace, the types of values
match those of the typed trace. When the VM observes a side exit
along such a type guard, a new typed trace is recorded originating
at the side exit location, capturing the new type of the operation in
question.
Representation specialization: objects.
In Jav
aScript, name
lookup semantics are complex and potentially expensive because
they include features like object inheritance and
eval
. To evaluate
an object property read expression like
o.x
, the interpreter must
search the property map of
o
and all of its prototypes and parents.
Property maps can be implemented with different data structures
(e.g., per-object hash tables or shared hash tables), so the search
process also must dispatch on the representation of each object
found during search. TraceMonkey can simply observe the result of
the search process and record the simplest possible LIR to access
the property value. For example, the search might finds the value of
o.x
in the prototype of
o
, which uses a shared hash-table represen-
tation that places
x
in slot 2 of a property vector. Then the recorded
can generate LIR that reads
o.x
with just two or three loads: one to
get the prototype, possibly one to get the property value vector, and
one more to get slot 2 from the vector. This is a vast simplification
and speedup compared to the original interpreter code. Inheritance
relationships and object representations can change during execu-
tion, so the simplified code requires guard instructions that ensure
the object representation is the same. In TraceMonkey,objects’ rep-