filename stringlengths 19 182 | omp_pragma_line stringlengths 24 416 | context_chars int64 100 100 | text stringlengths 152 177k |
|---|---|---|---|
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) HYPRE_SMP_SCHEDULE | 100 | k - 1) * size + i];
}
}
else if (restk == 5)
{
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
y_data[i] += + alpha[k - 5] * x_data[(k - 5) * size + i] + alpha[k - 4] * x_data[(k - 4) * size + i]
+ alpha[k - 3] * x_data[(k - 3) * size + ... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) HYPRE_SMP_SCHEDULE | 100 | }
else if (restk == 6)
{
jstart = (k - 6) * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
y_data[i] += alpha[k - 6] * x_data[jstart + i] + alpha[k - 5] * x_data[jstart + i + size]
+ alpha[k - 4] * x_data[(k - 4) * size + i] + alpha[... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) HYPRE_SMP_SCHEDULE | 100 | }
else if (restk == 7)
{
jstart = (k - 7) * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
y_data[i] += alpha[k - 7] * x_data[jstart + i] + alpha[k - 6] * x_data[jstart + i + size]
+ alpha[k - 5] * x_data[(k - 5) * size + i] + alpha[... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) HYPRE_SMP_SCHEDULE | 100 | 0; j < k - 3; j += 4)
{
jstart = j * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
y_data[i] += alpha[j] * x_data[jstart + i] + alpha[j + 1] * x_data[jstart + i + size]
+ alpha[j + 2] * x_data[(j + 2) * size + i] + a... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) HYPRE_SMP_SCHEDULE | 100 | }
}
if (restk == 1)
{
jstart = (k - 1) * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
y_data[i] += alpha[k - 1] * x_data[jstart + i];
}<LOOP-END> <OMP-START>#pragma omp parallel for private(i) HYPRE_SMP_SCHEDULE<OMP-END> |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) HYPRE_SMP_SCHEDULE | 100 | }
else if (restk == 2)
{
jstart = (k - 2) * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
y_data[i] += alpha[k - 2] * x_data[jstart + i] + alpha[k - 1] * x_data[jstart + size + i];
}<LOOP-END> <OMP-START>#pragma omp parallel for private(i) HYPRE_SM... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) HYPRE_SMP_SCHEDULE | 100 | }
else if (restk == 3)
{
jstart = (k - 3) * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
y_data[i] += alpha[k - 3] * x_data[jstart + i] + alpha[k - 2] * x_data[jstart + size + i] + alpha[k
... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) HYPRE_SMP_SCHEDULE | 100 | or (j = 0; j < k; j++)
{
jstart = j * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
y_data[i] += alpha[j] * x_data[jstart + i];
}<LOOP-END> <OMP-START>#pragma omp parallel for private(i) HYPRE_SMP_SCHEDULE<OMP-END> |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1,res2,res3,res4,res5,res6,res7,res8) HYPRE_SMP_SCHEDULE | 100 | tart6 = jstart5 + size;
jstart7 = jstart6 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
res2 += hypre_conj(y_data[jstart1 + i]) * x_data[i];
res3 += hypre_conj(y_data[jst... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1) HYPRE_SMP_SCHEDULE | 100 | stk == 1)
{
res1 = 0;
jstart = (k - 1) * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
}<LOOP-END> <OMP-START>#pragma omp parallel for private(i) reduction(+:res1) HYPRE_SMP_SCHEDULE<OMP-END> |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1,res2) HYPRE_SMP_SCHEDULE | 100 | jstart = (k - 2) * size;
jstart1 = jstart + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
res2 += hypre_conj(y_data[jstart1 + i]) * x_data[i];
}<LOOP-END> <OMP-START>#pragma omp parallel ... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1,res2,res3) HYPRE_SMP_SCHEDULE | 100 | jstart1 = jstart + size;
jstart2 = jstart1 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
res2 += hypre_conj(y_data[jstart1 + i]) * x_data[i];
res3 += hypre_conj(y_data[jstart2 + i]) *... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1,res2,res3,res4) HYPRE_SMP_SCHEDULE | 100 | jstart2 = jstart1 + size;
jstart3 = jstart2 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
res2 += hypre_conj(y_data[jstart1 + i]) * x_data[i];
res3 += hypre_conj(y_data[jstart2 + i]) *... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1,res2,res3,res4,res5) HYPRE_SMP_SCHEDULE | 100 | jstart3 = jstart2 + size;
jstart4 = jstart3 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
res2 += hypre_conj(y_data[jstart1 + i]) * x_data[i];
res3 += hypre_conj(y_data[jstart2 + i]) *... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1,res2,res3,res4,res5,res6) HYPRE_SMP_SCHEDULE | 100 | jstart4 = jstart3 + size;
jstart5 = jstart4 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
