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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 compile 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 alternative 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 actual 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 discovered 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 Languages]: Processors — Incremental compilers, code generation.
General Terms Design, Experimentation, Measurement, Performance.
Keywords JavaScript, just-in-time compilation, trace trees.
1. Introduction
Dynamic languages such as JavaScript, Python, and Ruby, are popular 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
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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 Collaboration Suite. In this domain, in order to provide a fluid user
experience and enable a new generation of applications, virtual machines must provide a low startup time and high performance.
Compilers for statically typed languages rely on type information to generate efficient machine code. In a dynamically typed programming 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 inference might be able to gather type information to generate optimized 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 performance of the generated machine code. Our system uses a mixedmode 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 sequence of instructions a trace.
Unlike method-based dynamic compilers, our dynamic compiler 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 counters 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, efficiently 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
