Everything about Interpreted totally explained
In
computer science, an
interpreter normally means a
computer program that
executes, for example
performs, instructions written in a
programming language. 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 the
Java virtual machine are type 3: Java source programs are compiled ahead of time and stored as machine independent code, which is then
linked at run-time and executed by an interpreter (virtual machine). Some systems, such as
Smalltalk, and others, may also combine 2 and 3.
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.
Efficiency
The main disadvantage of interpreters is that when a program is interpreted, it 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's executed and then perform the desired action whereas the compiled code just performs the action. 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 (for example, 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 don't 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 isn't 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.
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 bytecode 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 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.
Foot notes
Further Information
Get more info on 'Interpreted'.
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