res2 += hypre_conj(y_data[jstart1 + i]) * x_data[i];
res3 += hypre_conj(y_data[jstart2 + i]) *... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1,res2,res3,res4,res5,res6,res7) HYPRE_SMP_SCHEDULE | 100 | jstart5 = jstart4 + size;
jstart6 = jstart5 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
res2 += hypre_conj(y_data[jstart1 + i]) * x_data[i];
res3 += hypre_conj(y_data[jstart2 + i]) *... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1,res2,res3,res4) HYPRE_SMP_SCHEDULE | 100 | tart2 = jstart1 + size;
jstart3 = jstart2 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
res2 += hypre_conj(y_data[jstart1 + i]) * x_data[i];
res3 += hypre_conj(y_data[jst... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1) HYPRE_SMP_SCHEDULE | 100 | stk == 1)
{
res1 = 0;
jstart = (k - 1) * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
}<LOOP-END> <OMP-START>#pragma omp parallel for private(i) reduction(+:res1) HYPRE_SMP_SCHEDULE<OMP-END> |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1,res2) HYPRE_SMP_SCHEDULE | 100 | jstart = (k - 2) * size;
jstart1 = jstart + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
res2 += hypre_conj(y_data[jstart1 + i]) * x_data[i];
}<LOOP-END> <OMP-START>#pragma omp parallel ... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res1,res2,res3) HYPRE_SMP_SCHEDULE | 100 | jstart1 = jstart + size;
jstart2 = jstart1 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res1 += hypre_conj(y_data[jstart + i]) * x_data[i];
res2 += hypre_conj(y_data[jstart1 + i]) * x_data[i];
res3 += hypre_conj(y_data[jstart2 + i]) *... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_x2,res_x3,res_x4,res_x5,res_x6,res_x7,res_x8,res_y1,res_y2,res_y3,res_y4,res_y5,res_y6,res_y7,res_y8) HYPRE_SMP_SCHEDULE | 100 | tart6 = jstart5 + size;
jstart7 = jstart6 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
res_x2 += hypre_conj(z_dat... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_y1) HYPRE_SMP_SCHEDULE | 100 | res_x1 = 0;
res_y1 = 0;
jstart = (k - 1) * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
}<LOOP-END> <OMP-START>#pragma omp parall... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_x2,res_y1,res_y2) HYPRE_SMP_SCHEDULE | 100 | jstart = (k - 2) * size;
jstart1 = jstart + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
res_x2 += hypre_conj(z_data[jstart1 + ... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_x2,res_x3,res_y1,res_y2,res_y3) HYPRE_SMP_SCHEDULE | 100 | jstart1 = jstart + size;
jstart2 = jstart1 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
res_x2 += hypre_conj(z_data[jstart1 + ... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_x2,res_x3,res_x4,res_y1,res_y2,res_y3,res_y4) HYPRE_SMP_SCHEDULE | 100 | jstart2 = jstart1 + size;
jstart3 = jstart2 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
res_x2 += hypre_conj(z_data[jstart1 + ... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_x2,res_x3,res_x4,res_x5,res_y1,res_y2,res_y3,res_y4,res_y5) HYPRE_SMP_SCHEDULE | 100 | jstart3 = jstart2 + size;
jstart4 = jstart3 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
res_x2 += hypre_conj(z_data[jstart1 + ... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_x2,res_x3,res_x4,res_x5,res_x6,res_y1,res_y2,res_y3,res_y4,res_y5,res_y6) HYPRE_SMP_SCHEDULE | 100 | jstart4 = jstart3 + size;
jstart5 = jstart4 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
res_x2 += hypre_conj(z_data[jstart1 + ... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_x2,res_x3,res_x4,res_x5,res_x6,res_x7,res_y1,res_y2,res_y3,res_y4,res_y5,res_y6,res_y7) HYPRE_SMP_SCHEDULE | 100 | jstart5 = jstart4 + size;
jstart6 = jstart5 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
res_x2 += hypre_conj(z_data[jstart1 + ... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_x2,res_x3,res_x4,res_y1,res_y2,res_y3,res_y4) HYPRE_SMP_SCHEDULE | 100 | tart2 = jstart1 + size;
jstart3 = jstart2 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
res_x2 += hypre_conj(z_dat... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_y1) HYPRE_SMP_SCHEDULE | 100 | res_x1 = 0;
res_y1 = 0;
jstart = (k - 1) * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
}<LOOP-END> <OMP-START>#pragma omp parall... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_x2,res_y1,res_y2) HYPRE_SMP_SCHEDULE | 100 | jstart = (k - 2) * size;
jstart1 = jstart + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
res_x2 += hypre_conj(z_data[jstart1 + ... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x1,res_x2,res_x3,res_y1,res_y2,res_y3) HYPRE_SMP_SCHEDULE | 100 | jstart1 = jstart + size;
