Runtime Feedback in a Meta-Tracing JIT for Efficient Dynamic

Runtime Feedback in a Meta-Tracing
JIT for Efficient Dynamic Languages
Writer: Carl Friedrich Bolz
Introduced by Ryotaro IKEDA
at 2011/09/06
This paper describes about…
How to make it more efficient to apply JIT compiler with PyPy
PyPy :
Well-known as fast Python implementation.
However, in actual, it is one of framework
to implement interpreter with JIT and GC!
( Python implementation is just a demo! )
What is PyPy?
Framework which enables to write
interpreter implementation with Restricted Python
The project mainly intends to give environments to
implement dynamic interpreter much efficient
PyPy’s JIT Automatic Implementation
It is implemented
by PyPy user!
Target code that is
written in any language
Any interpreter
that is written in RPython
PyPy’s RPython interpreter
Give some “hints”
to enable to run JIT
compiler efficiently
The most bottom one performs JIT compilation
and optimization to the middle one
In result, JIT compiler that is suitable for
any language is automatically implemented
How to treat
non-language-specific JIT compilation
Typical JIT Compiler
Uses language-specific feature because
each JIT compiler is dedicated to compile only one language
PyPy’s JIT Compiler
Though it is for RPython, PyPy can’t use any language-specific
feature which PyPy user want to implement.
It is what we called “ Meta-Tracing “
How can we make it much faster with applying
efficient method for Meta-Tracing??
= Objective
What Merit Using PyPy
Rather than JIT of other implementation
Widen compilation / optimization area
Typical JIT
It is too challenging for JIT compiler
to target data structure operation
PyPy’s JIT
It traces ,and only looks to whole RPython code,
so it can target data structure operation
which written in RPython by developer.
Hinting Mechanism
Main concept
Giving hints to enable JIT compiler to compile
efficiently is the most important
A hint to turn arbitrary variables into constants in the trace by
feeding back runtime information into compilation
A way to annotate operations which the constant folding optimization then recognizes and exploits.
General techniques for refactoring code to expose constant
folding opportunities of likely runtime constants.
PyPy’s Meta-Tracing JIT Compilers
To check and determine which control path to compile
x = 100
y = 200
Cycle: Trace
Optimizations are also
performed during
this trace form
Also constant-folded
Trace (cycles, to be compiled):
Cond ->
x = x + 200
Cond ….
PyPy’s Tracer
Trace Area
PyPy (can / by default) traces only “hot” paths.
-> Trace will be invoked frequently executed path
When it crosses
threshold, it is
regarded as “hot”
It indicates how many times
the loop is executed
for x in sequence :
t = x + ….
☆ As mentioned before, PyPy’s tracer doesn’t trace user program
directly, but interpreter implementation written in RPython instead.
Optimization Passes
• Remove/simplify operations in the trace
– Constant folding
– Common subexpression elimination
– Allocation removal
– Store/load propagation
– Loop invariant code motion
These can be applied because traces are absolutely linear form
Operate during RPython form
Running Example
Arrangements of shown examples
Simple and bare-bones object model.
• Just supports classes and instances
• No inheritance
• Class contains method and variable
• Instance have a class, if no requested method / variable
found in the instance, it searches among the class.
Example Implementation
Use dictionary
to manage class method
Use dictionary
to manage instance
To search requested method
To register given method
Dictionary’s “get” method costs too much.
To solve the problem, it is required to make it target to JIT compilation
( The way to do this is described later discussion )
Hints for Controlling Optimization
Applied only to interpreter written in RPython, not user program.
• Two hints that enables to increase the optimization
opportunities for constant folding
Enable propagation to find “Constantfoldable” variables via trace guard
Annotation to notify which variables
are assumed as constant variable
Though each of them never break code’s behavior,
Using them incorrectly will definitely deteriorate its speed.
What “Guard” is
Dynamic Language
test = x + y;
That both x
and y are
number, or
string is OK
Static Language
test = x + y;
That both x and y are either
number or string, types
cannot be canged
It is necessary to assure each variables’ type are same
to compile Dynamic Language to Static Language
Native code is one of static language, it’s needed
How “Guard” works
Guard assures that interpreter is running compiled trace in
same condition as when it is compiled at first time.
