1026 lines
37 KiB
Plaintext
1026 lines
37 KiB
Plaintext
/*
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Copyright (C) 2014-2018, Johannes Pekkilae, Miikka Vaeisalae.
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This file is part of Astaroth.
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Astaroth is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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Astaroth is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with Astaroth. If not, see <http://www.gnu.org/licenses/>.
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*/
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/**
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* @file
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* \brief Brief info.
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*
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* Detailed info.
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*
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*/
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#pragma once
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__global__ void
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kernel_periodic_boundconds(const int3 start, const int3 end, AcReal* vtxbuf)
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{
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const int i_dst = start.x + threadIdx.x + blockIdx.x * blockDim.x;
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const int j_dst = start.y + threadIdx.y + blockIdx.y * blockDim.y;
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const int k_dst = start.z + threadIdx.z + blockIdx.z * blockDim.z;
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// If within the start-end range (this allows threadblock dims that are not
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// divisible by end - start)
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if (i_dst >= end.x || j_dst >= end.y || k_dst >= end.z)
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return;
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// if (i_dst >= DCONST_INT(AC_mx) || j_dst >= DCONST_INT(AC_my) || k_dst >= DCONST_INT(AC_mz))
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// return;
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// If destination index is inside the computational domain, return since
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// the boundary conditions are only applied to the ghost zones
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if (i_dst >= DCONST_INT(AC_nx_min) && i_dst < DCONST_INT(AC_nx_max) &&
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j_dst >= DCONST_INT(AC_ny_min) && j_dst < DCONST_INT(AC_ny_max) &&
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k_dst >= DCONST_INT(AC_nz_min) && k_dst < DCONST_INT(AC_nz_max))
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return;
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// Find the source index
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// Map to nx, ny, nz coordinates
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int i_src = i_dst - DCONST_INT(AC_nx_min);
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int j_src = j_dst - DCONST_INT(AC_ny_min);
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int k_src = k_dst - DCONST_INT(AC_nz_min);
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// Translate (s.t. the index is always positive)
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i_src += DCONST_INT(AC_nx);
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j_src += DCONST_INT(AC_ny);
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k_src += DCONST_INT(AC_nz);
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// Wrap
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i_src %= DCONST_INT(AC_nx);
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j_src %= DCONST_INT(AC_ny);
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k_src %= DCONST_INT(AC_nz);
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// Map to mx, my, mz coordinates
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i_src += DCONST_INT(AC_nx_min);
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j_src += DCONST_INT(AC_ny_min);
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k_src += DCONST_INT(AC_nz_min);
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const int src_idx = DEVICE_VTXBUF_IDX(i_src, j_src, k_src);
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const int dst_idx = DEVICE_VTXBUF_IDX(i_dst, j_dst, k_dst);
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vtxbuf[dst_idx] = vtxbuf[src_idx];
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}
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void
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periodic_boundconds(const cudaStream_t stream, const int3& start, const int3& end, AcReal* vtxbuf)
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{
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const dim3 tpb(8, 2, 8);
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const dim3 bpg((unsigned int)ceil((end.x - start.x) / (float)tpb.x),
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(unsigned int)ceil((end.y - start.y) / (float)tpb.y),
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(unsigned int)ceil((end.z - start.z) / (float)tpb.z));
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kernel_periodic_boundconds<<<bpg, tpb, 0, stream>>>(start, end, vtxbuf);
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ERRCHK_CUDA_KERNEL();
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////
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#include <assert.h>
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static __device__ __forceinline__ int
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IDX(const int i)
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{
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return i;
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}
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static __device__ __forceinline__ int
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IDX(const int i, const int j, const int k)
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{
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return DEVICE_VTXBUF_IDX(i, j, k);
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}
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static __device__ __forceinline__ int
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IDX(const int3 idx)
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{
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return DEVICE_VTXBUF_IDX(idx.x, idx.y, idx.z);
