In computer science, an interpreter is a computer program that directly executes instructions written in a programming or scripting language, without requiring them previously to have been compiled into a machine language program. An interpreter generally uses one of the following strategies for program execution:
- Parse the source code and perform its behavior directly;
- Translate source code into some efficient intermediate representation and immediately execute this;
- Explicitly execute stored precompiled code made by a compiler which is part of the interpreter system.
While interpretation and compilation are the two main means by which programming languages are implemented, they are not mutually exclusive, as most interpreting systems also perform some translation work, just like compilers. The terms "interpreted language" or "compiled language" signify that the canonical implementation of that language is an interpreter or a compiler, respectively. A high level language is ideally an abstraction independent of particular implementations.
History
Interpreters
were used as early as 1952 to ease programming within the limitations
of computers at the time (e.g. a shortage of program storage space, or
no native support for floating point numbers). Interpreters were also
used to translate between low-level machine languages, allowing code to
be written for machines that were still under construction and tested on
computers that already existed. The first interpreted high-level language was Lisp. Lisp was first implemented in 1958 by Steve Russell on an IBM 704 computer. Russell had read John McCarthy's paper, and realized (to McCarthy's surprise) that the Lisp eval function could be implemented in machine code.
The result was a working Lisp interpreter which could be used to run
Lisp programs, or more properly, "evaluate Lisp expressions".
Compilers versus interpreters
An illustration of the linking process. Object files and static libraries are assembled into a new library or executable
Programs written in a high level language are either directly executed by some kind of interpreter or converted into machine code by a compiler (and assembler and linker) for the CPU to execute.
While compilers (and assemblers) generally produce machine code
directly executable by computer hardware, they can often (optionally)
produce an intermediate form called object code. This is basically the same machine specific code but augmented with a symbol table
with names and tags to make executable blocks (or modules) identifiable
and relocatable. Compiled programs will typically use building blocks
(functions) kept in a library of such object code modules. A linker
is used to combine (pre-made) library files with the object file(s) of
the application to form a single executable file. The object files that
are used to generate an executable file are thus often produced at
different times, and sometimes even by different languages (capable of
generating the same object format).
A simple interpreter written in a low level language (e.g. assembly)
may have similar machine code blocks implementing functions of the high
level language stored, and executed when a function's entry in a look
up table points to that code. However, an interpreter written in a high
level language typically uses another approach, such as generating and
then walking a parse tree, or by generating and executing intermediate software-defined instructions, or both.
Thus, both compilers and interpreters generally turn source code
(text files) into tokens, both may (or may not) generate a parse tree,
and both may generate immediate instructions (for a stack machine, quadruple code,
or by other means). The basic difference is that a compiler system,
including a (built in or separate) linker, generates a stand-alone machine code program, while an interpreter system instead performs the actions described by the high level program.
A compiler can thus make almost all the conversions from source
code semantics to the machine level once and for all (i.e. until the
program has to be changed) while an interpreter has to do some of
this conversion work every time a statement or function is executed.
However, in an efficient interpreter, much of the translation work
(including analysis of types, and similar) is factored out and done only
the first time a program, module, function, or even statement, is run,
thus quite akin to how a compiler works. However, a compiled program
still runs much faster, under most circumstances, in part because
compilers are designed to optimize code, and may be given ample time for
this. This is especially true for simpler high level languages without
(many) dynamic data structures, checks, or type-checks.
In traditional compilation, the executable output of the linkers
(.exe files or .dll files or a library, see picture) is typically
relocatable when run under a general operating system, much like the
object code modules are but with the difference that this relocation is
done dynamically at run time, i.e. when the program is loaded for
execution. On the other hand, compiled and linked programs for small embedded systems are typically statically allocated, often hard coded in a NOR flash memory, as there is often no secondary storage and no operating system in this sense.
