Neko 1.99.1
A portable framework for high-order spectral element flow simulations
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tensor_kernel.h
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1#ifndef __MATH_TENSOR_KERNEL_H__
2#define __MATH_TENSOR_KERNEL_H__
3/*
4 Copyright (c) 2022, 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*/
36template< typename T, const int N >
37__global__ void tnsr3d_el_kernel(T * __restrict__ v,
38 const int nv,
39 const T * __restrict__ u,
40 const int nu,
41 const T * __restrict__ A,
42 const T * __restrict__ Bt,
43 const T * __restrict__ Ct,
44 const int * elements,
45 const int n_points) {
46 __shared__ T shwork[N*N*N];
47 __shared__ T shwork2[N*N*N];
48
49 const int idx = threadIdx.x;
50 const int str = blockDim.x;
51 const int pt = blockIdx.x;
52 const int e = elements[pt];
53
54 for (int ii = idx; ii< nu*nu*nv; ii += str) {
55 T tmp = 0.0;
56 int j = ii/nv;
57 int i = ii - j*nv;
58 for( int l = 0; l < nu; l++){
59 tmp += A[i+l*nv+pt*nv*nu]*u[l+nu*j+e*nu*nu*nu];
60 }
61 shwork[ii] = tmp;
62 }
63
64 __syncthreads();
65
66 for (int ijk = idx; ijk< nu*nv*nv; ijk += str) {
67 const int jk = ijk / nv;
68 const int i = ijk - jk * nv;
69 const int k = jk / nv;
70 const int j = jk - k * nv;
71 T tmp = 0.0;
72 const int ik2 = i + k*nv*nu;
73 for( int l = 0; l < nu; l++){
74 tmp += Bt[l+j*nu+pt*nv*nu]*shwork[l*nv+ik2];
75 }
76 shwork2[ijk] = tmp;
77 }
78
79 __syncthreads();
80
81 for (int ijk = idx; ijk< nv*nv*nv; ijk += str) {
82 const int jk = ijk / nv;
83 const int i = ijk - jk * nv;
84 const int k = jk / nv;
85 const int j = jk - k * nv;
86 T tmp = 0.0;
87 const int ij2 = i + j*nv;
88 for( int l = 0; l < nu; l++){
89 tmp += Ct[l+k*nu+pt*nv*nu]*shwork2[ij2 + l*nv*nv];
90 }
91 v[ijk+pt*nv*nv*nv] = tmp;
92 }
93
94}
95
96
97template< typename T, const int N >
98__global__ void tnsr3d_kernel(T * __restrict__ v,
99 const int nv,
100 const T * __restrict__ u,
101 const int nu,
102 const T * __restrict__ A,
103 const T * __restrict__ Bt,
104 const T * __restrict__ Ct) {
105 __shared__ T shwork[N*N*N];
106 __shared__ T shwork2[N*N*N];
107
108 const int idx = threadIdx.x;
109 const int str = blockDim.x;
110 const int e = blockIdx.x;
111
112 for (int ii = idx; ii< nu*nu*nv; ii += str) {
113 T tmp = 0.0;
114 int j = ii/nv;
115 int i = ii - j*nv;
116 for( int l = 0; l < nu; l++){
117 tmp += A[i+l*nv]*u[l+nu*j+e*nu*nu*nu];
118 }
119 shwork[ii] = tmp;
120 }
121
122 __syncthreads();
123
124 for (int ijk = idx; ijk< nu*nv*nv; ijk += str) {
125 const int jk = ijk / nv;
126 const int i = ijk - jk * nv;
127 const int k = jk / nv;
128 const int j = jk - k * nv;
129 T tmp = 0.0;
130 const int ik2 = i + k*nv*nu;
131 for( int l = 0; l < nu; l++){
132 tmp += Bt[l+j*nu]*shwork[l*nv+ik2];
133 }
134 shwork2[ijk] = tmp;
135 }
136
137 __syncthreads();
138
139 for (int ijk = idx; ijk< nv*nv*nv; ijk += str) {
140 const int jk = ijk / nv;
141 const int i = ijk - jk * nv;
142 const int k = jk / nv;
143 const int j = jk - k * nv;
144 T tmp = 0.0;
145 const int ij2 = i + j*nv;
146 for( int l = 0; l < nu; l++){
147 tmp += Ct[l+k*nu]*shwork2[ij2 + l*nv*nv];
148 }
149 v[ijk+e*nv*nv*nv] = tmp;
150 }
151
152}
153
154
155
156#endif // __MATH_TENSOR_KERNEL_H__
i
const int i
Definition ax_helm_full_kernel.h:95
u
__global__ void T *__restrict__ T *__restrict__ const T *__restrict__ u
Definition ax_helm_full_kernel.h:46
e
const int e
Definition ax_helm_full_kernel.h:93
v
__global__ void T *__restrict__ T *__restrict__ const T *__restrict__ const T *__restrict__ v
Definition ax_helm_full_kernel.h:47
j
const int j
Definition ax_helm_full_kernel.h:94
__syncthreads
__syncthreads()
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
tnsr3d_el_kernel
__global__ void tnsr3d_el_kernel(T *__restrict__ v, const int nv, const T *__restrict__ u, const int nu, const T *__restrict__ A, const T *__restrict__ Bt, const T *__restrict__ Ct, const int *elements, const int n_points)
Definition tensor_kernel.h:37
tnsr3d_kernel
__global__ void tnsr3d_kernel(T *__restrict__ v, const int nv, const T *__restrict__ u, const int nu, const T *__restrict__ A, const T *__restrict__ Bt, const T *__restrict__ Ct)
Definition tensor_kernel.h:99