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Array programming is very well suited to implicit parallelization; a topic of much research nowadays.Further, Intel and compatible CPUs developed and produced after 1997 contained various instruction set extensions, starting from MMX and continuing through SSSE3 and 3DNow!, which include rudimentary SIMD array capabilities.
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]
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 supported.
Array, a sequence of elements of the same type stored contiguously in memory; Record (also called a structure or struct), a collection of fields Product type (also called a tuple), a record in which the fields are not named; String, a sequence of characters representing text; Union, a datum which may be one of a set of types
An array data structure can be mathematically modeled as an abstract data structure (an abstract array) with two operations get(A, I): the data stored in the element of the array A whose indices are the integer tuple I. set(A, I, V): the array that results by setting the value of that element to V. These operations are required to satisfy the ...
Instead, they used array operations, and use of structured programming constructs was often unneeded, since an operation could be performed on a full array in one statement. For example, the iota function ( ι ) can replace for-loop iteration : ιN when applied to a scalar positive integer yields a one-dimensional array (vector), 1 2 3 ...
Programming languages or their standard libraries that support multi-dimensional arrays typically have a native row-major or column-major storage order for these arrays. Row-major order is used in C / C++ / Objective-C (for C-style arrays), PL/I , [ 4 ] Pascal , [ 5 ] Speakeasy , [ citation needed ] and SAS .
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) 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. [3]