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In C++, a class can overload all of the pointer operations, so an iterator can be implemented that acts more or less like a pointer, complete with dereference, increment, and decrement. This has the advantage that C++ algorithms such as std::sort can immediately be applied to plain old memory buffers, and that there is no new syntax to learn.
One same container type can have more than one associated iterator type; for instance the std::vector<T> container type allows traversal either using (raw) pointers to its elements (of type *<T>), or values of a special type std::vector<T>::iterator, and yet another type is provided for "reverse iterators", whose operations are defined in such ...
C++11 provides a foreach loop. ... in is the only kind of for loop in Python, ... SystemVerilog supports iteration over any vector or array type of any dimensionality ...
The dependency graph contains all local dependencies with distance not greater than the vector size. So, if the vector register is 128 bits, and the array type is 32 bits, the vector size is 128/32 = 4. All other non-cyclic dependencies should not invalidate vectorization, since there won't be any concurrent access in the same vector instruction.
In Python NumPy arrays implement the flatten method, [note 1] while in R the desired effect can be achieved via the c() or as.vector() functions. In R , function vec() of package 'ks' allows vectorization and function vech() implemented in both packages 'ks' and 'sn' allows half-vectorization.
Common examples of array slicing are extracting a substring from a string of characters, the "ell" in "hello", extracting a row or column from a two-dimensional array, or extracting a vector from a matrix. Depending on the programming language, an array slice can be made out of non-consecutive elements.
The Nial example of the inner product of two arrays can be implemented using the native matrix multiplication operator. If a is a row vector of size [1 n] and b is a corresponding column vector of size [n 1]. a * b; By contrast, the entrywise product is implemented as: 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 supported.