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  2. Vector processor - Wikipedia

    en.wikipedia.org/wiki/Vector_processor

    If the time taken for the vector unit to add an array of 64 numbers is 10 times faster than its equivalent scalar counterpart, r = 10. Also, if the total number of operations in a program is 100, out of which only 10 are scalar (after vectorization), then f = 0.9, i.e., 90% of the work is done by the vector unit.

  3. Array programming - Wikipedia

    en.wikipedia.org/wiki/Array_programming

    In array languages, operations are generalized to apply to both scalars and arrays. Thus, a+b expresses the sum of two scalars if a and b are scalars, or the sum of two arrays if they are arrays. An array language simplifies programming but possibly at a cost known as the abstraction penalty.

  4. Vector space - Wikipedia

    en.wikipedia.org/wiki/Vector_space

    A linear subspace or vector subspace W of a vector space V is a non-empty subset of V that is closed under vector addition and scalar multiplication; that is, the sum of two elements of W and the product of an element of W by a scalar belong to W. [10] This implies that every linear combination of elements of W belongs to W. A linear subspace ...

  5. AltiVec - Wikipedia

    en.wikipedia.org/wiki/AltiVec

    The "vector" type keyword is introduced to permit the declaration of native vector types, e.g., "vector unsigned char foo;" declares a 128-bit vector variable named "foo" containing sixteen 8-bit unsigned chars. The full complement of arithmetic and binary operators is defined on vector types so that the normal C expression language can be used ...

  6. 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 sometimes more useful than the ...

  7. Vector calculus identities - Wikipedia

    en.wikipedia.org/wiki/Vector_calculus_identities

    The dotted vector, in this case B, is differentiated, while the (undotted) A is held constant. The utility of the Feynman subscript notation lies in its use in the derivation of vector and tensor derivative identities, as in the following example which uses the algebraic identity C⋅(A×B) = (C×A)⋅B:

  8. IBM OpenDX - Wikipedia

    en.wikipedia.org/wiki/IBM_OpenDX

    OpenDX screen-shot showing visualization of a scalar field. In this example we show the visualization of the flow of a particulated material (dust) inside a building. In this case the quantity visualized is a scalar, namely the concentration of the particles. The concentration of particles at each point in space is represented using colors.

  9. Linear map - Wikipedia

    en.wikipedia.org/wiki/Linear_map

    In mathematics, and more specifically in linear algebra, a linear map (also called a linear mapping, linear transformation, vector space homomorphism, or in some contexts linear function) is a mapping between two vector spaces that preserves the operations of vector addition and scalar multiplication.

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