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Due to Python’s Global Interpreter Lock, local threads provide parallelism only when the computation is primarily non-Python code, which is the case for Pandas DataFrame, Numpy arrays or other Python/C/C++ based projects. Local process A multiprocessing scheduler leverages Python’s concurrent.futures.ProcessPoolExecutor to execute computations.
This article lists concurrent and parallel programming languages, categorizing them by a defining paradigm.Concurrent and parallel programming languages involve multiple timelines.
Implementations of the fork–join model will typically fork tasks, fibers or lightweight threads, not operating-system-level "heavyweight" threads or processes, and use a thread pool to execute these tasks: the fork primitive allows the programmer to specify potential parallelism, which the implementation then maps onto actual parallel execution. [1]
OpenMP (Open Multi-Processing) is an application programming interface (API) that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran, [3] on many platforms, instruction-set architectures and operating systems, including Solaris, AIX, FreeBSD, HP-UX, Linux, macOS, and Windows.
Some parallel programming systems, such as OpenMP and Cilk, have language support for the map pattern in the form of a parallel for loop; [2] languages such as OpenCL and CUDA support elemental functions (as "kernels") at the language level. The map pattern is typically combined with other parallel design patterns.
Exploitation of the concept of data parallelism started in 1960s with the development of the Solomon machine. [1] The Solomon machine, also called a vector processor, was developed to expedite the performance of mathematical operations by working on a large data array (operating on multiple data in consecutive time steps).
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...
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 execution can be implemented with limited overhead.