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19/04/2021 - 20minutos
Trace-based Just-in-Time Type Specialization for Dynamic
Languages
Andreas Gal
∗
+
, Brendan Eich
∗
, Mike Shaver
∗
, David Anderson
∗
, David Mandelin
∗
,
Mohammad R. Haghighat
$
, Blake Kaplan
∗
, Graydon Hoare
∗
, Boris Zbarsky
∗
, Jason Orendorff
∗
,
Jesse Ruderman
∗
, Edwin Smith
#
, Rick Reitmaier
#
, Michael Bebenita
+
, Mason Chang
+#
, Michael Franz
+
Mozilla Corporation
∗
{
gal,brendan,shaver,danderson,dmandelin,mrbkap,graydon,bz,jorendorff,jruderman
}
@mozilla.com
Adobe Corporation
#
{
edwsmith,rreitmai
}
@adobe.com
Intel Corporation
$
{
mohammad.r.haghighat
}
@intel.com
University of California, Irvine
+
{
mbebenit,changm,franz
}
@uci.edu
Abstract
Dynamic languages such as JavaScript are more difficult to com-
pile than statically typed ones. Since no concrete type information
is available, traditional compilers need to emit generic code that can
handle all possible type combinations at runtime. We present an al-
ternative compilation technique for dynamically-typed languages
that identifies frequently executed loop traces at run-time and then
generates machine code on the fly that is specialized for the ac-
tual dynamic types occurring on each path through the loop. Our
method provides cheap inter-procedural type specialization, and an
elegant and efficient way of incrementally compiling lazily discov-
ered alternative paths through nested loops. We have implemented
a dynamic compiler for JavaScript based on our technique and we
have measured speedups of 10x and more for certain benchmark
programs.
Categories and Subject Descriptors
D.3.4 [
Programming Lan-
guages
]: Processors —
Incremental compilers, code generation
.
General Terms
Design, Experimentation, Measurement, Perfor-
mance.
Keywords
JavaScript, just-in-time compilation, trace trees.
1. Introduction
Dynamic languages
such as JavaScript, Python, and Ruby, are pop-
ular since they are expressive, accessible to non-experts, and make
deployment as easy as distributing a source file. They are used for
small scripts as well as for complex applications. JavaScript, for
example, is the de facto standard for client-side web programming
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. To copy otherwise, to republish, to post on servers or to redistribute
to lists, requires prior specific permission and/or a fee.
PLDI’09,
June 15–20, 2009, Dublin, Ireland.
Copyright
c
©
2009 ACM 978-1-60558-392-1/09/06...$5.00
and is used for the application logic of browser-based productivity
applications such as Google Mail, Google Docs and Zimbra Col-
laboration Suite. In this domain, in order to provide a fluid user
experience and enable a new generation of applications, virtual ma-
chines must provide a low startup time and high performance.
Compilers for statically typed languages rely on type informa-
tion to generate efficient machine code. In a dynamically typed pro-
gramming language such as JavaScript, the types of expressions
may vary at runtime. This means that the compiler can no longer
easily transform operations into machine instructions that operate
on one specific type. Without exact type information, the compiler
must emit slower generalized machine code that can deal with all
potential type combinations. While compile-time static type infer-
ence might be able to gather type information to generate opti-
mized machine code, traditional static analysis is very expensive
and hence not well suited for the highly interactive environment of
a web browser.
We present a trace-based compilation technique for dynamic
languages that reconciles speed of compilation with excellent per-
formance of the generated machine code. Our system uses a mixed-
mode execution approach: the system starts running JavaScript in a
fast-starting bytecode interpreter. As the program runs, the system
identifies
hot
(frequently executed) bytecode sequences, records
them, and compiles them to fast native code. We call such a se-
quence of instructions a
trace
.
Unlike method-based dynamic compilers, our dynamic com-
piler operates at the granularity of individual loops. This design
choice is based on the expectation that programs spend most of
their time in hot loops. Even in dynamically typed languages, we
expect hot loops to be mostly
type-stable
, meaning that the types of
values are invariant. (12) For example, we would expect loop coun-
ters that start as integers to remain integers for all iterations. When
both of these expectations hold, a trace-based compiler can cover
the program execution with a small number of type-specialized, ef-
ficiently compiled traces.
Each compiled trace covers one path through the program with
one mapping of values to types. When the VM executes a compiled
trace, it cannot guarantee that the same path will be followed
or that the same types will occur in subsequent loop iterations.
Hence, recording and compiling a trace
speculates
that the path and
typing will be exactly as they were during recording for subsequent
iterations of the loop.
Every compiled trace contains all the
guards
(checks) required
to validate the speculation. If one of the guards fails (if control
flow is different, or a value of a different type is generated), the
trace exits. If an exit becomes hot, the VM can record a
branch
trace
starting at the exit to cover the new path. In this way, the VM
records a
trace tree
covering all the hot paths through the loop.
