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Interpreter (computing)
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In computer science, an interpreter normally means a computer program that executes, i.e. performs, instructions written in a programming language.
The terms interpreted language or compiled language merely mean that the canonical implementation of that language is an interpreter or a compiler; a high level language is basically an abstraction which is (ideally) independent of particular implementations.
While interpretation and compilation are the two principal means by which programming languages are implemented, these are not fully distinct categories, one of the reasons being that most interpreting systems also perform some translation work, just like compilers.
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In computer science, an interpreter normally means a computer program that executes, i.e. performs, instructions written in a programming language.
The terms interpreted language or compiled language merely mean that the canonical implementation of that language is an interpreter or a compiler; a high level language is basically an abstraction which is (ideally) independent of particular implementations.
While interpretation and compilation are the two principal means by which programming languages are implemented, these are not fully distinct categories, one of the reasons being that most interpreting systems also perform some translation work, just like compilers. An interpreter may be a program that either
- executes the source code directly
- translates source code into some efficient intermediate representation (code) and immediately executes this
- explicitly executes stored precompiled code made by a compiler which is part of the interpreter system
Perl, Python, MATLAB, and Ruby are examples of type 2, while UCSD Pascal and Java are type 3.
The intermediate representation can be machine independent code, which is then linked at run-time and executed by an interpreter and/or compiler (for JIT systems). General purpose market languages, such as Smalltalk, Java, Python and others generally employ JIT.
Efficiency
The main disadvantage of interpreters is that when a program is interpreted, it 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 (e.g., 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. For example, some BASIC interpreters replace keywords with single byte tokens which can be used to find the instruction in a jump table. An interpreter might well use the same lexical analyzer and parser as the compiler and then interpret the resulting abstract syntax tree.
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. The same approach is used with 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 transforming the source code into an optimized Abstract Syntax Tree (AST), and then proceeding to execute the program following this tree structure. 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 provides a more compact representation.
Thus, AST has been proposed as a better intermediate format for Just-in-time compilers than bytecode. Also, it allows to perform better analysis during runtime. An AST-based Java interpreter has been proved to be faster than a similar bytecode-based interpreter, thanks to the more powerful optimizations allowed by having the complete structure of the program, as well as higher level typing, available during execution.
Just-in-time compilation
Further blurring the distinction between interpreters, byte-code interpreters and compilation is just-in-time compilation (or 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 implementors in recent years, with Java, Python and the .NET Framework all now including JITs.
Punched card interpreter
The term "interpreter" often referred to a piece of unit record equipment that could read punched cards and print the characters in human-readable form on the card. The IBM 550 Numeric Interpreter and IBM 557 Alphabetic Interpreter are typical examples from 1930 and 1954, respectively.
See also
External links
- A tiny Interpreter written in Scheme, which provides to interpret PUBA-GUMP (a subset of BASIC) in Scheme
- page at Columbia University
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