jstart2 = jstart1 + size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x1 += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y1 += hypre_conj(z_data[jstart + i]) * y_data[i];
res_x2 += hypre_conj(z_data[jstart1 + ... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res) HYPRE_SMP_SCHEDULE | 100 | j++)
{
res = 0;
jstart = j * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res += hypre_conj(y_data[jstart + i]) * x_data[i];
}<LOOP-END> <OMP-START>#pragma omp parallel for private(i) reduction(+:res) HYPRE_SMP_SCHEDULE<OMP... |
hypre-space/hypre/src/seq_mv/vector_batched.c | #pragma omp parallel for private(i) reduction(+:res_x,res_y) HYPRE_SMP_SCHEDULE | 100 | res_y = 0; //result_y[j];
jstart = j * size;
#if defined(HYPRE_USING_OPENMP)
<LOOP-START>for (i = 0; i < size; i++)
{
res_x += hypre_conj(z_data[jstart + i]) * x_data[i];
res_y += hypre_conj(z_data[jstart + i]) * y_data[i];
}<LOOP-END> <OMP-START>#pragma ... |
chiao45/mgmetis/mgmetis/src/metis/GKlib/csr.c | #pragma omp parallel for if (ptr[n] > OMPMINOPS) schedule(static) | 100 | eak;
default:
gk_errexit(SIGERR, "Invalid sum type of %d.\n", what);
return;
}
<LOOP-START>for (i=0; i<n; i++)
sums[i] = gk_fsum(ptr[i+1]-ptr[i], val+ptr[i], 1);
}
/*************************************************************************/
/*! Computes the squared of the norms of the rows/co... |
chiao45/mgmetis/mgmetis/src/metis/GKlib/csr.c | #pragma omp parallel for if (ptr[n] > OMPMINOPS) schedule(static) | 100 | ak;
default:
gk_errexit(SIGERR, "Invalid norm type of %d.\n", what);
return;
}
<LOOP-START>for (i=0; i<n; i++)
norms[i] = gk_fdot(ptr[i+1]-ptr[i], val+ptr[i], 1, val+ptr[i], 1);
}
/*************************************************************************/
/*! Computes the similarity between ... |
stefanomoriconi/libmpMuelMat/C-libs/mp_comp_MM_polarim_Params.c | #pragma omp parallel for | 100 | double *Mdelta_in,
int *idx_in, int *numel_in )
{
int m = 16;
<LOOP-START>for (int i=0; i<numel_in[0]; ++i) // for each pixel
{
compute_Diatt_Params( MD_in[idx_in[i]*m+4], MD_in[idx_in[i]*m+8], MD_in[idx_in[i]*m+12],
&totD_out[idx_in[i]],... |
stefanomoriconi/libmpMuelMat/C-libs/mp_comp_MM_AIW.c | #pragma omp parallel for | 100 | , double *I_in , double *W_in ,
int *idx_in, int *numel_in )
{
int m = 16;
<LOOP-START>for (int i=0; i<numel_in[0]; ++i) // for each pixel
{
// Components MUST be Transposed!
compute_M_AIW( A_in[idx_in[i]*m+0] , A_in[idx_in[i]*m+4] , A_in[idx_in[i]*m+8] , A_in[idx_i... |
stefanomoriconi/libmpMuelMat/C-libs/mp_comp_MM_AIW.c | #pragma omp parallel for (parallel) | 100 | +3] , &M_out[idx_in[i]*m+7] , &M_out[idx_in[i]*m+11] , &M_out[idx_in[i]*m+15] );
} // End of <LOOP-START>#pragma omp parallel for
for (int i=0; i<numel_in[0]; ++i) // for each pixel
{
nM_out[idx_in[i]*m+0] = 1.0;
nM_out[idx_in[i]*m+1] = M_out[idx_in[i]*m+1] / M_out[idx_in[i]*m+0];
... |
stefanomoriconi/libmpMuelMat/C-libs/mp_comp_MM_AIW.c | #pragma omp parallel for | 100 | dx_in[i]*m+11] , &M_out[idx_in[i]*m+15] );
} // End of #pragma omp parallel for (parallel)
<LOOP-START>for (int i=0; i<numel_in[0]; ++i) // for each pixel
{
nM_out[idx_in[i]*m+0] = 1.0;
nM_out[idx_in[i]*m+1] = M_out[idx_in[i]*m+1] / M_out[idx_in[i]*m+0];
nM_out[idx_in[i]*m+2] = M_... |
stefanomoriconi/libmpMuelMat/C-libs/mp_comp_MM_eig_REls.c | #pragma omp parallel for | 100 | ueller Matrix coefficients as input! i.e. m11 equal to 1.0 everywhere
int l = 4;
int m = 16;
<LOOP-START>for (int i=0; i<numel_in[0]; ++i) // for each pixel
{
comp_MM_eig_REls( &elsR_out[idx_in[i]*l+0] , &elsR_out[idx_in[i]*l+1] , &elsR_out[idx_in[i]*l+2] , &elsR_out[idx_in[i]*l+3],
... |
stefanomoriconi/libmpMuelMat/C-libs/test_openMP.c | #pragma omp parallel for | 100 | openMP()
{
printf(" Testing parallel-computing (openMP) libraries:... \n\n");
printf(" >> ");
<LOOP-START>for (int i=0; i<10; ++i)
{
printf("%d ",i);
}<LOOP-END> <OMP-START>#pragma omp parallel for <OMP-END> |
stefanomoriconi/libmpMuelMat/C-libs/mp_comp_MM_det.c | #pragma omp parallel for | 100 | *NORMALISED* Mueller Matrix coefficients as input! i.e. m11 equal to 1.0 everywhere
int m = 16;
<LOOP-START>for (int i=0; i<numel_in[0]; ++i) // for each pixel
{
compute_det4x4real( &Mdet_out[idx_in[i]],
&M_in[idx_in[i]*m+0] , &M_in[idx_in[i]*m+1] , &M_in[idx_in[i]*m+2] , &... |
stefanomoriconi/libmpMuelMat/C-libs/mp_comp_MM_pol_LuChipman.c | #pragma omp parallel for | 100 | *NORMALISED* Mueller Matrix coefficients as input! i.e. m11 equal to 1.0 everywhere
int m = 16;
<LOOP-START>for (int i=0; i<numel_in[0]; ++i) // for each pixel
{
// NB: Transposed Components! (MD is symmetric?)
