In computing, a context switch is the process of storing the state of a process or thread, so that it can be restored and resume execution at a later point, and then restoring a different, previously saved, state. This allows multiple processes to share a single central processing unit (CPU), and is an essential feature of a multiprogramming or multitasking operating system. In a traditional CPU, each process - a program in execution - utilizes the various CPU registers to store data and hold the current state of the running process. However, in a multitasking operating system, the operating system switches between processes or threads to allow the execution of multiple processes simultaneously. For every switch, the operating system must save the state of the currently running process, followed by loading the next process state, which will run on the CPU. This sequence of operations that stores the state of the running process and loads the following running process is called a context switch.
Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.
In computer science, a thread of execution is the smallest sequence of programmed instructions that can be managed independently by a scheduler, which is typically a part of the operating system. In many cases, a thread is a component of a process.
In computing, scheduling is the action of assigning resources to perform tasks. The resources may be processors, network links or expansion cards. The tasks may be threads, processes or data flows.
Coroutines are computer program components that allow execution to be suspended and resumed, generalizing subroutines for cooperative multitasking. Coroutines are well-suited for implementing familiar program components such as cooperative tasks, exceptions, event loops, iterators, infinite lists and pipes.
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. The predecessor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. NumPy is open-source software and has many contributors. NumPy is a NumFOCUS fiscally sponsored project.
Jython is an implementation of the Python programming language designed to run on the Java platform. It was known as JPython until 1999.
IronPython is an implementation of the Python programming language targeting the .NET and Mono frameworks. The project is currently maintained by a group of volunteers at GitHub. It is free and open-source software, and can be implemented with Python Tools for Visual Studio, which is a free and open-source extension for Microsoft's Visual Studio IDE.
CPython is the reference implementation of the Python programming language. Written in C and Python, CPython is the default and most widely used implementation of the Python language.
PyPy is an implementation of the Python programming language. PyPy often runs faster than the standard implementation CPython because PyPy uses a just-in-time compiler. 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.
A protothread is a low-overhead mechanism for concurrent programming.
In computer security, executable-space protection marks memory regions as non-executable, such that an attempt to execute machine code in these regions will cause an exception. It makes use of hardware features such as the NX bit, or in some cases software emulation of those features. However, technologies that emulate or supply an NX bit will usually impose a measurable overhead while using a hardware-supplied NX bit imposes no measurable overhead.
In computer science, a fiber is a particularly lightweight thread of execution.
In computer programming, a green thread is a thread that is scheduled by a runtime library or virtual machine (VM) instead of natively by the underlying operating system (OS). Green threads emulate multithreaded environments without relying on any native OS abilities, and they are managed in user space instead of kernel space, enabling them to work in environments that do not have native thread support.
A global interpreter lock (GIL) is a mechanism used in computer-language interpreters to synchronize the execution of threads so that only one native thread can execute basic operations at a time. As a general rule, an interpreter that uses GIL will see only one thread to execute at a time, even if it runs on a multi-core processor, although some implementations provide for CPU intensive code to release the GIL, allowing multiple threads to use multiple cores. Some popular interpreters that have GIL are CPython and Ruby MRI.
Cython is a superset of the programming language Python, which allows developers to write Python code that yields performance comparable to that of C.
MicroPython is a software implementation of a programming language largely compatible with Python 3, written in C, that is optimized to run on a microcontroller.
Nuitka is a source-to-source compiler which compiles Python code to C source code, applying some compile-time optimizations in the process such as constant folding and propagation, built-in call prediction, type inference, and conditional statement execution. Nuitka initially was designed to produce C++ code, but current versions produce C source code using only those features of C11 that are shared by C++03, enabling further compilation to a binary executable format by modern C and C++ compilers including gcc, clang, MinGW, or Microsoft Visual C++. It accepts Python code compatible with several different Python versions and optionally allows for the creation of standalone programs that do not require Python to be installed on the target computer.
Tim Peters is a software developer who is known for creating the Timsort hybrid sorting algorithm and for his major contributions to the Python programming language and its original CPython implementation. A pre-1.0 CPython user, he was among the group of early adopters who contributed to the detailed design of the language in its early stages.
