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.
CPython can be defined as both an interpreter and a compiler as it compiles Python code into bytecode before interpreting it. It has a foreign function interface with several languages, including C, in which one must explicitly write bindings in a language other than Python.
A particular feature of CPython is that it makes use of a global interpreter lock (GIL) on each CPython interpreter process, which means that within a single process, only one thread may be processing Python bytecode at any one time. This does not mean that there is no point in multithreading; the most common multithreading scenario is where threads are mostly waiting on external processes to complete.
This can happen when multiple threads are servicing separate clients. One thread may be waiting for a client to reply, and another may be waiting for a database query to execute, while the third thread is actually processing Python code.
However, the GIL does mean that CPython is not suitable for processes that implement CPU-intensive algorithms in Python code that could potentially be distributed across multiple cores.
In real-world applications, situations where the GIL is a significant bottleneck are quite rare. This is because Python is an inherently slow language and is generally not used for CPU-intensive or time-sensitive operations. Python is typically used at the top level and calls functions in libraries to perform specialized tasks. These libraries are generally not written in Python, and Python code in another thread can be executed while a call to one of these underlying processes takes place. The non-Python library being called to perform the CPU-intensive task is not subject to the GIL and may concurrently execute many threads on multiple processors without restriction.
Concurrency of Python code can only be achieved with separate CPython interpreter processes managed by a multitasking operating system.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
In computer programming, a runtime system or runtime environment is a sub-system that exists both in the computer where a program is created, as well as in the computers where the program is intended to be run. The name comes from the compile time and runtime division from compiled languages, which similarly distinguishes the computer processes involved in the creation of a program (compilation) and its execution in the target machine (the run time). Most programming languages have some form of runtime system that provides an environment in which programs run.
Application virtualization software refers to both application virtual machines and software responsible for implementing them. Application virtual machines are typically used to allow application bytecode to run portably on many different computer architectures and operating systems. The application is usually run on the computer using an interpreter or just-in-time compilation (JIT). There are often several implementations of a given virtual machine, each covering a different set of functions.
Jython is an implementation of the Python programming language designed to run on the Java platform. The implementation was formerly known as JPython until 1999. Jython programs can import and use any Java class. Except for some standard modules, Jython programs use Java classes instead of Python modules. Jython includes almost all of the modules in the standard Python programming language distribution, lacking only some of the modules implemented originally in C. For example, a user interface in Jython could be written with Swing, AWT or SWT.
Covers the Nearest Neighbor search algorithm and the Johnson-Lindenstrauss lemma for dimensionality reduction, exploring preprocessing techniques and locality-sensitive hashing.
In order to improve their power efficiency and computational capacity, modern servers are adopting hardware accelerators, especially GPUs. Modern analytical DMBS engines have been highly optimized for multi-core multi-CPU query execution, but lack the nece ...