compute_MM_polarLuChipman( M_in[idx_in[i]*m+0], M_in[idx_in[i]*m+4], M_in[idx_in[i]*... |
NJU-TJL/OpenMP-MPI_Labs/Lab02/OpenMP/LU_OpenMP.c | #pragma omp parallel for | 100 | /计算L、U矩阵
for (int i = 0; i < N; i++) {
U[i][i] = A[i][i] - sum_i_j_K(i, i, i);
L[i][i] = 1;
<LOOP-START>for (int j = i+1; j < N; j++) {
//按照递推公式进行计算
U[i][j] = A[i][j] - sum_i_j_K(i, j, i);
L[j][i] = (A[j][i] - sum_i_j_K(j, i, i)) / U[i][i];
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
NJU-TJL/OpenMP-MPI_Labs/Lab01/OpenMP/MatrixMtp_OpenMP.c | #pragma omp parallel for | 100 | 线程数
omp_set_num_threads(n_threads);
//计时开始
double ts = omp_get_wtime();
//计算C
<LOOP-START>for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
for (int k = 0; k < n; k++) {
C[i][j] += A[i][k] * B[k][j];
}
}
}<LOOP-END> <OMP-START>#... |
NJU-TJL/OpenMP-MPI_Labs/Lab03/OpenMP/main.c | #pragma omp parallel for | 100 | ARG], &filenames);
// 分配存放所有文件的文档向量的空间
vectors = (int **)calloc(file_count, sizeof(int *));
<LOOP-START>for (int i = 0; i < file_count; ++i) {
vectors[i] = (int *)calloc(dict_size, sizeof(int));
// 读取文件并生成文档向量
make_profile(filenames[i], dict_size, vectors[i]);
}<LOOP-END> <OMP-START>... |
5uso/HiPGMC/src/gmc_funs.c | #pragma omp parallel for | 100 | olumns vector
double * ssc;
if(!rank) {
ssc = malloc(m.w * sizeof(double));
<LOOP-START>for(int i = 0; i < m.w; i++)
ssc[i] = block_sum_col_sqr(m.data + i, m.h, m.w);
}
// Sequential section, faster on some setups
#ifdef SEQ_SQR
if(rank) return m;
matrix ... |
5uso/HiPGMC/src/gmc_funs.c | #pragma omp parallel for | 100 | // Workers can return here
if(rank) return mt;
#endif
// Compute final matrix
<LOOP-START>for(long long i = 0; i < m.w; i++) {
mt.data[i * m.w + i] = 0.0;
for(long long j = i + 1; j < m.w; j++) {
double mul = mt.data[i * m.w + j];
mt.data[j * m.w + i] = mt.d... |
5uso/HiPGMC/src/gmc_scale.c | #pragma omp parallel for | 100 | PI_Bcast(&w, 1, MPI_INT, 0, comm);
MPI_Bcast(&h, 1, MPI_INT, 0, comm);
if(!rank) {
<LOOP-START>for(int r = 0; r < numprocs; r++) {
// Dimensions of r's local matrix
int blacs_col = r / blacs_height;
int blacs_row = r % blacs_height;
long long mp = numroc_... |
5uso/HiPGMC/src/gmc_scale.c | #pragma omp parallel for | 100 | PI_Bcast(&w, 1, MPI_INT, 0, comm);
MPI_Bcast(&h, 1, MPI_INT, 0, comm);
if(!rank) {
<LOOP-START>for(int r = 0; r < numprocs; r++) {
// Dimensions of r's local matrix
int blacs_col = r / blacs_height;
int blacs_row = r % blacs_height;
long long mp = numroc_... |
5uso/HiPGMC/src/gmc_scale.c | #pragma omp parallel for | 100 | I_Bcast(&w, 1, MPI_LONG, 0, comm);
MPI_Bcast(&h, 1, MPI_INT, 0, comm);
if(!rank) {
<LOOP-START>for(int r = 0; r < numprocs; r++) {
// Rows assigned to process
long long numrows = h / numprocs + (r < h % numprocs);
long long numbytes = numrows * w;
if(!r) ... |
5uso/HiPGMC/src/gmc_scale.c | #pragma omp parallel for | 100 | I_Bcast(&w, 1, MPI_LONG, 0, comm);
MPI_Bcast(&h, 1, MPI_INT, 0, comm);
if(!rank) {
<LOOP-START>for(int r = 0; r < numprocs; r++) {
// Rows assigned to process
long long numrows = h / numprocs + (r < h % numprocs);
long long numbytes = numrows * w;
if(!r) ... |
5uso/HiPGMC/src/gmc.c | #pragma omp parallel for | 100 | nt m, int num) {
for(int v = 0; v < m; v++) {
long long h = X[v].h, w = X[v].w;
<LOOP-START>for(long long x = 0; x < w; x++) {
double mean = 0.0;
for(long long y = 0; y < h; y++) mean += X[v].data[y * w + x];
mean /= h;
double std = 0.0;
f... |
5uso/HiPGMC/src/gmc.c | #pragma omp parallel for | 100 | trix(PN + 1, local_ted.h);
int s = displs[rank]; // Start pattern for this process
<LOOP-START>for(long long y = 0; y < pattern_cnts[rank]; y++) {
local_ted.data[y * num + s + y] = INFINITY;
heap h = new_heap(local_ted.data + y * num, PN + 1);
for(long l... |
5uso/HiPGMC/src/gmc.c | #pragma omp parallel for | 100 | s
memset(U.data, 0x00, (long long) num * (long long) pattern_cnts[rank] * sizeof(double));
<LOOP-START>for(long long y = 0; y < pattern_cnts[rank]; y++) {
double sum = 0.0;
for(long long i = 0; i < PN + 1; i++)
