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CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.
Turbo C is a discontinued integrated development environment (IDE) and compiler for the C programming language from Borland. First introduced in 1987, it was noted for its integrated development environment, small size, fast compile speed, comprehensive manuals and low price.
The results are made callable in Python through the ctypes library. Comes installed on JavaScript Linux [13] (also by Bellard). Has been used as a reference for the compiled version of the Super Micro-Max Chess program source. [14] Bun, the JavaScript runtime, uses TCC to expose an API which allows users to compile and run C programs from ...
c = a + b In addition to support for vectorized arithmetic and relational operations, these languages also vectorize common mathematical functions such as sine. For example, if x is an array, then y = sin (x) will result in an array y whose elements are sine of the corresponding elements of the array x. Vectorized index operations are also ...
For example, in the Pascal programming language, the declaration type MyTable = array [1.. 4, 1.. 2] of integer, defines a new array data type called MyTable. The declaration var A: MyTable then defines a variable A of that type, which is an aggregate of eight elements, each being an integer variable identified by two indices.
pyFAI, [99] Fast Azimuthal Integration in Python; Random123, [100] library of counter-based random number generators; SecondSpace, [101] simulation software for waves in 2D space; StarPU, [102] task programming library; Theano: Python array library [103] [104] UFO, [105] [106] data processing framework; VexCL, [107] [108] vector expression ...
Array programming primitives concisely express broad ideas about data manipulation. The level of concision can be dramatic in certain cases: it is not uncommon [example needed] to find array programming language one-liners that require several pages of object-oriented code.
The first digital computers used machine-language programming to set up and access array structures for data tables, vector and matrix computations, and for many other purposes. John von Neumann wrote the first array-sorting program in 1945, during the building of the first stored-program computer. [6]