Historically, most interpreter-systems have had a self-contained
editor built in. This is becoming more common also for compilers (then
often called an IDE),
although some programmers prefer to use an editor of their choice and
run the compiler, linker and other tools manually. Historically,
compilers predate interpreters because hardware at that time could not
support both the interpreter and interpreted code and the typical batch
environment of the time limited the advantages of interpretation.
Development cycle
During the software development cycle,
programmers make frequent changes to source code. When using a
compiler, each time a change is made to the source code, they must wait
for the compiler to translate the altered source files and link
all of the binary code files together before the program can be
executed. The larger the program, the longer the wait. By contrast, a
programmer using an interpreter does a lot less waiting, as the
interpreter usually just needs to translate the code being worked on to
an intermediate representation (or not translate it at all), thus
requiring much less time before the changes can be tested. Effects are
evident upon saving the source code and reloading the program. Compiled
code is generally less readily debugged as editing, compiling, and
linking are sequential processes that have to be conducted in the proper
sequence with a proper set of commands. For this reason, many compilers
also have an executive aid, known as a Make
file and program. The Make file lists compiler and linker command lines
and program source code files, but might take a simple command line
menu input (e.g. "Make 3") which selects the third group (set) of
instructions then issues the commands to the compiler, and linker
feeding the specified source code files.
Distribution
A compiler
converts source code into binary instruction for a specific processor's
architecture, thus making it less portable. This conversion is made
just once, on the developer's environment, and after that the same
binary can be distributed to the user's machines where it can be
executed without further translation. A cross compiler can generate binary code for the user machine even if it has a different processor than the machine where the code is compiled.
An interpreted program can be distributed as source code. It
needs to be translated in each final machine, which takes more time but
makes the program distribution independent of the machine's
architecture. However, the portability of interpreted source code is
dependent on the target machine actually having a suitable interpreter.
If the interpreter needs to be supplied along with the source, the
overall installation process is more complex than delivery of a
monolithic executable since the interpreter itself is part of what need
be installed.
The fact that interpreted code can easily be read and copied by humans can be of concern from the point of view of copyright. However, various systems of encryption and obfuscation
exist. Delivery of intermediate code, such as bytecode, has a similar
effect to obfuscation, but bytecode could be decoded with a decompiler or disassembler.
Efficiency
The main disadvantage of interpreters is that an interpreted program typically runs slower than if it had been compiled.
The difference in speeds could be tiny or great; often an order of
magnitude and sometimes more. It generally takes longer to run a program
under an interpreter than to run the compiled code but it can take less
time to interpret it than the total time required to compile and run
it. This is especially important when prototyping and testing code when
an edit-interpret-debug cycle can often be much shorter than an
edit-compile-run-debug cycle.
Interpreting code is slower than running the compiled code because the interpreter must analyze each statement
in the program each time it is executed and then perform the desired
action, whereas the compiled code just performs the action within a
fixed context determined by the compilation. This run-time
analysis is known as "interpretive overhead". Access to variables is
also slower in an interpreter because the mapping of identifiers to
storage locations must be done repeatedly at run-time rather than at compile time.
There are various compromises between the development speed when using an interpreter and the execution speed when using a compiler. Some systems (such as some Lisps)
allow interpreted and compiled code to call each other and to share
variables. This means that once a routine has been tested and debugged
under the interpreter it can be compiled and thus benefit from faster
execution while other routines are being developed. Many interpreters do not execute the source code as it stands but convert it into some more compact internal form. Many BASIC interpreters replace keywords with single byte tokens which can be used to find the instruction in a jump table. A few interpreters, such as the PBASIC
interpreter, achieve even higher levels of program compaction by using a
bit-oriented rather than a byte-oriented program memory structure,
where commands tokens occupy perhaps 5 bits, nominally "16-bit"
constants are stored in a variable-length code
requiring 3, 6, 10, or 18 bits, and address operands include a "bit
offset". Many BASIC interpreters can store and read back their own
tokenized internal representation.
