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Consider a smart vector type, such as C++'s std:: vector or Java's ArrayList. When an item x is added to a vector v, the vector must actually add x to the internal list of objects and update a count field that says how many objects are in v. It may also need to allocate new memory if the existing capacity isn't sufficient. Exception safety ...
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.
This example starts with an algorithm ("IAXPY"), first show it in scalar instructions, then SIMD, then predicated SIMD, and finally vector instructions. This incrementally helps illustrate the difference between a traditional vector processor and a modern SIMD one. The example starts with a 32-bit integer variant of the "DAXPY" function, in C:
Vector processors, some SIMD ISAs (such as AVX2 and AVX-512) and GPUs in general make heavy use of predication, applying one bit of a conditional mask vector to the corresponding elements in the vector registers being processed, whereas scalar predication in scalar instruction sets only need the one predicate bit.
For example, LINPACK is a general purpose library that can be used on many different machines without modification. LINPACK could use a generic version of BLAS. To gain performance, different machines might use tailored versions of BLAS. As computer architectures became more sophisticated, vector machines appeared. BLAS for a vector machine ...
Note that the examples given there do not work as-is on modern Windows systems (post XP SP2) due to the changes Microsoft made to address the security issues present in the early SEH design. The examples still work on later versions of Windows if compiled with /link /safeseh:no. "win32: Safe Structured Exception Handling". Yasm manual.
Intrinsic functions are often used to explicitly implement vectorization and parallelization in languages which do not address such constructs. Some application programming interfaces (API), for example, AltiVec and OpenMP, use intrinsic functions to declare, respectively, vectorizable and multiprocessing-aware operations during compiling.
Examples of its use include sparse linear algebra operations, [1] sorting algorithms, fast Fourier transforms, [2] and some computational graph theory problems. [3] It is the vector equivalent of register indirect addressing, with gather involving indexed reads, and scatter, indexed writes.