What is "vectorization"?

Solution 1:

Many CPUs have "vector" or "SIMD" instruction sets which apply the same operation simultaneously to two, four, or more pieces of data. Modern x86 chips have the SSE instructions, many PPC chips have the "Altivec" instructions, and even some ARM chips have a vector instruction set, called NEON.

"Vectorization" (simplified) is the process of rewriting a loop so that instead of processing a single element of an array N times, it processes (say) 4 elements of the array simultaneously N/4 times.

(I chose 4 because it's what modern hardware is most likely to directly support; the term "vectorization" is also used to describe a higher level software transformation where you might just abstract away the loop altogether and just describe operating on arrays instead of the elements that comprise them)

The difference between vectorization and loop unrolling: Consider the following very simple loop that adds the elements of two arrays and stores the results to a third array.

for (int i=0; i<16; ++i)
    C[i] = A[i] + B[i];

Unrolling this loop would transform it into something like this:

for (int i=0; i<16; i+=4) {
    C[i]   = A[i]   + B[i];
    C[i+1] = A[i+1] + B[i+1];
    C[i+2] = A[i+2] + B[i+2];
    C[i+3] = A[i+3] + B[i+3];
}

Vectorizing it, on the other hand, produces something like this:

for (int i=0; i<16; i+=4)
    addFourThingsAtOnceAndStoreResult(&C[i], &A[i], &B[i]);

Where "addFourThingsAtOnceAndStoreResult" is a placeholder for whatever intrinsic(s) your compiler uses to specify vector instructions. Note that some compilers are able to auto vectorize very simple loops like this, which can often be enabled via a compile option. More complex algorithms still require help from the programmer to generate good vector code.

Solution 2:

Vectorization is the term for converting a scalar program to a vector program. Vectorized programs can run multiple operations from a single instruction, whereas scalar can only operate on pairs of operands at once.

From wikipedia:

Scalar approach:

for (i = 0; i < 1024; i++)
{
   C[i] = A[i]*B[i];
}

Vectorized approach:

for (i = 0; i < 1024; i+=4)
{
   C[i:i+3] = A[i:i+3]*B[i:i+3];
}

Solution 3:

Vectorization is used greatly in scientific computing where huge chunks of data needs to be processed efficiently.

In real programming application , i know it's used in NUMPY(not sure of other else).

Numpy (package for scientific computing in python) , uses vectorization for speedy manipulation of n-dimensional array ,which generally is slower if done with in-built python options for handling arrays.

although tons of explanation are out there , HERE'S WHAT VECTORIZATION IS DEFINED AS IN NUMPY DOCUMENTATION PAGE

Vectorization describes the absence of any explicit looping, indexing, etc., in the code - these things are taking place, of course, just “behind the scenes” in optimized, pre-compiled C code. Vectorized code has many advantages, among which are:

1. vectorized code is more concise and easier to read

2. fewer lines of code generally means fewer bugs

3. the code more closely resembles standard mathematical notation (making it easier, typically, to correctly code mathematical constructs)

4. vectorization results in more “Pythonic” code. Without vectorization, our code would be littered with inefficient and difficult to read for loops.

Solution 4:

It refers to a the ability to do single mathematical operation on a list -- or "vector" -- of numbers in a single step. You see it often with Fortran because that's associated with scientific computing, which is associated with supercomputing, where vectorized arithmetic first appeared. Nowadays almost all desktop CPUs offer some form of vectorized arithmetic, through technologies like Intel's SSE. GPUs also offer a form of vectorized arithmetic.