Toy expression interpreter
An interpreter might well use the same lexical analyzer and parser as the compiler and then interpret the resulting abstract syntax tree.
Example data type definitions for the latter, and a toy interpreter for syntax trees obtained from C expressions are shown in the box.
Regression
Interpretation
cannot be used as the sole method of execution: even though an
interpreter can itself be interpreted and so on, a directly executed
program is needed somewhere at the bottom of the stack because the code
being interpreted is not, by definition, the same as the machine code
that the CPU can execute.
Variations
Bytecode interpreters
There is a spectrum of possibilities between interpreting and
compiling, depending on the amount of analysis performed before the
program is executed. For example, Emacs Lisp is compiled to bytecode,
which is a highly compressed and optimized representation of the Lisp
source, but is not machine code (and therefore not tied to any
particular hardware). This "compiled" code is then interpreted by a
bytecode interpreter (itself written in C). The compiled code in this case is machine code for a virtual machine, which is implemented not in hardware, but in the bytecode interpreter. Such compiling interpreters are sometimes also called compreters.
In a bytecode interpreter each instruction starts with a byte, and
therefore bytecode interpreters have up to 256 instructions, although
not all may be used. Some bytecodes may take multiple bytes, and may be
arbitrarily complicated.
Control tables - that do not necessarily ever need to pass through a compiling phase - dictate appropriate algorithmic control flow via customized interpreters in similar fashion to bytecode interpreters.
Threaded code interpreters
Threaded code interpreters are similar to bytecode interpreters but
instead of bytes they use pointers. Each "instruction" is a word that
points to a function or an instruction sequence, possibly followed by a
parameter. The threaded code interpreter either loops fetching
instructions and calling the functions they point to, or fetches the
first instruction and jumps to it, and every instruction sequence ends
with a fetch and jump to the next instruction. Unlike bytecode there is
no effective limit on the number of different instructions other than
available memory and address space. The classic example of threaded code
is the Forth code used in Open Firmware systems: the source language is compiled into "F code" (a bytecode), which is then interpreted by a virtual machine.
Abstract syntax tree interpreters
In the spectrum between interpreting and compiling, another approach
is to transform the source code into an optimized abstract syntax tree
(AST), then execute the program following this tree structure, or use it
to generate native code just-in-time.
In this approach, each sentence needs to be parsed just once. As an
advantage over bytecode, the AST keeps the global program structure and
relations between statements (which is lost in a bytecode
representation), and when compressed provides a more compact
representation.
Thus, using AST has been proposed as a better intermediate format for
just-in-time compilers than bytecode. Also, it allows the system to
perform better analysis during runtime.
However, for interpreters, an AST causes more overhead than a
bytecode interpreter, because of nodes related to syntax performing no
useful work, of a less sequential representation (requiring traversal of
more pointers) and of overhead visiting the tree.
Just-in-time compilation
Further blurring the distinction between interpreters, bytecode
interpreters and compilation is just-in-time compilation (JIT), a
technique in which the intermediate representation is compiled to native
machine code
at runtime. This confers the efficiency of running native code, at the
cost of startup time and increased memory use when the bytecode or AST
is first compiled. Adaptive optimization
is a complementary technique in which the interpreter profiles the
running program and compiles its most frequently executed parts into
native code. Both techniques are a few decades old, appearing in
languages such as Smalltalk in the 1980s.
Just-in-time compilation has gained mainstream attention amongst language implementers in recent years, with Java, the .NET Framework, most modern JavaScript implementations, and Matlab now including JITs.
Self-interpreter
A self-interpreter is a programming language interpreter written in a programming language which can interpret itself; an example is a BASIC interpreter written in BASIC. Self-interpreters are related to self-hosting compilers.
