Comparison of application virtualization software

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Application virtualization software refers to both application virtual machines and software responsible for implementing them. Application virtual machines are typically used to allow application bytecode to run portably on many different computer architectures and operating systems. The application is usually run on the computer using an interpreter or just-in-time compilation (JIT). There are often several implementations of a given virtual machine, each covering a different set of functions.

Comparison of virtual machines[edit]

JavaScript machines not included. See List of ECMAScript engines to find them.

The table here summarizes elements for which the virtual machine designs are intended to be efficient, not the list of abilities present in any implementation.

Virtual machine Machine model Memory management Code security Interpreter JIT AOT Shared libraries Common Language Object Model Dynamic typing
Android Runtime (ART) register automatic Yes Yes Yes Yes ? Yes Yes
Common Language Runtime (CLR) stack automatic or manual Yes Yes Yes Yes Yes Yes Yes
Dis (Inferno) register automatic Yes Yes Yes Yes Yes Yes Yes
DotGNU Portable.NET stack automatic or manual Yes Yes Yes Yes Yes Yes No
Java virtual machine (JVM) stack automatic Yes Yes Yes Yes Yes Yes Yes[1]
JikesRVM stack automatic Yes Yes Yes Yes ? Yes Yes
LLVM register manual No Yes Yes Yes Yes Yes No
Mono stack automatic or manual Yes Yes Yes Yes Yes Yes Yes
Parrot register automatic No Yes No[2] Yes Yes Yes Yes
Dalvik register automatic Yes Yes Yes No ? No No
Squeak stack automatic No Yes Yes No Yes No Yes
BEAM (Erlang) register automatic ? Yes Yes Yes Yes Yes Yes
MoarVM register automatic ? Yes Yes Yes Yes Yes Yes

Virtual machine instructions process data in local variables using a main model of computation, typically that of a stack machine, register machine, or random access machine often called the memory machine. Use of these three methods is motivated by different tradeoffs in virtual machines vs physical machines, such as ease of interpreting, compiling, and verifying for security.

Memory management in these portable virtual machines is addressed at a higher level of abstraction than in physical machines. Some virtual machines, such as the popular Java virtual machines (JVM), are involved with addresses in such a way as to require safe automatic memory management by allowing the virtual machine to trace pointer references, and disallow machine instructions from manually constructing pointers to memory. Other virtual machines, such as LLVM, are more like traditional physical machines, allowing direct use and manipulation of pointers. Common Intermediate Language (CIL) offers a hybrid in between, allowing both controlled use of memory (like the JVM, which allows safe automatic memory management), while also allowing an 'unsafe' mode that allows direct pointer manipulation in ways that can violate type boundaries and permission.

Code security generally refers to the ability of the portable virtual machine to run code while offering it only a prescribed set of abilities. For example, the virtual machine might only allow the code access to a certain set of functions or data. The same controls over pointers which make automatic memory management possible and allow the virtual machine to ensure typesafe data access are used to assure that a code fragment is only allowed to certain elements of memory and cannot bypass the virtual machine itself. Other security mechanisms are then layered on top as code verifiers, stack verifiers, and other methods.

An interpreter allows programs made of virtual instructions to be loaded and run immediately without a potentially costly compile into native machine instructions. Any virtual machine which can be run can be interpreted, so the column designation here refers to whether the design includes provisions for efficient interpreting (for common usage).

Just-in-time compilation (JIT), refers to a method of compiling to native instructions at the latest possible time, usually immediately before or during the running of the program. The challenge of JIT is more one of implementation than of virtual machine design, however, modern designs have begun to make considerations to help efficiency. The simplest JIT methods simply compile to a code fragment similar to an offline compiler. However, more complex methods are often employed, which specialize compiled code fragments to parameters known only at runtime (see Adaptive optimization).

Ahead-of-time compilation (AOT) refers to the more classic method of using a precompiler to generate a set of native instructions which do not change during the runtime of the program. Because aggressive compiling and optimizing can take time, a precompiled program may launch faster than one which relies on JIT alone for execution. JVM implementations have mitigated this startup cost by initial interpreting to speed launch times, until native code fragments can be generated by JIT.

Shared libraries are a facility to reuse segments of native code across multiple running programs. In modern operating systems, this generally means using virtual memory to share the memory pages containing a shared library across different processes which are protected from each other via memory protection. It is interesting that aggressive JIT methods such as adaptive optimization often produce code fragments unsuitable for sharing across processes or successive runs of the program, requiring a tradeoff be made between the efficiencies of precompiled and shared code and the advantages of adaptively specialized code. For example, several design provisions of CIL are present to allow for efficient shared libraries, possibly at the cost of more specialized JIT code. The JVM implementation on OS X uses a Java Shared Archive[3] to provide some of the benefits of shared libraries.

Comparison of application virtual machine implementations[edit]

In addition to the portable virtual machines described above, virtual machines are often used as an execution model for individual scripting languages, usually by an interpreter. This table lists specific virtual machine implementations, both of the above portable virtual machines, and of scripting language virtual machines.

