Neko 0.9.99
A portable framework for high-order spectral element flow simulations
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gradient_jump_penalty_kernel.h
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1#ifndef __COMMON_GRADIENT_JUMP_PENALTY_KERNEL_H__
2#define __COMMON_GRADIENT_JUMP_PENALTY_KERNEL_H__
3/*
4 Copyright (c) 2024, The Neko Authors
5 All rights reserved.
6
7 Redistribution and use in source and binary forms, with or without
8 modification, are permitted provided that the following conditions
9 are met:
10
11 * Redistributions of source code must retain the above copyright
12 notice, this list of conditions and the following disclaimer.
13
14 * Redistributions in binary form must reproduce the above
15 copyright notice, this list of conditions and the following
16 disclaimer in the documentation and/or other materials provided
17 with the distribution.
18
19 * Neither the name of the authors nor the names of its
20 contributors may be used to endorse or promote products derived
21 from this software without specific prior written permission.
22
23 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
24 "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
25 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
26 FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
27 COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
28 INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
29 BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
30 LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
31 CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
32 LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
33 ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
34 POSSIBILITY OF SUCH DAMAGE.
35*/
36
40template< typename T>
41__global__ void pick_facet_value_hex_kernel(T * __restrict__ b,
42 T *__restrict__ a,
43 const int nx) {
44
45 const int idx = threadIdx.x;
46 const int nx2 = nx+2;
47 const int el2 = blockIdx.x*nx2*nx2*nx2;
48 const int el = blockIdx.x*nx*nx*nx;
49 for(int ijk = idx; ijk < nx*nx*nx; ijk += blockDim.x){
50 const int jk = ijk / nx;
51 const int i = ijk - jk * nx;
52 const int k = jk / nx;
53 const int j = jk - k * nx;
54 if(i == 0){
55 b[0+(j+1)*nx2+(k+1)*nx2*nx2+el2] = a[ijk+el];
56 }
57 if(i == nx-1){
58 b[nx2-1+(j+1)*nx2+(k+1)*nx2*nx2+el2] = a[ijk+el];
59 }
60 if(j == 0){
61 b[(i+1)+0*nx2+(k+1)*nx2*nx2+el2] = a[ijk+el];
62 }
63 if(j == nx-1){
64 b[(i+1)+(nx2-1)*nx2+(k+1)*nx2*nx2+el2] = a[ijk+el];
65 }
66 if(k == 0){
67 b[(i+1)+(j+1)*nx2+0*nx2*nx2+el2] = a[ijk+el];
68 }
69 if(k == nx-1){
70 b[(i+1)+(j+1)*nx2+(nx2-1)*nx2*nx2+el2] = a[ijk+el];
71 }
72 }
73}
74
78template< typename T>
79__global__ void gradient_jump_penalty_finalize_kernel(T * __restrict__ penalty_d,
80 T *__restrict__ penalty_facet_d,
81 T *__restrict__ dphidxi_d,
82 const int nx) {
83
84 const int idx = threadIdx.x;
85 const int nx2 = nx+2;
86 const int el2 = blockIdx.x*nx2*nx2*nx2;
87 const int el = blockIdx.x*nx*nx*nx;
88 for(int ijk = idx; ijk < nx*nx*nx; ijk += blockDim.x){
89 const int jk = ijk / nx;
90 const int i = ijk - jk * nx;
91 const int k = jk / nx;
92 const int j = jk - k * nx;
93 penalty_d[ijk+el] = penalty_facet_d[0+(j+1)*nx2+(k+1)*nx2*nx2+el2] \
94 * dphidxi_d[0+i*nx] + \
95 penalty_facet_d[(nx2-1)+(j+1)*nx2+(k+1)*nx2*nx2+el2] \
96 * dphidxi_d[nx-1+i*nx] + \
97 penalty_facet_d[(i+1)+0*nx2+(k+1)*nx2*nx2+el2] \
98 * dphidxi_d[0+j*nx] + \
99 penalty_facet_d[(i+1)+(nx2-1)*nx2+(k+1)*nx2*nx2+el2] \
100 * dphidxi_d[nx-1+j*nx] + \
101 penalty_facet_d[(i+1)+(j+1)*nx2+0*nx2*nx2+el2] \
102 * dphidxi_d[0+k*nx] + \
103 penalty_facet_d[(i+1)+(j+1)*nx2+(nx2-1)*nx2*nx2+el2] \
104 * dphidxi_d[nx-1+k*nx];
105
106 }
107}
108
109#endif // __COMMON_GRADIENT_JUMP_PENALTY_KERNEL_H__
110
i
const int i
Definition ax_helm_full_kernel.h:95
j
const int j
Definition ax_helm_full_kernel.h:94
dirichlet_apply_scalar_kernel
__global__ void dirichlet_apply_scalar_kernel(const int *__restrict__ msk, T *__restrict__ x, const T g, const int m)
Definition dirichlet_kernel.h:42
gradient_jump_penalty_finalize_kernel
__global__ void gradient_jump_penalty_finalize_kernel(T *__restrict__ penalty_d, T *__restrict__ penalty_facet_d, T *__restrict__ dphidxi_d, const int nx)
Definition gradient_jump_penalty_kernel.h:79
pick_facet_value_hex_kernel
__global__ void pick_facet_value_hex_kernel(T *__restrict__ b, T *__restrict__ a, const int nx)
Definition gradient_jump_penalty_kernel.h:41