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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.
Vectors allow random access; that is, an element of a vector may be referenced in the same manner as elements of arrays (by array indices). Linked-lists and sets , on the other hand, do not support random access or pointer arithmetic.
Modern C++ compilers are tuned to minimize abstraction penalties arising from heavy use of the STL. The STL was created as the first library of generic algorithms and data structures for C++, with four ideas in mind: generic programming, abstractness without loss of efficiency, the Von Neumann computation model, [2] and value semantics.
In linear algebra, the outer product of two coordinate vectors is the matrix whose entries are all products of an element in the first vector with an element in the second vector. If the two coordinate vectors have dimensions n and m, then their outer product is an n × m matrix.
The formalism of dyadic algebra is an extension of vector algebra to include the dyadic product of vectors. The dyadic product is also associative with the dot and cross products with other vectors, which allows the dot, cross, and dyadic products to be combined to obtain other scalars, vectors, or dyadics.
Sub-vectors – elements may typically contain two, three or four sub-elements (vec2, vec3, vec4) where any given bit of a predicate mask applies to the whole vec2/3/4, not the elements in the sub-vector. Sub-vectors are also introduced in RISC-V RVV (termed "LMUL"). [32] Subvectors are a critical integral part of the Vulkan SPIR-V spec.
What does a pair of orthonormal vectors in 2-D Euclidean space look like? Let u = (x 1 , y 1 ) and v = (x 2 , y 2 ). Consider the restrictions on x 1 , x 2 , y 1 , y 2 required to make u and v form an orthonormal pair.
In multilinear algebra, a tensor contraction is an operation on a tensor that arises from the canonical pairing of a vector space and its dual.In components, it is expressed as a sum of products of scalar components of the tensor(s) caused by applying the summation convention to a pair of dummy indices that are bound to each other in an expression.