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  2. Comparison of programming languages (array) - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_programming...

    For example, to perform an element by element sum of two arrays, a and b to produce a third c, it is only necessary to write 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)

  3. Array programming - Wikipedia

    en.wikipedia.org/wiki/Array_programming

    The cross product operation is an example of a vector rank function because it operates on vectors, not scalars. Matrix multiplication is an example of a 2-rank function, because it operates on 2-dimensional objects (matrices). Collapse operators reduce the dimensionality of an input data array by one or more dimensions. For example, summing ...

  4. Vectorization (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Vectorization_(mathematics)

    For a symmetric matrix A, the vector vec(A) contains more information than is strictly necessary, since the matrix is completely determined by the symmetry together with the lower triangular portion, that is, the n(n + 1)/2 entries on and below the main diagonal. For such matrices, the half-vectorization is

  5. MATLAB - Wikipedia

    en.wikipedia.org/wiki/MATLAB

    MATLAB does include standard for and while loops, but (as in other similar applications such as APL and R), using the vectorized notation is encouraged and is often faster to execute. The following code, excerpted from the function magic.m , creates a magic square M for odd values of n (MATLAB function meshgrid is used here to generate square ...

  6. Foreach loop - Wikipedia

    en.wikipedia.org/wiki/Foreach_loop

    foreach is usually used in place of a standard for loop statement. Unlike other for loop constructs, however, foreach loops [1] usually maintain no explicit counter: they essentially say "do this to everything in this set", rather than "do this x times". This avoids potential off-by-one errors and makes code simpler to read.

  7. Automatic vectorization - Wikipedia

    en.wikipedia.org/wiki/Automatic_vectorization

    Automatic vectorization, in parallel computing, is a special case of automatic parallelization, where a computer program is converted from a scalar implementation, which processes a single pair of operands at a time, to a vector implementation, which processes one operation on multiple pairs of operands at once.

  8. Row- and column-major order - Wikipedia

    en.wikipedia.org/wiki/Row-_and_column-major_order

    Note how the use of A[i][j] with multi-step indexing as in C, as opposed to a neutral notation like A(i,j) as in Fortran, almost inevitably implies row-major order for syntactic reasons, so to speak, because it can be rewritten as (A[i])[j], and the A[i] row part can even be assigned to an intermediate variable that is then indexed in a separate expression.

  9. Loop dependence analysis - Wikipedia

    en.wikipedia.org/wiki/Loop_dependence_analysis

    In this example, code block 1 shows loop-dependent dependence between statement S2 iteration i and statement S1 iteration i-1. This is to say that statement S2 cannot proceed until statement S1 in the previous iteration finishes. Code block 2 show loop independent dependence between statements S1 and S2 in the same iteration.