for(int v = 0; v < m; v++) {
sprs_val val = S0[v].data[y... |
5uso/HiPGMC/src/gmc.c | #pragma omp parallel for | 100 | * sums) {
for(long long v = 0; v < m; v++) {
double weight = w.data[v] * 2.0;
<LOOP-START>for(long long y = 0; y < pattern_cnts[rank]; y++) {
double max = ed[v].data[(PN + 1) * y];
double maxU = U.data[y * num + S0[v].data[(PN + 1) * y].i];
double sumU = 0.0;
... |
5uso/HiPGMC/src/gmc.c | #pragma omp parallel for | 100 | .data, U.data, (long long) num * (long long) pattern_cnts[rank] * sizeof(double));
<LOOP-START>for(long long y = 0; y < pattern_cnts[rank]; y++)
for(long long i = 0; i < PN + 1; i++) {
sprs_val val = S0[v].data[y * (PN + 1) + i];
long long x = val.i;
... |
5uso/HiPGMC/src/gmc.c | #pragma omp parallel for | 100 | ), dist.h, dist.data, local_dist.data, rank, numprocs, comm);
if(!rank) free_matrix(dist);
<LOOP-START>for(long long y = 0; y < pattern_cnts[rank]; y++) {
int qw = 0;
int * idx = malloc((long long) num * sizeof(int));
#ifdef IS_LOCAL
memset(idx, 0x00, (long long) num * size... |
5uso/HiPGMC/src/gmc.c | #pragma omp parallel for | 100 | C_STEP("End: symU");
bool * adj = malloc((long long) num * (long long) num * sizeof(bool));
<LOOP-START>for(long long j = 0; j < num; j++)
for(long long x = 0; x < j; x++)
adj[j * num + x] = (U.data[j * num + x] != 0.0) || (U.data[x * num + j] != 0.0);
// Final clustering. Find connecte... |
UoB-HPC/miniBUDE/openmp/bude.c | #pragma omp parallel for | 100 | arams.nposes);
for(int p = 0; p < 6; p++){
poses[p] = malloc(sizeof(float) * params.nposes);
<LOOP-START>for(int i = 0; i < params.nposes; i++){
poses[p][i] = params.poses[p][i];
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
UoB-HPC/miniBUDE/openmp/bude.c | #pragma omp parallel for | 100 | for
for(int i = 0; i < params.nposes; i++){
poses[p][i] = params.poses[p][i];
}
}
<LOOP-START>for(int i = 0; i < params.nposes; i++){
buffer[i] = 0.f;
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
UoB-HPC/miniBUDE/openmp/bude.c | #pragma omp parallel for | 100 | }
}
#pragma omp parallel for
for(int i = 0; i < params.nposes; i++){
buffer[i] = 0.f;
}
<LOOP-START>for(int i = 0; i < params.natpro; i++){
protein[i] = params.protein[i];
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
UoB-HPC/miniBUDE/openmp/bude.c | #pragma omp parallel for | 100 | omp parallel for
for(int i = 0; i < params.natpro; i++){
protein[i] = params.protein[i];
}
<LOOP-START>for(int i = 0; i < params.natlig; i++){
ligand[i] = params.ligand[i];
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
UoB-HPC/miniBUDE/openmp/bude.c | #pragma omp parallel for | 100 | a omp parallel for
for(int i = 0; i < params.natlig; i++){
ligand[i] = params.ligand[i];
}
<LOOP-START>for(int i = 0; i < params.ntypes; i++){
forcefield[i] = params.forcefield[i];
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
UoB-HPC/miniBUDE/openmp/bude.c | #pragma omp parallel for | 100 | i = 0; i < params.ntypes; i++){
forcefield[i] = params.forcefield[i];
}
// warm up 1 iter
<LOOP-START>for (unsigned group = 0; group < (params.nposes/WGSIZE); group++)
{
fasten_main(params.natlig, params.natpro, protein, ligand,
poses[0], poses[1], poses[2],
poses... |
UoB-HPC/miniBUDE/makedeck/main.cpp | #pragma omp parallel for default(none) shared(ligand, protein, ffParams, poses, energies, totalPoses, completed, std::cout) | 100 | chrono::high_resolution_clock::now();
size_t completed = 0;
size_t totalPoses = config.poseSize;
<LOOP-START>for (size_t pose = 0; pose < totalPoses; pose++) {
bude::kernel::fasten_main(
ligand.first.size(), protein.first.size(),
protein.first, ligand.first,
poses.tilt, poses.roll, poses.pan,
poses... |
ShadenSmith/splatt/src/mttkrp.c | #pragma omp parallel for | 100 | _t const * const restrict bv = B->vals + (r * B->I);
/* stretch out columns of A and B */
<LOOP-START>for(idx_t x=0; x < nnz; ++x) {
scratch[x] = vals[x] * av[indA[x]] * bv[indB[x]];
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