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}
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static __forceinline__ AcMatrix
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create_rotz(const AcReal radians)
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{
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AcMatrix mat;
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mat.row[0] = (AcReal3){cos(radians), -sin(radians), 0};
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mat.row[1] = (AcReal3){sin(radians), cos(radians), 0};
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mat.row[2] = (AcReal3){0, 0, 0};
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return mat;
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}
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#if AC_DOUBLE_PRECISION == 0
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#define sin __sinf
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#define cos __cosf
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#define exp __expf
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#define rsqrt rsqrtf // hardware reciprocal sqrt
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#endif // AC_DOUBLE_PRECISION == 0
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/*
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typedef struct {
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int i, j, k;
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} int3;*/
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/*
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* =============================================================================
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* Level 0 (Input Assembly Stage)
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* =============================================================================
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*/
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/*
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* =============================================================================
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* Level 0.1 (Read stencil elements and solve derivatives)
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* =============================================================================
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*/
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static __device__ __forceinline__ AcReal
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first_derivative(const AcReal* __restrict__ pencil, const AcReal inv_ds)
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{
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#if STENCIL_ORDER == 2
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const AcReal coefficients[] = {0, 1.0 / 2.0};
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#elif STENCIL_ORDER == 4
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const AcReal coefficients[] = {0, 2.0 / 3.0, -1.0 / 12.0};
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#elif STENCIL_ORDER == 6
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const AcReal coefficients[] = {0, 3.0 / 4.0, -3.0 / 20.0, 1.0 / 60.0};
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#elif STENCIL_ORDER == 8
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const AcReal coefficients[] = {0, 4.0 / 5.0, -1.0 / 5.0, 4.0 / 105.0, -1.0 / 280.0};
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#endif
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#define MID (STENCIL_ORDER / 2)
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AcReal res = 0;
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#pragma unroll
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for (int i = 1; i <= MID; ++i)
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res += coefficients[i] * (pencil[MID + i] - pencil[MID - i]);
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return res * inv_ds;
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}
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static __device__ __forceinline__ AcReal
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second_derivative(const AcReal* __restrict__ pencil, const AcReal inv_ds)
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{
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#if STENCIL_ORDER == 2
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const AcReal coefficients[] = {-2., 1.};
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#elif STENCIL_ORDER == 4
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const AcReal coefficients[] = {-5.0 / 2.0, 4.0 / 3.0, -1.0 / 12.0};
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#elif STENCIL_ORDER == 6
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const AcReal coefficients[] = {-49.0 / 18.0, 3.0 / 2.0, -3.0 / 20.0, 1.0 / 90.0};
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#elif STENCIL_ORDER == 8
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const AcReal coefficients[] = {-205.0 / 72.0, 8.0 / 5.0, -1.0 / 5.0, 8.0 / 315.0, -1.0 / 560.0};
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#endif
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#define MID (STENCIL_ORDER / 2)
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AcReal res = coefficients[0] * pencil[MID];
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#pragma unroll
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for (int i = 1; i <= MID; ++i)
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res += coefficients[i] * (pencil[MID + i] + pencil[MID - i]);
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return res * inv_ds * inv_ds;
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}
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/** inv_ds: inverted mesh spacing f.ex. 1. / mesh.int_params[AC_dsx] */
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static __device__ __forceinline__ AcReal
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cross_derivative(const AcReal* __restrict__ pencil_a, const AcReal* __restrict__ pencil_b,
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const AcReal inv_ds_a, const AcReal inv_ds_b)
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{
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#if STENCIL_ORDER == 2
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const AcReal coefficients[] = {0, 1.0 / 4.0};
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#elif STENCIL_ORDER == 4
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const AcReal coefficients[] = {
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0, 1.0 / 32.0, 1.0 / 64.0}; // TODO correct coefficients, these are just placeholders