Source code
Now it
Assure conditions
Becomes y = 10
to compile them
hot! z = 100
to machine code
100 for x in sequence:
y += 1
…. = func(x)
During execution
of compiled
machine code…
Trace result
guard(x == int() )
guard(y = int())
guard(z = 100)
y += 1
…. = func(x)
If conditions described in guard is true,
it continues to run.
If conditions described in guard is false,
it stops to run and switch to interpreter exec.
• Technique to operate constant-fold using guard
Source Code
x = somefunc()
y = func(x)
Trace Result
x = somefunc()
guard( x == 200)
y = func(x)
Trace tree
x = somefunc()
guard(x == 200)
y = func(x)
Normal execution root (interpreter)
Result after Promotion
x = somefunc()
guard( x == 200)
y = func(200)
y = func(200)
Traced root (will be / already compiled)
“Promote” how to
Use “promote()” embedded method
which is given by PyPy RPython interpreter
to give it a “hint” that indicates
“promote” can be applied during this scope.
Assume the trace here usually be with a
condition that self and val are expected
to not so frequently varied.
Guard-fall is expected not so
occurs frequently
Later discussion!
It may not consume overhead
so much and can be expected
that constant-folding will bring
great improvement.
“Trace-Elidable” helps to apply “Promote”
• To tell the truth, promote cannot be invoked without
@elidable annotation in the example.
Assure specific method never
change any variables.
Though tracer want to “promote” method “f” ,
tracer doesn’t know whether self.c() returns
always same value or not…
Tracer considers not to use
value-specific guard but
type-specific guard… never “promoted”.
@elidable annotations shows that given method is immutable
This “hint” enables tracer to promote f()!
Result trace after these 2 hints applied
This trace is created
without any hints given.
Constant-folding is applied
via @elidable and promote.
Technique to increate “trace-elidable”
Putting It All Together
Increasing the amount of Trace-elidable method increases
chance to apply constant-folding and to help Promote.
Prepare original “Map” class
to manage Instance’s attributes
instead of using dictionary
To append @elidable
for index map
(described in next slide)
Index map
• Efficient / Suitable data structure for PyPy
Map: To manage data location (index)
“v1” : 0
“string” : 1
“x” : 3
List: Stores actual data
Prepare getindex with this impelemntation,
though it is immutable, trace-elidable can be used!
How does Instance use the “Map”?
This class which is used for manage instances no longer uses dictionary!
No longer use dictionary
Whole methods belong to “map”
are “trace-elidable”.
So the promote will work correctly!
Versioning of Classes
Using only trace-elidable don’t satisfy requirements
In Python, though @elidable annotation is given,
the method may yield not same value because
any attributes can be changed.
How do you feel if
“inst.x = -1” is executed?
It is necessary to handle
this possible changes
class A:
def __init__(self):
x = 100
def X(self):
return x
They propose “Versioning”
inst = A()
Use Guard Feature to Versioning
Dummy class to use guard feature
This promote helps to create value-specified
guard with current “version”.
So, it is still trace-elidable but can handle
methods changing.
When some of methods is changed,
Yield new VersionTag and save it to self.version
Intel Core2 Duo P8400 processor with 2.26
GHz and 3072 KB of cache on a machine
with 3GB RAM running Linux 2.6.35
It uses many OOP’s features
No hints given
for board game
BZ2 decoder
OS Kernel
Decimal floating
Point calculations
• Two hints that can be used in the source code
of an interpreter written with PyPy.
• They give control over runtime feedback and
optimization to the language implementor.
• They are expressive enough for building wellknown virtual machine optimization
techniques, such as maps and inlining.
Effects to my Study
• Use PyPy as infrastructure
– It can emit C source code from RPython
• Applying P.T seems easy
– Parallelized Template for Rpython
• This paper performs optimizations in RPython form.
How do you think that I consider to implement
template code in RPython?

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