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Commit ea5457ca authored by Daniel Maier's avatar Daniel Maier
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adi application

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/**
* This version is stamped on May 10, 2016
*
* Contact:
* Louis-Noel Pouchet <pouchet.ohio-state.edu>
* Tomofumi Yuki <tomofumi.yuki.fr>
*
* Web address: http://polybench.sourceforge.net
*/
/* adi.c: this file is part of PolyBench/C */
#include <stdio.h>
#include <unistd.h>
#include <string.h>
#include <math.h>
/* Include polybench common header. */
#include <polybench.h>
/* Include benchmark-specific header. */
#include "adi.h"
/* Array initialization. */
static
void init_array (int n,
DATA_TYPE POLYBENCH_2D(u,N,N,n,n))
{
int i, j;
for (i = 0; i < n; i++)
for (j = 0; j < n; j++)
{
u[i][j] = (DATA_TYPE)(i + n-j) / n;
}
}
/* DCE code. Must scan the entire live-out data.
Can be used also to check the correctness of the output. */
static
void print_array(int n,
DATA_TYPE POLYBENCH_2D(u,N,N,n,n))
{
int i, j;
POLYBENCH_DUMP_START;
POLYBENCH_DUMP_BEGIN("u");
for (i = 0; i < n; i++)
for (j = 0; j < n; j++) {
if ((i * n + j) % 20 == 0) fprintf(POLYBENCH_DUMP_TARGET, "\n");
fprintf (POLYBENCH_DUMP_TARGET, DATA_PRINTF_MODIFIER, u[i][j]);
}
POLYBENCH_DUMP_END("u");
POLYBENCH_DUMP_FINISH;
}
/* Main computational kernel. The whole function will be timed,
including the call and return. */
/* Based on a Fortran code fragment from Figure 5 of
* "Automatic Data and Computation Decomposition on Distributed Memory Parallel Computers"
* by Peizong Lee and Zvi Meir Kedem, TOPLAS, 2002
*/
static
void kernel_adi(int tsteps, int n,
DATA_TYPE POLYBENCH_2D(u,N,N,n,n),
DATA_TYPE POLYBENCH_2D(v,N,N,n,n),
DATA_TYPE POLYBENCH_2D(p,N,N,n,n),
DATA_TYPE POLYBENCH_2D(q,N,N,n,n))
{
int t, i, j;
DATA_TYPE DX, DY, DT;
DATA_TYPE B1, B2;
DATA_TYPE mul1, mul2;
DATA_TYPE a, b, c, d, e, f;
#pragma scop
DX = SCALAR_VAL(1.0)/(DATA_TYPE)_PB_N;
DY = SCALAR_VAL(1.0)/(DATA_TYPE)_PB_N;
DT = SCALAR_VAL(1.0)/(DATA_TYPE)_PB_TSTEPS;
B1 = SCALAR_VAL(2.0);
B2 = SCALAR_VAL(1.0);
mul1 = B1 * DT / (DX * DX);
mul2 = B2 * DT / (DY * DY);
a = -mul1 / SCALAR_VAL(2.0);
b = SCALAR_VAL(1.0)+mul1;
c = a;
d = -mul2 / SCALAR_VAL(2.0);
e = SCALAR_VAL(1.0)+mul2;
f = d;
for (t=1; t<=_PB_TSTEPS; t++) {
//Column Sweep
for (i=1; i<_PB_N-1; i++) {
v[0][i] = SCALAR_VAL(1.0);
p[i][0] = SCALAR_VAL(0.0);
q[i][0] = v[0][i];
for (j=1; j<_PB_N-1; j++) {
p[i][j] = -c / (a*p[i][j-1]+b);
q[i][j] = (-d*u[j][i-1]+(SCALAR_VAL(1.0)+SCALAR_VAL(2.0)*d)*u[j][i] - f*u[j][i+1]-a*q[i][j-1])/(a*p[i][j-1]+b);
}
v[_PB_N-1][i] = SCALAR_VAL(1.0);
for (j=_PB_N-2; j>=1; j--) {
v[j][i] = p[i][j] * v[j+1][i] + q[i][j];
}
}
//Row Sweep
for (i=1; i<_PB_N-1; i++) {
u[i][0] = SCALAR_VAL(1.0);
p[i][0] = SCALAR_VAL(0.0);
q[i][0] = u[i][0];
for (j=1; j<_PB_N-1; j++) {
p[i][j] = -f / (d*p[i][j-1]+e);
