Strip Mining and Cleanup

Strip mining, also known as loop sectioning, is a loop transformation technique for enabling SIMD-encodings of loops, as well as providing a means of improving memory performance. By fragmenting a large loop into smaller segments or strips, this technique transforms the loop structure in two ways:

It increases the temporal and spatial locality in the data cache if the data are reusable in different passes of an algorithm.
It reduces the number of iterations of the loop by a factor of the length of each "vector," or number of operations being performed per SIMD operation. In the case of Streaming SIMD Extensions, this vector, or strip length, is reduced by 4 times: four floating-point data items per single Streaming SIMD Extensions single-precision floating-point SIMD operation are processed.

First introduced for vectorizers, this technique consists of the generation of code when each vector operation is done for a size less than or equal to the maximum vector length on a given vector machine.

The compiler automatically strip-mines your loop and generates a cleanup loop.

Before Vectorization

i=0;

while(i<n)

{

// Original loop code

a[i]=b[i]+c[i];

++i;

}

After Vectorization

// The vectorizer generates the following two loops

i=0;

while(i<(n-n%4))

{

// Vector strip-mined loop

// Subscript [i:i+3] denotes SIMD execution

a[i:i+3]=b[i:i+3]+c[i:i+3];

i=i+4;

}

while(i<n)

{

// Scalar clean-up loop

a[i]=b[i]+c[i];

++i;

}