c++ - Does the restrict keyword provide significant benefits in gcc/g++?

Question

Has anyone ever seen any numbers/analysis on whether or not use of the C/C++ restrict keyword in gcc/g++ actual provides any significant performance boost in reality (and not just in theory)?

I've read various articles recommending / disparaging its use, but I haven't ran across any real numbers practically demonstrating either sides arguments.

EDIT

I know that restrict is not officially part of C++, but it is supported by some compilers and I've read a paper by Christer Ericson which strongly recommends its usage.

Answer 1

The restrict keyword does a difference.

I've seen improvements of factor 2 and more in some situations (image processing). Most of the time the difference is not that large though. About 10%.

Here is a little example that illustrate the difference. I've written a very basic 4x4 vector * matrix transform as a test. Note that I have to force the function not to be inlined. Otherwise GCC detects that there aren't any aliasing pointers in my benchmark code and restrict wouldn't make a difference due to inlining.

I could have moved the transform function to a different file as well.

#include <math.h>

#ifdef USE_RESTRICT
#else
#define __restrict
#endif


void transform (float * __restrict dest, float * __restrict src, 
                float * __restrict matrix, int n) __attribute__ ((noinline));

void transform (float * __restrict dest, float * __restrict src, 
                float * __restrict matrix, int n)
{
  int i;

  // simple transform loop.

  // written with aliasing in mind. dest, src and matrix 
  // are potentially aliasing, so the compiler is forced to reload
  // the values of matrix and src for each iteration.

  for (i=0; i<n; i++)
  {
    dest[0] = src[0] * matrix[0] + src[1] * matrix[1] + 
              src[2] * matrix[2] + src[3] * matrix[3];

    dest[1] = src[0] * matrix[4] + src[1] * matrix[5] + 
              src[2] * matrix[6] + src[3] * matrix[7];

    dest[2] = src[0] * matrix[8] + src[1] * matrix[9] + 
              src[2] * matrix[10] + src[3] * matrix[11];

    dest[3] = src[0] * matrix[12] + src[1] * matrix[13] + 
              src[2] * matrix[14] + src[3] * matrix[15];

    src  += 4;
    dest += 4;
  }
}

float srcdata[4*10000];
float dstdata[4*10000];

int main (int argc, char**args)
{
  int i,j;
  float matrix[16];

  // init all source-data, so we don't get NANs  
  for (i=0; i<16; i++)   matrix[i] = 1;
  for (i=0; i<4*10000; i++) srcdata[i] = i;

  // do a bunch of tests for benchmarking. 
  for (j=0; j<10000; j++)
    transform (dstdata, srcdata, matrix, 10000);
}

---

Results: (on my 2 Ghz Core Duo)

nils@doofnase:~$ gcc -O3 test.c
nils@doofnase:~$ time ./a.out

real    0m2.517s
user    0m2.516s
sys     0m0.004s

nils@doofnase:~$ gcc -O3 -DUSE_RESTRICT test.c
nils@doofnase:~$ time ./a.out

real    0m2.034s
user    0m2.028s
sys     0m0.000s

Over the thumb 20% faster execution, on that system.

To show how much it depends on the architecture I've let the same code run on a Cortex-A8 embedded CPU (adjusted the loop count a bit cause I don't want to wait that long):

root@beagleboard:~# gcc -O3 -mcpu=cortex-a8 -mfpu=neon -mfloat-abi=softfp test.c
root@beagleboard:~# time ./a.out

real    0m 7.64s
user    0m 7.62s
sys     0m 0.00s

root@beagleboard:~# gcc -O3 -mcpu=cortex-a8 -mfpu=neon -mfloat-abi=softfp -DUSE_RESTRICT test.c 
root@beagleboard:~# time ./a.out

real    0m 7.00s
user    0m 6.98s
sys     0m 0.00s

Here the difference is just 9% (same compiler btw.)