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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 ...
An interpreter is a program that reads another program, typically as text, [4] as seen in languages like Python. [2] Interpreters read code, and produce the result directly. [8] Interpreters typically read code line by line, and parse it to convert and execute the code as operations and actions. [9]
In computer programming, a programming language implementation is a system for executing computer programs. There are two general approaches to programming language implementation: [1] Interpretation: The program is read as input by an interpreter, which performs the actions written in the program. [2]
In computer programming, machine code is computer code consisting of machine language instructions, which are used to control a computer's central processing unit (CPU). For conventional binary computers, machine code is the binary representation of a computer program which is actually read and interpreted by the computer. A program in machine ...
Numba is used from Python, as a tool (enabled by adding a decorator to relevant Python code), a JIT compiler that translates a subset of Python and NumPy code into fast machine code. Pythran compiles a subset of Python 3 to C++ . [165] RPython can be compiled to C, and is used to build the PyPy interpreter of Python.
Concurrency of Python code can only be achieved with separate CPython interpreter processes managed by a multitasking operating system. This complicates communication between concurrent Python processes , though the multiprocessing module mitigates this somewhat; it means that applications that really can benefit from concurrent Python-code ...
Since object code is not created in the interpretation process, less memory is required for the code. [5] Interpreter languages do not create machine-specific code and can be executed on any type of machine. [7] The development and debugging process is typically quicker due to less complexity and it has more flexibility. [7] There are clear ...
Most Python code runs well on PyPy except for code that depends on CPython extensions, which either does not work or incurs some overhead when run in PyPy. PyPy itself is built using a technique known as meta-tracing, which is a mostly automatic transformation that takes an interpreter as input and produces a tracing just-in-time compiler as ...