q[i][j] = (-a*v[i-1][j]+(SCALAR_VAL(1.0)+SCALAR_VAL(2.0)*a)*v[i][j] - c*v[i+1][j]-d*q[i][j-1])/(d*p[i][j-1]+e);
}
u[i][_PB_N-1] = SCALAR_VAL(1.0);
for (j=_PB_N-2; j>=1; j--) {
u[i][j] = p[i][j] * u[i][j+1] + q[i][j];
}
}
}
#pragma endscop
}
int main(int argc, char** argv)
{
/* Retrieve problem size. */
int n = N;
int tsteps = TSTEPS;
/* Variable declaration/allocation. */
POLYBENCH_2D_ARRAY_DECL(u, DATA_TYPE, N, N, n, n);
POLYBENCH_2D_ARRAY_DECL(v, DATA_TYPE, N, N, n, n);
POLYBENCH_2D_ARRAY_DECL(p, DATA_TYPE, N, N, n, n);
POLYBENCH_2D_ARRAY_DECL(q, DATA_TYPE, N, N, n, n);
/* Initialize array(s). */
init_array (n, POLYBENCH_ARRAY(u));
/* Start timer. */
polybench_start_instruments;
/* Run kernel. */
kernel_adi (tsteps, n, POLYBENCH_ARRAY(u), POLYBENCH_ARRAY(v), POLYBENCH_ARRAY(p), POLYBENCH_ARRAY(q));
/* Stop and print timer. */
polybench_stop_instruments;
polybench_print_instruments;
/* Prevent dead-code elimination. All live-out data must be printed
by the function call in argument. */
polybench_prevent_dce(print_array(n, POLYBENCH_ARRAY(u)));
/* Be clean. */
POLYBENCH_FREE_ARRAY(u);
POLYBENCH_FREE_ARRAY(v);
POLYBENCH_FREE_ARRAY(p);
POLYBENCH_FREE_ARRAY(q);
return 0;
}
/**
* This version is stamped on May 10, 2016
*
* Contact:
* Louis-Noel Pouchet <pouchet.ohio-state.edu>
* Tomofumi Yuki <tomofumi.yuki.fr>
*
* Web address: http://polybench.sourceforge.net
*/
#ifndef _ADI_H
# define _ADI_H
/* Default to LARGE_DATASET. */
# if !defined(MINI_DATASET) && !defined(SMALL_DATASET) && !defined(MEDIUM_DATASET) && !defined(LARGE_DATASET) && !defined(EXTRALARGE_DATASET)
# define LARGE_DATASET
# endif
# if !defined(TSTEPS) && !defined(N)
/* Define sample dataset sizes. */
# ifdef MINI_DATASET
# define TSTEPS 20
# define N 20
# endif
# ifdef SMALL_DATASET
# define TSTEPS 40
# define N 60
# endif
# ifdef MEDIUM_DATASET
# define TSTEPS 100
# define N 200
# endif
# ifdef LARGE_DATASET
# define TSTEPS 500
# define N 1000
# endif
# ifdef EXTRALARGE_DATASET
# define TSTEPS 1000
# define N 2000
# endif
#endif /* !(TSTEPS N) */
# define _PB_TSTEPS POLYBENCH_LOOP_BOUND(TSTEPS,tsteps)
# define _PB_N POLYBENCH_LOOP_BOUND(N,n)
/* Default data type */
# if !defined(DATA_TYPE_IS_INT) && !defined(DATA_TYPE_IS_FLOAT) && !defined(DATA_TYPE_IS_DOUBLE)
# define DATA_TYPE_IS_DOUBLE
# endif
#ifdef DATA_TYPE_IS_INT
# define DATA_TYPE int
# define DATA_PRINTF_MODIFIER "%d "
#endif
#ifdef DATA_TYPE_IS_FLOAT
# define DATA_TYPE float
# define DATA_PRINTF_MODIFIER "%0.2f "
# define SCALAR_VAL(x) x##f
# define SQRT_FUN(x) sqrtf(x)
# define EXP_FUN(x) expf(x)
# define POW_FUN(x,y) powf(x,y)
# endif
#ifdef DATA_TYPE_IS_DOUBLE
# define DATA_TYPE double
# define DATA_PRINTF_MODIFIER "%0.2lf "
# define SCALAR_VAL(x) x
# define SQRT_FUN(x) sqrt(x)
# define EXP_FUN(x) exp(x)
# define POW_FUN(x,y) pow(x,y)
# endif
#endif /* !_ADI_H */
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