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#elif STENCIL_ORDER == 6
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const AcReal fac = (1. / 720.);
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const AcReal coefficients[] = {0.0 * fac, 270.0 * fac, -27.0 * fac, 2.0 * fac};
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#elif STENCIL_ORDER == 8
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const AcReal fac = (1. / 20160.);
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const AcReal coefficients[] = {0.0 * fac, 8064. * fac, -1008. * fac, 128. * fac, -9. * fac};
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#endif
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#define MID (STENCIL_ORDER / 2)
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AcReal res = AcReal(0.);
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#pragma unroll
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for (int i = 1; i <= MID; ++i) {
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res += coefficients[i] *
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(pencil_a[MID + i] + pencil_a[MID - i] - pencil_b[MID + i] - pencil_b[MID - i]);
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}
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return res * inv_ds_a * inv_ds_b;
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}
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static __device__ __forceinline__ AcReal
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derx(const int3 vertexIdx, const AcReal* __restrict__ arr)
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{
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AcReal pencil[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil[offset] = arr[IDX(vertexIdx.x + offset - STENCIL_ORDER / 2, vertexIdx.y,
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vertexIdx.z)];
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return first_derivative(pencil, DCONST_REAL(AC_inv_dsx));
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}
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static __device__ __forceinline__ AcReal
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derxx(const int3 vertexIdx, const AcReal* __restrict__ arr)
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{
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AcReal pencil[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil[offset] = arr[IDX(vertexIdx.x + offset - STENCIL_ORDER / 2, vertexIdx.y,
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vertexIdx.z)];
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return second_derivative(pencil, DCONST_REAL(AC_inv_dsx));
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}
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static __device__ __forceinline__ AcReal
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derxy(const int3 vertexIdx, const AcReal* __restrict__ arr)
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{
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AcReal pencil_a[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil_a[offset] = arr[IDX(vertexIdx.x + offset - STENCIL_ORDER / 2,
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vertexIdx.y + offset - STENCIL_ORDER / 2, vertexIdx.z)];
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AcReal pencil_b[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil_b[offset] = arr[IDX(vertexIdx.x + offset - STENCIL_ORDER / 2,
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vertexIdx.y + STENCIL_ORDER / 2 - offset, vertexIdx.z)];
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return cross_derivative(pencil_a, pencil_b, DCONST_REAL(AC_inv_dsx), DCONST_REAL(AC_inv_dsy));
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}
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static __device__ __forceinline__ AcReal
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derxz(const int3 vertexIdx, const AcReal* __restrict__ arr)
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{
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AcReal pencil_a[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil_a[offset] = arr[IDX(vertexIdx.x + offset - STENCIL_ORDER / 2, vertexIdx.y,
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vertexIdx.z + offset - STENCIL_ORDER / 2)];
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AcReal pencil_b[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil_b[offset] = arr[IDX(vertexIdx.x + offset - STENCIL_ORDER / 2, vertexIdx.y,
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vertexIdx.z + STENCIL_ORDER / 2 - offset)];
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return cross_derivative(pencil_a, pencil_b, DCONST_REAL(AC_inv_dsx), DCONST_REAL(AC_inv_dsz));
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}
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static __device__ __forceinline__ AcReal
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dery(const int3 vertexIdx, const AcReal* __restrict__ arr)
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{
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AcReal pencil[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil[offset] = arr[IDX(vertexIdx.x, vertexIdx.y + offset - STENCIL_ORDER / 2,
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vertexIdx.z)];
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return first_derivative(pencil, DCONST_REAL(AC_inv_dsy));
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}
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static __device__ __forceinline__ AcReal
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deryy(const int3 vertexIdx, const AcReal* __restrict__ arr)
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{
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AcReal pencil[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil[offset] = arr[IDX(vertexIdx.x, vertexIdx.y + offset - STENCIL_ORDER / 2,
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vertexIdx.z)];
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return second_derivative(pencil, DCONST_REAL(AC_inv_dsy));
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}
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static __device__ __forceinline__ AcReal
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deryz(const int3 vertexIdx, const AcReal* __restrict__ arr)
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{
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AcReal pencil_a[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil_a[offset] = arr[IDX(vertexIdx.x, vertexIdx.y + offset - STENCIL_ORDER / 2,
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vertexIdx.z + offset - STENCIL_ORDER / 2)];
