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Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.
Written in C++ and published under an MIT license, HiGHS provides programming interfaces to C, Python, Julia, Rust, R, JavaScript, Fortran, and C#. It has no external dependencies. A convenient thin wrapper to Python is available via the highspy PyPI package. Although generally single-threaded, some solver components can utilize multi-core ...
Concurrent and parallel programming languages involve multiple timelines. Such languages provide synchronization constructs whose behavior is defined by a parallel execution model. A concurrent programming language is defined as one which uses the concept of simultaneously executing processes or threads of execution as a means of structuring a ...
SequenceL is a general purpose functional programming language and auto-parallelizing (Parallel computing) compiler and tool set, whose primary design objectives are performance on multi-core processor hardware, ease of programming, platform portability
Texas Multicore Technologies: 1989 2012 2.4 10 February 2016: Free (Community Edition), $2495 (Professional Edition) Proprietary: Functional programming language and tools. [Note 3] S-Lang: John E. Davis 1992 2.3.0 18 September 2014: Free GPL, Artistic License (1.x only) Available as a standalone (slsh) and embedded interpreter (jed, slrn ...
Scala—general purpose, designed to express common programming patterns in a concise, elegant, and type-safe way; SequenceL—general purpose functional, main design objectives are ease of programming, code clarity-readability, and automatic parallelization for performance on multicore hardware, and provably free of race conditions; SR—for ...
The core is the computing unit of the processor and in multi-core processors each core is independent and can access the same memory concurrently. Multi-core processors have brought parallel computing to desktop computers. Thus parallelization of serial programs has become a mainstream programming task.
Python: PyLinda; Ruby: Rinda; Swift: pSpaces; Some of the more notable Linda implementations include: C-Linda or TCP-Linda - the earliest commercial and a widespread implementation of virtual shared memory for supercomputers and clustered systems from Scientific Computing Associates, founded by Martin Schultz.