If no compiler
exists for the language to be interpreted, creating a self-interpreter
requires the implementation of the language in a host language (which
may be another programming language or assembler). By having a first interpreter such as this, the system is bootstrapped and new versions of the interpreter can be developed in the language itself. It was in this way that Donald Knuth developed the TANGLE interpreter for the language WEB of the industrial standard TeX typesetting system.
Defining a computer language is usually done in relation to an abstract machine (so-called operational semantics) or as a mathematical function (denotational semantics).
A language may also be defined by an interpreter in which the semantics
of the host language is given. The definition of a language by a
self-interpreter is not well-founded (it cannot define a language), but a
self-interpreter tells a reader about the expressiveness and elegance
of a language. It also enables the interpreter to interpret its source
code, the first step towards reflective interpreting.
An important design dimension in the implementation of a
self-interpreter is whether a feature of the interpreted language is
implemented with the same feature in the interpreter's host language. An
example is whether a closure in a Lisp-like
language is implemented using closures in the interpreter language or
implemented "manually" with a data structure explicitly storing the
environment. The more features implemented by the same feature in the
host language, the less control the programmer of the interpreter has; a
different behavior for dealing with number overflows cannot be realized
if the arithmetic operations are delegated to corresponding operations
in the host language.
Some languages have an elegant self-interpreter, such as Lisp or Prolog. Much research on self-interpreters (particularly reflective interpreters) has been conducted in the Scheme programming language, a dialect of Lisp. In general, however, any Turing-complete
language allows writing of its own interpreter. Lisp is such a
language, because Lisp programs are lists of symbols and other lists.
XSLT is such a language, because XSLT programs are written in XML. A
sub-domain of meta-programming is the writing of domain-specific languages (DSLs).
Clive Gifford introduced a measure quality of self-interpreter (the eigenratio), the limit of the ratio between computer time spent running a stack of N self-interpreters and time spent to run a stack of N − 1 self-interpreters as N goes to infinity. This value does not depend on the program being run.
The book Structure and Interpretation of Computer Programs presents examples of meta-circular interpretation for Scheme and its dialects. Other examples of languages with a self-interpreter are Forth and Pascal.
Microcode
Microcode is a very commonly used technique "that imposes an
interpreter between the hardware and the architectural level of a
computer". As such, the microcode is a layer of hardware-level instructions that implement higher-level machine code instructions or internal state machine sequencing in many digital processing elements. Microcode is used in general-purpose central processing units, as well as in more specialized processors such as microcontrollers, digital signal processors, channel controllers, disk controllers, network interface controllers, network processors, graphics processing units, and in other hardware.
Microcode typically resides in special high-speed memory and translates machine instructions, state machine
data or other input into sequences of detailed circuit-level
operations. It separates the machine instructions from the underlying electronics
so that instructions can be designed and altered more freely. It also
facilitates the building of complex multi-step instructions, while
reducing the complexity of computer circuits. Writing microcode is often
called microprogramming and the microcode in a particular processor implementation is sometimes called a microprogram.
More extensive microcoding allows small and simple microarchitectures to emulate more powerful architectures with wider word length, more execution units and so on, which is a relatively simple way to achieve software compatibility between different products in a processor family.
Applications
- Interpreters are frequently used to execute command languages, and glue languages since each operator executed in command language is usually an invocation of a complex routine such as an editor or compiler.
- Self-modifying code can easily be implemented in an interpreted language. This relates to the origins of interpretation in Lisp and artificial intelligence research.
- Virtualization. Machine code intended for a hardware architecture can be run using a virtual machine. This is often used when the intended architecture is unavailable, or among other uses, for running multiple copies.
- Sandboxing: While some types of sandboxes rely on operating system protections, an interpreter or virtual machine is often used. The actual hardware architecture and the originally intended hardware architecture may or may not be the same. This may seem pointless, except that sandboxes are not compelled to actually execute all the instructions the source code it is processing. In particular, it can refuse to execute code that violates any security constraints it is operating under.
- Emulators for running computer software written for obsolete and unavailable hardware on more modern equipment.