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AcReal pencil_b[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil_b[offset] = arr[IDX(vertexIdx.x, vertexIdx.y + offset - STENCIL_ORDER / 2,
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vertexIdx.z + STENCIL_ORDER / 2 - offset)];
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return cross_derivative(pencil_a, pencil_b, DCONST_REAL(AC_inv_dsy), DCONST_REAL(AC_inv_dsz));
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}
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static __device__ __forceinline__ AcReal
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derz(const int3 vertexIdx, const AcReal* __restrict__ arr)
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{
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AcReal pencil[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil[offset] = arr[IDX(vertexIdx.x, vertexIdx.y,
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vertexIdx.z + offset - STENCIL_ORDER / 2)];
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return first_derivative(pencil, DCONST_REAL(AC_inv_dsz));
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}
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static __device__ __forceinline__ AcReal
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derzz(const int3 vertexIdx, const AcReal* __restrict__ arr)
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{
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AcReal pencil[STENCIL_ORDER + 1];
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#pragma unroll
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for (int offset = 0; offset < STENCIL_ORDER + 1; ++offset)
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pencil[offset] = arr[IDX(vertexIdx.x, vertexIdx.y,
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vertexIdx.z + offset - STENCIL_ORDER / 2)];
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return second_derivative(pencil, DCONST_REAL(AC_inv_dsz));
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}
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/*
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* =============================================================================
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* Level 0.2 (Caching functions)
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* =============================================================================
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*/
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#include "stencil_assembly.cuh"
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/*
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typedef struct {
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AcRealData x;
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AcRealData y;
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AcRealData z;
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} AcReal3Data;
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static __device__ __forceinline__ AcReal3Data
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read_data(const int i, const int j, const int k,
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AcReal* __restrict__ buf[], const int3& handle)
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{
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AcReal3Data data;
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data.x = read_data(i, j, k, buf, handle.x);
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data.y = read_data(i, j, k, buf, handle.y);
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data.z = read_data(i, j, k, buf, handle.z);
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return data;
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}
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*/
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/*
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* =============================================================================
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* Level 0.3 (Built-in functions available during the Stencil Processing Stage)
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* =============================================================================
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*/
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static __host__ __device__ __forceinline__ AcReal3
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operator-(const AcReal3& a, const AcReal3& b)
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{
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return (AcReal3){a.x - b.x, a.y - b.y, a.z - b.z};
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}
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static __host__ __device__ __forceinline__ AcReal3
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operator+(const AcReal3& a, const AcReal3& b)
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{
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return (AcReal3){a.x + b.x, a.y + b.y, a.z + b.z};
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}
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static __host__ __device__ __forceinline__ AcReal3
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operator-(const AcReal3& a)
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{
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return (AcReal3){-a.x, -a.y, -a.z};
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}
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static __host__ __device__ __forceinline__ AcReal3 operator*(const AcReal a, const AcReal3& b)
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{
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return (AcReal3){a * b.x, a * b.y, a * b.z};
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}
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static __host__ __device__ __forceinline__ AcReal
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dot(const AcReal3& a, const AcReal3& b)
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{
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return a.x * b.x + a.y * b.y + a.z * b.z;
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}
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static __host__ __device__ __forceinline__ AcReal3
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mul(const AcMatrix& aa, const AcReal3& x)
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{
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return (AcReal3){dot(aa.row[0], x), dot(aa.row[1], x), dot(aa.row[2], x)};