ShadenSmith/splatt/src/sort.c | #pragma omp parallel for schedule(dynamic) | 100 | nnz;
/* for 3/4D, we can use quicksort on only the leftover modes */
if(tt->nmodes == 3) {
<LOOP-START>for(idx_t i = 0; i < nslices; ++i) {
p_tt_quicksort2(tt, cmplt+1, histogram_array[i], histogram_array[i + 1]);
for(idx_t j = histogram_array[i]; j < histogram_array[i + 1]; ++j) {
tt->ind[... |
ShadenSmith/splatt/src/sort.c | #pragma omp parallel for schedule(dynamic) | 100 | m_array[i + 1]; ++j) {
tt->ind[m][j] = i;
}
}
} else if(tt->nmodes == 4) {
<LOOP-START>for(idx_t i = 0; i < nslices; ++i) {
p_tt_quicksort3(tt, cmplt+1, histogram_array[i], histogram_array[i + 1]);
for(idx_t j = histogram_array[i]; j < histogram_array[i + 1]; ++j) {
tt->ind[... |
ShadenSmith/splatt/src/sort.c | #pragma omp parallel for schedule(dynamic) | 100 | memmove(cmplt, cmplt+1, (tt->nmodes - 1) * sizeof(*cmplt));
cmplt[tt->nmodes-1] = saved;
<LOOP-START>for(idx_t i = 0; i < nslices; ++i) {
p_tt_quicksort(tt, cmplt, histogram_array[i], histogram_array[i + 1]);
for(idx_t j = histogram_array[i]; j < histogram_array[i + 1]; ++j) {
tt->ind[m][... |
ShadenSmith/splatt/src/matrix.c | #pragma omp parallel for schedule(static) | 100 | N = B->J;
idx_t const Na = A->J;
/* tiled matrix multiplication */
idx_t const TILE = 16;
<LOOP-START>for(idx_t i=0; i < M; ++i) {
for(idx_t jt=0; jt < N; jt += TILE) {
for(idx_t kt=0; kt < Na; kt += TILE) {
idx_t const JSTOP = SS_MIN(jt+TILE, N);
for(idx_t j=jt; j < JSTOP; ++j) {
... |
ShadenSmith/splatt/src/sptensor.c | #pragma omp parallel for schedule(static) | 100 | t(hist, 0, tt->dims[mode] * sizeof(*hist));
idx_t const * const restrict inds = tt->ind[mode];
<LOOP-START>for(idx_t x=0; x < tt->nnz; ++x) {
#pragma omp atomic
++hist[inds[x]];
}<LOOP-END> <OMP-START>#pragma omp parallel for schedule(static)<OMP-END> |
ShadenSmith/splatt/src/io.c | #pragma omp parallel for schedule(static) | 100 | t read_count = SS_MIN(BUF_LEN, count - n);
fread(ubuf, sizeof(*ubuf), read_count, fin);
<LOOP-START>for(idx_t i=0; i < read_count; ++i) {
buffer[n + i] = ubuf[i];
}<LOOP-END> <OMP-START>#pragma omp parallel for schedule(static)<OMP-END> |
ShadenSmith/splatt/src/io.c | #pragma omp parallel for schedule(static) | 100 | t read_count = SS_MIN(BUF_LEN, count - n);
fread(ubuf, sizeof(*ubuf), read_count, fin);
<LOOP-START>for(idx_t i=0; i < read_count; ++i) {
buffer[n + i] = ubuf[i];
}<LOOP-END> <OMP-START>#pragma omp parallel for schedule(static)<OMP-END> |
ShadenSmith/splatt/src/ftensor.c | #pragma omp parallel for reduction(+:nfibs) | 100 | ttinds[nmodes-1][0];
ft->vals[0] = tt->vals[0];
/* count fibers in tt */
idx_t nfibs = 0;
<LOOP-START>for(idx_t n=1; n < nnz; ++n) {
for(idx_t m=0; m < nmodes-1; ++m) {
/* check for new fiber */
if(ttinds[m][n] != ttinds[m][n-1]) {
++nfibs;
break;
}
}
ft->inds[n] ... |
ShadenSmith/splatt/src/csf.c | #pragma omp parallel for schedule(static) | 100 | ices = csf->pt[tile_id].nfibs[0];
idx_t * weights = splatt_malloc(nslices * sizeof(*weights));
<LOOP-START>for(idx_t i=0; i < nslices; ++i) {
weights[i] = p_csf_count_nnz(csf->pt[tile_id].fptr, csf->nmodes, 0, i);
}<LOOP-END> <OMP-START>#pragma omp parallel for schedule(static)<OMP-END> |
ShadenSmith/splatt/src/csf.c | #pragma omp parallel for schedule(static) | 100 | idx_t const ntiles = csf->ntiles;
idx_t * weights = splatt_malloc(ntiles * sizeof(*weights));
<LOOP-START>for(idx_t i=0; i < ntiles; ++i) {
weights[i] = csf->pt[i].nfibs[nmodes-1];
}<LOOP-END> <OMP-START>#pragma omp parallel for schedule(static)<OMP-END> |
ShadenSmith/splatt/src/graph.c | #pragma omp parallel for | 100 | er */
case VTX_WT_FIB_NNZ:
hg->vwts = (idx_t *) splatt_malloc(hg->nvtxs * sizeof(idx_t));
<LOOP-START>for(idx_t v=0; v < hg->nvtxs; ++v) {
hg->vwts[v] = ft->fptr[v+1] - ft->fptr[v];
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
ShadenSmith/splatt/src/mpi/mpi_cpd.c | #pragma omp parallel for | 100 | al_t * const restrict gmatv = globalmat->vals;
/* copy my partial products into the sendbuf */