Nested loops can be difficult to optimize for tracing VMs. In
a na
̈
ıve implementation, inner loops would become hot first, and
the VM would start tracing there. When the inner loop exits, the
VM would detect that a different branch was taken. The VM would
try to record a branch trace, and find that the trace reaches not the
inner loop header, but the outer loop header. At this point, the VM
could continue tracing until it reaches the inner loop header again,
thus tracing the outer loop inside a trace tree for the inner loop.
But this requires tracing a copy of the outer loop for every side exit
and type combination in the inner loop. In essence, this is a form
of unintended tail duplication, which can easily overflow the code
cache. Alternatively, the VM could simply stop tracing, and give up
on ever tracing outer loops.
We solve the nested loop problem by recording
nested trace
trees
. Our system traces the inner loop exactly as the na
̈
ıve version.
The system stops extending the inner tree when it reaches an outer
loop, but then it starts a new trace at the outer loop header. When
the outer loop reaches the inner loop header, the system tries to call
the trace tree for the inner loop. If the call succeeds, the VM records
the call to the inner tree as part of the outer trace and finishes
the outer trace as normal. In this way, our system can trace any
number of loops nested to any depth without causing excessive tail
duplication.
These techniques allow a VM to dynamically translate a pro-
gram to nested, type-specialized trace trees. Because traces can
cross function call boundaries, our techniques also achieve the ef-
fects of inlining. Because traces have no internal control-flow joins,
they can be optimized in linear time by a simple compiler (10).
Thus, our tracing VM efficiently performs the same kind of op-
timizations that would require interprocedural analysis in a static
optimization setting. This makes tracing an attractive and effective
tool to type specialize even complex function call-rich code.
We implemented these techniques for an existing JavaScript in-
terpreter, SpiderMonkey. We call the resulting tracing VM
Trace-
Monkey
. TraceMonkey supports all the JavaScript features of Spi-
derMonkey, with a 2x-20x speedup for traceable programs.
This paper makes the following contributions:
•
We explain an algorithm for dynamically forming trace trees to
cover a program, representing nested loops as nested trace trees.
•
We explain how to speculatively generate efficient type-specialized
code for traces from dynamic language programs.
•
We validate our tracing techniques in an implementation based
on the SpiderMonkey JavaScript interpreter, achieving 2x-20x
speedups on many programs.
The remainder of this paper is organized as follows. Section 3 is
a general overview of trace tree based compilation we use to cap-
ture and compile frequently executed code regions. In Section 4
we describe our approach of covering nested loops using a num-
ber of individual trace trees. In Section 5 we describe our trace-
compilation based speculative type specialization approach we use
to generate efficient machine code from recorded bytecode traces.
Our implementation of a dynamic type-specializing compiler for
JavaScript is described in Section 6. Related work is discussed in
Section 8. In Section 7 we evaluate our dynamic compiler based on
1 for (var i = 2; i < 100; ++i) {
2 if (!primes[i])
3 continue;
4 for (var k = i + i; i < 100; k += i)
5 primes[k] = false;
6 }
Figure 1. Sample program: sieve of Eratosthenes.
primes
is
initialized to an array of 100
false
values on entry to this code
snippet.
Interpret
Bytecodes
Monitor
Record
LIR
T
race
Execute
Compiled
T
race
Enter
Compiled
T
race
Compile
LIR
T
race
Leave
Compiled
T
race
loop
edge
hot
loop/exit
abort
recording
fi
nish at
loop header
cold/blacklisted
loop/exit
compiled trace
ready
loop edge with
same types
side exit to
existing trace
side exit,
no existing trace
Overhead
Interpreting
Native
Symbol Key
Figure 2.
State machine describing the major activities of Trace-
Monkey and the conditions that cause transitions to a new activ-
ity. In the dark box, TM executes JS as compiled traces. In the
light gray boxes, TM executes JS in the standard interpreter. White
boxes are overhead. Thus, to maximize performance, we need to
maximize time spent in the darkest box and minimize time spent in
the white boxes. The best case is a loop where the types at the loop
edge are the same as the types on entry–then TM can stay in native
code until the loop is done.
a set of industry benchmarks. The paper ends with conclusions in
Section 9 and an outlook on future work is presented in Section 10.
2. Overview: Example Tracing Run
This section provides an overview of our system by describing
how TraceMonkey executes an example program. The example
program, shown in Figure 1, computes the first 100 prime numbers
with nested loops. The narrative should be read along with Figure 2,
which describes the activities TraceMonkey performs and when it
transitions between the loops.
TraceMonkey always begins executing a program in the byte-
code interpreter. Every loop back edge is a potential trace point.
When the interpreter crosses a loop edge, TraceMonkey invokes
the
trace monitor
, which may decide to record or execute a native
trace. At the start of execution, there are no compiled traces yet, so
the trace monitor counts the number of times each loop back edge is
executed until a loop becomes
hot
, currently after 2 crossings. Note
that the way our loops are compiled, the loop edge is crossed before
entering the loop, so the second crossing occurs immediately after
the first iteration.
