example_r3.cc

This is an example of how to use the prepare_alg5() function and alg5() function in the GPU.

See also:

gpufilter.h

#include <ctime>
#include <cstdio>
#include <cstdlib>

#include <iostream>
#include <iomanip>

#include <timer.h>
#include <dvector.h>

#include <cpuground.h>
#include <gpufilter.h>

#include <alg5.cuh>

#define REPEATS 100

// Check computation
void check_reference( const float *ref,
                      const float *res,
                      const int& ne,
                      float& me,
                      float& mre ) {
    mre = me = (float)0;
    for (int i = 0; i < ne; i++) {
        float a = (float)(res[i]) - ref[i];
        if( a < (float)0 ) a = -a;
        if( ref[i] != (float)0 ) {
            float r = (ref[i] < (float)0) ? -ref[i] : ref[i];
            float b = a / r;
            mre = b > mre ? b : mre;
        }
        me = a > me ? a : me;
    }
}

// Main
int main(int argc, char *argv[]) {

    const int in_w = 4096, in_h = 4096;

    const float b0 = 1.26795, a1 = -0.26795;

    std::cout << "[r3] Generating random input image (" << in_w << "x"
              << in_h << ") ... " << std::flush;

    float *in_cpu = new float[in_w*in_h];
    float *in_gpu = new float[in_w*in_h];

    srand(time(0));

    for (int i = 0; i < in_w*in_h; ++i)
        in_gpu[i] = in_cpu[i] = rand() / (float)RAND_MAX;

    std::cout << "done!\n[r3] Recursive filter: y_i = b0 * x_i - a1 * "
              << "y_{i-1}\n[r3] Considering forward and reverse on rows "
              << "and columns\n[r3] Feedforward and feedback coefficients "
              << "are: b0 = " << b0 << " ; a1 = " << a1 << "\n"
              << "[r3] CPU Computing first-order recursive filtering ... "
              << std::flush;

    std::cout << std::fixed << std::setprecision(2);

    {
        gpufilter::scoped_timer_stop sts( gpufilter::timers.cpu_add(
                                              "CPU", in_w*in_h, "iP") );

        gpufilter::r( in_cpu, in_w, in_h, b0, a1 );
    }

    std::cout << "done!\n[r3] Configuring the GPU to run ... " << std::flush;

    gpufilter::alg_setup algs;
    gpufilter::dvector<float> d_out;
    gpufilter::dvector<float> d_transp_pybar, d_transp_ezhat, d_ptucheck,
        d_etvtilde;
    cudaArray *a_in;

    gpufilter::prepare_alg5( algs, d_out, d_transp_pybar, d_transp_ezhat,
                             d_ptucheck, d_etvtilde, a_in, in_gpu, in_w,
                             in_h, b0, a1 );

    std::cout << "done!\n[r3] GPU Computing first-order recursive filtering "
              << "using Algorithm 5 ... " << std::flush;

    {
        gpufilter::scoped_timer_stop sts( gpufilter::timers.gpu_add(
                                              "GPU", in_w*in_h*REPEATS, "iP") );

        for (int i = 0; i < REPEATS; ++i)
            gpufilter::alg5( d_out, d_transp_pybar, d_transp_ezhat, d_ptucheck,
                             d_etvtilde, a_in, algs );
    }

    std::cout << "done!\n";

    gpufilter::timers.flush();

    std::cout << "[r3] Copying result back from the GPU ... " << std::flush;

    d_out.copy_to( in_gpu, in_w * in_h );

    cudaFreeArray( a_in );

    std::cout << "done!\n[r3] Checking GPU result against CPU reference\n";

    float me, mre;

    check_reference( in_cpu, in_gpu, in_w*in_h, me, mre );

    std::cout << std::scientific;

    std::cout << "[r2] Maximum relative error: " << mre
              << " ; Maximum error: " << me << "\n";

    delete [] in_cpu;
    delete [] in_gpu;

    return 0;

}