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https://github.com/Steffo99/unimore-hpc-assignments.git
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149 lines
3.7 KiB
C
149 lines
3.7 KiB
C
#include <stdio.h>
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#include <unistd.h>
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#include <string.h>
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#include <math.h>
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/* Include polybench common header. */
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#include <polybench.h>
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/* Include benchmark-specific header. */
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/* Default data type is double, default size is 1000. */
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#include "correlation.h"
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/* Array initialization. */
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static
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void init_array (int m,
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int n,
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DATA_TYPE *float_n,
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DATA_TYPE POLYBENCH_2D(data,M,N,m,n))
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{
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int i, j;
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*float_n = 1.2;
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for (i = 0; i < m; i++)
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for (j = 0; j < n; j++)
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data[i][j] = ((DATA_TYPE) i*j) / M;
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}
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/* DCE code. Must scan the entire live-out data.
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Can be used also to check the correctness of the output. */
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static
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void print_array(int m,
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DATA_TYPE POLYBENCH_2D(symmat,M,M,m,m))
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{
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int i, j;
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for (i = 0; i < m; i++)
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for (j = 0; j < m; j++) {
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fprintf (stderr, DATA_PRINTF_MODIFIER, symmat[i][j]);
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if ((i * m + j) % 20 == 0) fprintf (stderr, "\n");
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}
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fprintf (stderr, "\n");
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}
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/* Main computational kernel. The whole function will be timed,
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including the call and return. */
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static
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void kernel_correlation(int m, int n,
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DATA_TYPE float_n,
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DATA_TYPE POLYBENCH_2D(data,M,N,m,n),
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DATA_TYPE POLYBENCH_2D(symmat,M,M,m,m),
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DATA_TYPE POLYBENCH_1D(mean,M,m),
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DATA_TYPE POLYBENCH_1D(stddev,M,m))
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{
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int i, j, j1, j2;
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DATA_TYPE eps = 0.1f;
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#define sqrt_of_array_cell(x,j) sqrt(x[j])
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/* Determine mean of column vectors of input data matrix */
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for (j = 0; j < _PB_M; j++)
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{
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mean[j] = 0.0;
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for (i = 0; i < _PB_N; i++)
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mean[j] += data[i][j];
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mean[j] /= float_n;
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}
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/* Determine standard deviations of column vectors of data matrix. */
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for (j = 0; j < _PB_M; j++)
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{
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stddev[j] = 0.0;
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for (i = 0; i < _PB_N; i++)
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stddev[j] += (data[i][j] - mean[j]) * (data[i][j] - mean[j]);
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stddev[j] /= float_n;
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stddev[j] = sqrt_of_array_cell(stddev, j);
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/* The following in an inelegant but usual way to handle
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near-zero std. dev. values, which below would cause a zero-
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divide. */
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stddev[j] = stddev[j] <= eps ? 1.0 : stddev[j];
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}
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/* Center and reduce the column vectors. */
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for (i = 0; i < _PB_N; i++)
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for (j = 0; j < _PB_M; j++)
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{
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data[i][j] -= mean[j];
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data[i][j] /= sqrt(float_n) * stddev[j];
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}
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/* Calculate the m * m correlation matrix. */
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for (j1 = 0; j1 < _PB_M-1; j1++)
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{
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symmat[j1][j1] = 1.0;
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for (j2 = j1+1; j2 < _PB_M; j2++)
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{
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symmat[j1][j2] = 0.0;
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for (i = 0; i < _PB_N; i++)
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symmat[j1][j2] += (data[i][j1] * data[i][j2]);
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symmat[j2][j1] = symmat[j1][j2];
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}
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}
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symmat[_PB_M-1][_PB_M-1] = 1.0;
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}
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int main(int argc, char** argv)
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{
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/* Retrieve problem size. */
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int n = N;
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int m = M;
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/* Variable declaration/allocation. */
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DATA_TYPE float_n;
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POLYBENCH_2D_ARRAY_DECL(data,DATA_TYPE,M,N,m,n);
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POLYBENCH_2D_ARRAY_DECL(symmat,DATA_TYPE,M,M,m,m);
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POLYBENCH_1D_ARRAY_DECL(mean,DATA_TYPE,M,m);
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POLYBENCH_1D_ARRAY_DECL(stddev,DATA_TYPE,M,m);
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/* Initialize array(s). */
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init_array (m, n, &float_n, POLYBENCH_ARRAY(data));
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/* Start timer. */
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polybench_start_instruments;
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/* Run kernel. */
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kernel_correlation (m, n, float_n,
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POLYBENCH_ARRAY(data),
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POLYBENCH_ARRAY(symmat),
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POLYBENCH_ARRAY(mean),
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POLYBENCH_ARRAY(stddev));
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/* Stop and print timer. */
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polybench_stop_instruments;
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polybench_print_instruments;
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/* Prevent dead-code elimination. All live-out data must be printed
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by the function call in argument. */
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polybench_prevent_dce(print_array(m, POLYBENCH_ARRAY(symmat)));
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/* Be clean. */
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POLYBENCH_FREE_ARRAY(data);
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POLYBENCH_FREE_ARRAY(symmat);
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POLYBENCH_FREE_ARRAY(mean);
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POLYBENCH_FREE_ARRAY(stddev);
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return 0;
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}
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