<LOOP-START>for(idx_t s=0; s < rinfo->nlocal2nbr[m]; ++s) {
idx_t const row = local2nbr_inds[s];
for(idx_t f=0; f < nfactors; ++f) {
local2nbr_buf[f + (s*nfactors)] = matv[f + (row*nfactors)];
}
}<LOO... |
ShadenSmith/splatt/src/mpi/mpi_io.c | #pragma omp parallel for schedule(static, 1) | 100 | p_rearrange_medium(
sptensor_t * const ttbuf,
idx_t * * ssizes,
rank_info * const rinfo)
{
<LOOP-START>for(idx_t m=0; m < ttbuf->nmodes; ++m) {
p_find_layer_boundaries(ssizes, m, rinfo);
}<LOOP-END> <OMP-START>#pragma omp parallel for schedule(static, 1)<OMP-END> |
ShadenSmith/splatt/src/mpi/mpi_io.c | #pragma omp parallel for schedule(static) | 100 | o);
}
/* create partitioning */
int * parts = splatt_malloc(ttbuf->nnz * sizeof(*parts));
<LOOP-START>for(idx_t n=0; n < ttbuf->nnz; ++n) {
parts[n] = mpi_determine_med_owner(ttbuf, n, rinfo);
}<LOOP-END> <OMP-START>#pragma omp parallel for schedule(static)<OMP-END> |
ShadenSmith/splatt/src/mpi/mpi_io.c | #pragma omp parallel for schedule(static, 1) | 100 | ssizes, rinfo);
/* now map tensor indices to local (layer) coordinates and fill in dims */
<LOOP-START>for(idx_t m=0; m < ttbuf->nmodes; ++m) {
tt->dims[m] = rinfo->layer_ends[m] - rinfo->layer_starts[m];
for(idx_t n=0; n < tt->nnz; ++n) {
assert(tt->ind[m][n] >= rinfo->layer_starts[m]);
... |
ShadenSmith/splatt/src/mpi/mpi_io.c | #pragma omp parallel for schedule(static, 1) | 100 | ry_file(fin, comm);
break;
}
if(rank == 0) {
fclose(fin);
}
/* set dims info */
<LOOP-START>for(idx_t m=0; m < tt->nmodes; ++m) {
idx_t const * const inds = tt->ind[m];
idx_t dim = 1 +inds[0];
for(idx_t n=1; n < tt->nnz; ++n) {
dim = SS_MAX(dim, 1 + inds[n]);
}
tt->dims[m] ... |
ShadenSmith/splatt/src/mpi/mpi_io.c | #pragma omp parallel for schedule(static) | 100 | tor = mat_rand(rinfo->global_dims[mode], nfactors);
/* copy root's own matrix to output */
<LOOP-START>for(idx_t i=0; i < localdim; ++i) {
idx_t const gi = rinfo->mat_start[mode] + perm->iperms[mode][i];
for(idx_t f=0; f < nfactors; ++f) {
mymat->vals[f + (i*nfactors)] = full_factor->vals[f+... |
ShadenSmith/splatt/src/mpi/mpi_io.c | #pragma omp parallel for schedule(static) | 100 | ecv(loc_perm, nrows, SPLATT_MPI_IDX, p, 2, rinfo->comm_3d, &status);
/* fill buffer */
<LOOP-START>for(idx_t i=0; i < nrows; ++i) {
idx_t const gi = layerstart + loc_perm[i];
for(idx_t f=0; f < nfactors; ++f) {
vbuf[f + (i*nfactors)] = full_factor->vals[f+(gi*nfactors)];
}... |
adammaj1/Mandelbrot-set-with-blended-gradients/src/d.c | #pragma omp parallel for schedule(dynamic) private(ix,iy, i, Cx, Cy) shared(A, ixMax , iyMax) | 100 | coordinate
fprintf(stderr, "compute image CheckOrientation\n");
// for all pixels of image
<LOOP-START>for (iy = iyMin; iy <= iyMax; ++iy){
fprintf (stderr, " %d from %d \r", iy, iyMax); //info
for (ix = ixMin; ix <= ixMax; ++ix){
// from screen to world coordinate
Cy = GiveCy(iy);
... |
adammaj1/Mandelbrot-set-with-blended-gradients/src/d.c | #pragma omp parallel for schedule(dynamic) private(ix,iy) shared(A, ixMax , iyMax) | 100 | int ix, iy; // pixel coordinate
//printf("compute image \n");
// for all pixels of image
<LOOP-START>for (iy = iyMin; iy <= iyMax; ++iy){
fprintf (stderr, " %d from %d \r", iy, iyMax); //info
for (ix = ixMin; ix <= ixMax; ++ix)
ComputePoint_dData(A, RepresentationFunction, ix, iy); // ... |
adammaj1/Mandelbrot-set-with-blended-gradients/src/d.c | #pragma omp parallel for schedule(dynamic) private(i) shared( D, C, iSize) | 100 | rr, "\nFill_rgbData_from_dData\n");
//printf("compute image \n");
// for all pixels of image
<LOOP-START>for (i = 0; i < iSize; ++i){
//fprintf (stderr, "rgb %d from %d \r", i, iSize); //info
ComputeAndSaveColor(i, D, RepresentationFunction, Gradient, C); //
}<LOOP-END> <OMP-START>#pragma omp p... |
adammaj1/Mandelbrot-set-with-blended-gradients/src/d.c | #pragma omp parallel for schedule(dynamic) private(i) shared( C1, C2, C, iSize) | 100 | , "\nFill_rgbData_from_2_dData\n");
//printf("compute image \n");
// for all pixels of image
