void *
thread_function (void *arg)
{
int my_number = (long) arg;
int *myp = (int *) &counters[my_number];
static volatile int t;
/* Don't run forever. Run just short of it :) */
while (*myp > 0)
{
t = break_here_before_inc ();
int y = inc_x (my_number);
if (y >= 0)
{
(*myp) ++; usleep (1); /* Loop increment. */
if (my_number == 0)
break_here ();
}
else
{
usleep (1);
}
}
pthread_exit (NULL);
}
void le_step_x(le_task *t)
{
assert(t->stype == ST_AOS); // this is array of structere
/*
* Due to our system of pde is linear, we can use some simple way to solve it.
* du/dt + A * du/dx = 0.
* Vector u = {vx, vy, sxx, sxy, syy}.
* Matrix A could be represent in form OmegaL * Lambda * OmegaR,
* where Lambda - diagonal matrix of eigen values of matrix A.
* In our case Lambda = diag{c1, -c1, c2, -c2, 0}.
* OmegaR and OmegaL - metrices from eigen vectors of matrix A,
* OmegaR * OmegaL = E, where E = diag{1, 1, 1, 1, 1}.
*
* We can rewrite out system in form:
* du/dt + OmegaL * Lambda * OmegaR du/dx = 0, multiply on matrix OmegaR:
*
* OmegaR * du/dt + OmegaR * OmegaL * Lambda * OmegaR du/dx = 0.
*
* Introduce new variables (http://en.wikipedia.org/wiki/Riemann_invariant):
* w = {w1, w2, w3, w4, w5},
* w = OmegaR * u, then we got:
*
* dw/dt + Lambda * dw/dx = 0.
* And we get system of independent advection equations, that we can solve separatly.
*
* So we get next algorithm:
* 1. Introduce new variables w = OmegaR * u;
* 2. Solve 5 equations of linear advection (in real we solve only 4, because of in fifth equation speed is 0);
* 3. Make inverse transformation u = OmegaL * w.
*/
int i, j;
// Courant number (http://en.wikipedia.org/wiki/Courant%E2%80%93Friedrichs%E2%80%93Lewy_condition).
const real k1 = t->dt * t->mat.c1 / t->h.x;
const real k2 = t->dt * t->mat.c2 / t->h.x;
int num_of_threads = omp_get_num_threads();
#pragma omp parallel for shared(t) private(i,j)
for (j = 0; j < t->n.y; j++) {
/*
* Riemann invariants for 5-point sctencil difference scheme.
*/
le_w w_2, w_1, w, w1, w2;
omega_x(&t->mat, &gind(0, j), &w);
omega_x(&t->mat, &gind(1, j), &w1);
omega_x(&t->mat, &gind(2, j), &w2);
w_2 = w_1 = w; // In linear case we can do this.
for (i = 0; i < t->n.x; i++) {
le_w d;
reconstruct(w_2, w_1, w, w1, w2, k1, k2, &d);
inc_x(&t->mat, &gind(i, j), &d);
w_2 = w_1;
w_1 = w;
w = w1;
w1 = w2;
if (i < t->n.x - 3) omega_x(&t->mat, &gind(i + 3, j), &w2);
}
}
}