Virtual machine Languages Comments Interpreter JIT Implementation language SLoC
Common Language Runtime (CLR) C#, C++/CLI, F#, VB.NET bytecode is CIL; .NET Core Runtime on GitHub No Yes C#, C++
Adobe Flash Player (aka Tamarin) ActionScript, SWF (file format) interactive web authoring tool. bytecode is named "ActionScript Byte Code (.abc)" Yes Yes C++ 135k (initially released)
Dis (Inferno) Limbo Dis Virtual Machine Specification Yes Yes C 15k + 2850 per JIT arch + 500 per host OS
DotGNU-Portable.NET CLI languages including: C# Common Language Runtime clone No Yes C, C#
Forth Forth Features are simplified, usually include assembler, compiler, text-level and binary-level interpreters, sometimes editor, debugger and OS. Compiling speeds are >20 SKLOC/S and behave much like JIT. Yes No Forth, Forth Assembler 2.8K to 5.6K; advanced, professional implementations are smaller.
Glulx Inform 6, Inform 7, others Yes No Various implementations exist
HHVM PHP, Hack Is an open-source virtual machine designed for executing programs written in Hack and PHP. Yes Yes C++, OCaml
Icon Icon Base source code provides both the interpreter as well as an unsupported compile-to-C version. The runtime code, that is shared between the compiler and the interpreter, is written in a variant of C called RTT. Yes No C, RTT (a custom front-end to C, provided with the base source for Icon). ~180k total. (source to bytecode: ~11k, bytecode interpreter: ~46k, iconc: ~23k, common/headers: ~13k, rtt: ~15k)
JVM Java, Kotlin, Jython, Groovy, JRuby, C, C++, Clojure, Scala and several others Reference implementation by Sun ; OpenJDK: code under GPL ; IcedTea: code and tools under GPL Yes Yes JDK, OpenJDK & IcedTea with regular JIT : Java, C, C++, ASM ; IcedTea with the "Zero" JIT : Java, C, C++ JVM is around 6500k lines; TCK is 80k tests and around 1000k lines
LLVM C, C++, Kotlin, Objective-C, Swift, Ada, Fortran, and Rust MSIL, C and C++ output are supported. ActionScript Byte Code output is supported by Adobe Alchemy. bytecode is named "LLVM Bytecode (.bc)". assembly is named "LLVM Assembly Language (*.ll)". Yes Yes C++ 811k [4]
Lua Lua Yes LuaJIT C 13k + 7k LuaJIT
Mono CLI languages including: C#, VB.NET, IronPython, IronRuby, and others Common Language Runtime clone Yes Yes C#, C 2332k
NekoVM currently Neko and Haxe Yes x86 only C 46k
Oz Oz, Alice
O-code machine BCPL
p-code machine Pascal UCSD Pascal, widespread in late 70s including Apple II Yes No assembly, Pascal
Parrot Perl 5, Raku, NQP-rx, PIR, PASM, PBC, BASIC, bc, C99, ECMAScript, Lisp, Lua, m4, Tcl, WMLScript, XML, and others Yes Yes C, Perl 111k C, 240k Perl
Perl virtual machine Perl op-code tree walker Yes No C, Perl 175k C, 9k Perl
CPython Python Yes C 387k C, 368k Python, 10k ASM, 31k Psyco
PyPy Python Self-hosting implementation of Python, next generation of Psyco Yes Yes Python
Rubinius Ruby Virtual machine for another Ruby implementation Yes Yes C++, Ruby
Silverlight C#, VB.NET A Micro-version of Microsoft .NET Framework to let applications run sandboxed inside browser Yes Yes C++ 7MB (initially released)
ScummVM Scumm Computer game engine
SECD ISWIM, Lispkit Lisp
Squirrel Squirrel Yes Squirrel_JIT C++ 12k
Smalltalk Smalltalk
SQLite SQLite opcodes Virtual database engine
Squeak Squeak Smalltalk Self hosting implementation of Squeak virtual machine. Rich multi-media support. Yes Cog & Exupery Smalltalk/Slang 110k Smalltalk, ~300K C
SWI-Prolog Prolog: SWI-Prolog, YAP Yes No C, SWI-Prolog
TraceMonkey JavaScript Based on Tamarin No Yes C++ 173k
TrueType TrueType Font rendering engine Yes No C (typically)
Valgrind x86/x86-64 binaries Checking of memory accesses and leaks under Linux C 467k [5]
VisualWorks Smalltalk No Yes C
Vx32 virtual machine x86 binaries Application-level virtualization for native code No Yes
Waba Virtual machine for small devices, similar to Java
Yet Another Ruby VM (YARV) Ruby Virtual machine of the reference implementation for Ruby 1.9 and newer versions Yes Yes C
Z-machine Z-Code
Zend Engine PHP Yes No C 75k

See also[edit]


  1. ^ "The Java Community Process(SM) Program - JSRs: Java Specification Requests - detail JSR# 292". Retrieved 2013-07-04.
  2. ^ "JITRewrite – Parrot". Retrieved 2013-07-04.
  3. ^ Apple docs on OS X use of Java Shared Archive
  4. ^ The LLVM Compiler Infrastructure,, 2011 Nov 30
  5. ^ Valgrind,, 2011 Nov 30.