<LOOP-START>for (i = 0; i < iSize; ++i){
ComputeAndSaveBlendColor( C1, C2, Blend, i, C);
}<LOOP-END> <OMP-START>#pragma omp parallel for schedule(dynamic) private(i) shared( C1, C2, C, iSize)<OMP-END> |
trrt-good/NeuralNetworks.c/NeuralNetCPU/neural_net_legacy.c | #pragma omp parallel for | 100 | dense(nnet->weights[0], npl[1], npl[0], inputs, nnet->biases[0], activations[0]);
int i;
// <LOOP-START>for (i = 1; i < LAYERS; i++)
{
nnet_layer_function_dense(nnet->weights[i], npl[i + 1], npl[i], activations[i - 1], nnet->biases[i], activations[i]);
}<LOOP-END> <OMP-START>#pragma omp parallel... |
trrt-good/NeuralNetworks.c/NeuralNetCPU/neural_net_legacy.c | #pragma omp parallel for | 100 | training_set->num_examples));
for (batch = 0; batch < parallel_batches; batch++)
{
<LOOP-START>for (thread = 0; thread < MAX_THREADS; thread++)
{
for (int nthExample = (batch * MAX_THREADS + thread) * examples_per_thread; nthExample < (batch * MAX_THREADS + thread + 1) * exa... |
trrt-good/NeuralNetworks.c/NeuralNetCPU/neural_net.c | #pragma omp parallel for | 100 | t->num_examples));
for (batch = 0; batch < parallel_batches; batch++)
{
<LOOP-START>for (thread = 0; thread < MAX_THREADS; thread++)
{
for (int nthExample = (batch * MAX_THREADS + thread) * examples_per_thread; nthExample < (batch * MAX_THREADS + thread + 1) * exa... |
ENCCS/intermediate-mpi/content/code/day-4/10_integrate-pi/solution/pi-integration.c | #pragma omp parallel for reduction(+:local_pi) | 100 | ntf("rank %d: start=%ld, end=%ld\n", rank, start, end);
double local_pi = 0.0;
long int i;
<LOOP-START>for (i = start; i < end; i++) {
double x = delta_x * ((double)(i) + 0.5);
local_pi += 1.0 / (1.0 + x * x);
}<LOOP-END> <OMP-START>#pragma omp parallel for reduction(+:local_pi)<OMP-END> |
ENCCS/intermediate-mpi/content/code/day-4/01_threading-funneled/threading-funneled.c | #pragma omp parallel for | 100 | tribute each
* iteration to a different thread. */
/* int local_work[] = FIXME; */
<LOOP-START>for (int k = 0; k != 2; k = k + 1)
{
/* compute_row(FIXME, working_data_set, next_working_data_set); */
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
ENCCS/intermediate-mpi/content/code/day-4/01_threading-funneled/threading-funneled.c | #pragma omp parallel for | 100 | ute each
* iteration to a different thread. */
/* int non_local_work[] = FIXME; */
<LOOP-START>for (int k = 0; k != 2; k = k + 1)
{
/* compute_row(FIXME, working_data_set, next_working_data_set); */
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
ENCCS/intermediate-mpi/content/code/day-4/01_threading-funneled/solution/threading-funneled.c | #pragma omp parallel for | 100 | l distribute each
* iteration to a different thread. */
int local_work[] = {2, 3};
<LOOP-START>for (int k = 0; k != 2; k = k + 1)
{
compute_row(local_work[k], working_data_set, next_working_data_set);
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
ENCCS/intermediate-mpi/content/code/day-4/01_threading-funneled/solution/threading-funneled.c | #pragma omp parallel for | 100 | stribute each
* iteration to a different thread. */
int non_local_work[] = {1, 4};
<LOOP-START>for (int k = 0; k != 2; k = k + 1)
{
compute_row(non_local_work[k], working_data_set, next_working_data_set);
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
ENCCS/intermediate-mpi/content/code/day-4/02_threading-multiple/threading-multiple.c | #pragma omp parallel for | 100 | cal computation. OpenMP will distribute each
* iteration to a different thread. */
<LOOP-START>for (int k = 0; k != 2; k = k + 1)
{
compute_row(/* FIXME */);
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
ENCCS/intermediate-mpi/content/code/day-4/02_threading-multiple/threading-multiple.c | #pragma omp parallel for | 100 | * iteration to a different thread. */
int non_local_work[] = /* FIXME */;
<LOOP-START>for (int k = 0; k != 2; k = k + 1)
{
compute_row(/* FIXME */);
}<LOOP-END> <OMP-START>#pragma omp parallel for<OMP-END> |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.