Here is the sequence of events broken down by outer loop
iteration:
v0 := ld state[748] // load primes from the trace activation record
st sp[0], v0 // store primes to interpreter stack
v1 := ld state[764] // load k from the trace activation record
v2 := i2f(v1) // convert k from int to double
st sp[8], v1 // store k to interpreter stack
st sp[16], 0 // store false to interpreter stack
v3 := ld v0[4] // load class word for primes
v4 := and v3, -4 // mask out object class tag for primes
v5 := eq v4, Array // test whether primes is an array
xf v5 // side exit if v5 is false
v6 := js_Array_set(v0, v2, false) // call function to set array element
v7 := eq v6, 0 // test return value from call
xt v7 // side exit if js_Array_set returns false.
Figure 3. LIR snippet for sample program.
This is the LIR recorded for line 5 of the sample program in Figure 1. The LIR encodes
the semantics in SSA form using temporary variables. The LIR also encodes all the stores that the interpreter would do to its data stack.
Sometimes these stores can be optimized away as the stack locations are live only on exits to the interpreter. Finally, the LIR records guards
and side exits to verify the assumptions made in this recording: that
primes
is an array and that the call to set its element succeeds.
mov edx, ebx(748) // load primes from the trace activation record
mov edi(0), edx // (*) store primes to interpreter stack
mov esi, ebx(764) // load k from the trace activation record
mov edi(8), esi // (*) store k to interpreter stack
mov edi(16), 0 // (*) store false to interpreter stack
mov eax, edx(4) // (*) load object class word for primes
and eax, -4 // (*) mask out object class tag for primes
cmp eax, Array // (*) test whether primes is an array
jne side_exit_1 // (*) side exit if primes is not an array
sub esp, 8 // bump stack for call alignment convention
push false // push last argument for call
push esi // push first argument for call
call js_Array_set // call function to set array element
add esp, 8 // clean up extra stack space
mov ecx, ebx // (*) created by register allocator
test eax, eax // (*) test return value of js_Array_set
je side_exit_2 // (*) side exit if call failed
...
side_exit_1:
mov ecx, ebp(-4) // restore ecx
mov esp, ebp // restore esp
jmp epilog // jump to ret statement
Figure 4. x86 snippet for sample program.
This is the x86 code compiled from the LIR snippet in Figure 3. Most LIR instructions compile
to a single x86 instruction. Instructions marked with
(*)
would be omitted by an idealized compiler that knew that none of the side exits
would ever be taken. The 17 instructions generated by the compiler compare favorably with the 100+ instructions that the interpreter would
execute for the same code snippet, including 4 indirect jumps.
i=2.
This is the first iteration of the outer loop. The loop on
lines 4-5 becomes hot on its second iteration, so TraceMonkey en-
ters recording mode on line 4. In recording mode, TraceMonkey
records the code along the trace in a low-level compiler intermedi-
ate representation we call
LIR
. The LIR trace encodes all the oper-
ations performed and the types of all operands. The LIR trace also
encodes
guards
, which are checks that verify that the control flow
and types are identical to those observed during trace recording.
Thus, on later executions, if and only if all guards are passed, the
trace has the required program semantics.
TraceMonkey stops recording when execution returns to the
loop header or exits the loop. In this case, execution returns to the
loop header on line 4.
After recording is finished, TraceMonkey compiles the trace to
native code using the recorded type information for optimization.
The result is a native code fragment that can be entered if the
interpreter PC and the types of values match those observed when
trace recording was started. The first trace in our example,
T
45
,
covers lines 4 and 5. This trace can be entered if the PC is at line 4,
i
and
k
are integers, and
primes
is an object. After compiling
T
45
,
TraceMonkey returns to the interpreter and loops back to line 1.
i=3.
Now the loop header at line 1 has become hot, so Trace-
Monkey starts recording. When recording reaches line 4, Trace-
Monkey observes that it has reached an inner loop header that al-
ready has a compiled trace, so TraceMonkey attempts to nest the
inner loop inside the current trace. The first step is to call the inner
trace as a subroutine. This executes the loop on line 4 to completion
and then returns to the recorder. TraceMonkey verifies that the call
was successful and then records the call to the inner trace as part of
the current trace. Recording continues until execution reaches line
1, and at which point TraceMonkey finishes and compiles a trace
for the outer loop,
T
16
.
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, TraceMonkey has 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 JavaScript, 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-
not be interpreted as necessarily representing the official views,
policies or endorsements, either expressed or implied, of the Na-
tional Science foundation (NSF), any other agency of the U.S. Gov-
ernment, or any of the companies mentioned above.
References
[1] LuaJIT roadmap 2008 - http://lua-users.org/lists/lua-l/2008-
02/msg00051.html.
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