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}
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static __host__ __device__ __forceinline__ AcReal3
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cross(const AcReal3& a, const AcReal3& b)
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{
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AcReal3 c;
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c.x = a.y * b.z - a.z * b.y;
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c.y = a.z * b.x - a.x * b.z;
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c.z = a.x * b.y - a.y * b.x;
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return c;
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}
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static __host__ __device__ __forceinline__ bool
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is_valid(const AcReal& a)
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{
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return !isnan(a) && !isinf(a);
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}
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static __host__ __device__ __forceinline__ bool
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is_valid(const AcReal3& a)
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{
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return is_valid(a.x) && is_valid(a.y) && is_valid(a.z);
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}
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/*
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* =============================================================================
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* Level 1 (Stencil Processing Stage)
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* =============================================================================
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*/
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/*
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* =============================================================================
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* Level 1.1 (Terms)
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* =============================================================================
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*/
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static __device__ __forceinline__ AcReal
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laplace(const AcRealData& data)
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{
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return hessian(data).row[0].x + hessian(data).row[1].y + hessian(data).row[2].z;
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}
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static __device__ __forceinline__ AcReal
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divergence(const AcReal3Data& vec)
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{
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return gradient(vec.x).x + gradient(vec.y).y + gradient(vec.z).z;
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}
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static __device__ __forceinline__ AcReal3
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laplace_vec(const AcReal3Data& vec)
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{
|
|
return (AcReal3){laplace(vec.x), laplace(vec.y), laplace(vec.z)};
|
|
}
|
|
|
|
static __device__ __forceinline__ AcReal3
|
|
curl(const AcReal3Data& vec)
|
|
{
|
|
return (AcReal3){gradient(vec.z).y - gradient(vec.y).z, gradient(vec.x).z - gradient(vec.z).x,
|
|
gradient(vec.y).x - gradient(vec.x).y};
|
|
}
|
|
|
|
static __device__ __forceinline__ AcReal3
|
|
gradient_of_divergence(const AcReal3Data& vec)
|
|
{
|
|
return (AcReal3){hessian(vec.x).row[0].x + hessian(vec.y).row[0].y + hessian(vec.z).row[0].z,
|
|
hessian(vec.x).row[1].x + hessian(vec.y).row[1].y + hessian(vec.z).row[1].z,
|
|
hessian(vec.x).row[2].x + hessian(vec.y).row[2].y + hessian(vec.z).row[2].z};
|
|
}
|
|
|
|
// Takes uu gradients and returns S
|
|
static __device__ __forceinline__ AcMatrix
|
|
stress_tensor(const AcReal3Data& vec)
|
|
{
|
|
AcMatrix S;
|
|
|
|
S.row[0].x = AcReal(2. / 3.) * gradient(vec.x).x -
|
|
AcReal(1. / 3.) * (gradient(vec.y).y + gradient(vec.z).z);
|
|
S.row[0].y = AcReal(1. / 2.) * (gradient(vec.x).y + gradient(vec.y).x);
|
|
S.row[0].z = AcReal(1. / 2.) * (gradient(vec.x).z + gradient(vec.z).x);
|
|
|
|
S.row[1].y = AcReal(2. / 3.) * gradient(vec.y).y -
|
|
AcReal(1. / 3.) * (gradient(vec.x).x + gradient(vec.z).z);
|
|
|
|
S.row[1].z = AcReal(1. / 2.) * (gradient(vec.y).z + gradient(vec.z).y);
|
|
|
|
S.row[2].z = AcReal(2. / 3.) * gradient(vec.z).z -
|
|
AcReal(1. / 3.) * (gradient(vec.x).x + gradient(vec.y).y);
|
|
|
|
S.row[1].x = S.row[0].y;
|
|
S.row[2].x = S.row[0].z;
|
|
S.row[2].y = S.row[1].z;
|
|
|
|
return S;
|
|
}
|
|
|
|
static __device__ __forceinline__ AcReal
|
|
contract(const AcMatrix& mat)
|
|
{
|
|
AcReal res = 0;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < 3; ++i)
|
|
res += dot(mat.row[i], mat.row[i]);
|
|
|
|
return res;
|
|
}
|
|
|
|
/*
|
|
* =============================================================================
|
|
* Level 1.2 (Equations)
|
|
* =============================================================================
|
|
*/
|
|
static __device__ __forceinline__ AcReal
|
|
length(const AcReal3& vec)
|
|
{
|
|
return sqrt(vec.x * vec.x + vec.y * vec.y + vec.z * vec.z);
|
|
}
|
|
|
|
static __device__ __forceinline__ AcReal
|
|
reciprocal_len(const AcReal3& vec)
|
|
{
|
|
return rsqrt(vec.x * vec.x + vec.y * vec.y + vec.z * vec.z);
|
|
}
|
|
|
|
static __device__ __forceinline__ AcReal3
|
|
normalized(const AcReal3& vec)
|
|
{
|
|
const AcReal inv_len = reciprocal_len(vec);
|
|
return inv_len * vec;
|
|
}
|
|
|
|
// Sinusoidal forcing
|
|
// https://arxiv.org/pdf/1704.04676.pdf
|
|
// NOTE: This method of forcing is depracated. However, it will remain in here
|
|
// until a corresponding scheme exists in the new code.
|
|
__constant__ AcReal3 forcing_vec;
|
|
__constant__ AcReal forcing_phi;
|
|
static __device__ __forceinline__ AcReal3
|
|
DEPRECATED_forcing(const int i, const int j, const int k)
|
|
{
|
|
#define DOMAIN_SIZE_X (DCONST_INT(AC_nx) * DCONST_REAL(AC_dsx))
|
|
#define DOMAIN_SIZE_Y (DCONST_INT(AC_ny) * DCONST_REAL(AC_dsy))
|
|
#define DOMAIN_SIZE_Z (DCONST_INT(AC_nz) * DCONST_REAL(AC_dsz))
|
|
const AcReal3 k_vec = (AcReal3){
|
|
(i - DCONST_INT(AC_nx_min)) * DCONST_REAL(AC_dsx) - AcReal(.5) * DOMAIN_SIZE_X,
|
|
(j - DCONST_INT(AC_ny_min)) * DCONST_REAL(AC_dsy) - AcReal(.5) * DOMAIN_SIZE_Y,
|
|
(k - DCONST_INT(AC_nz_min)) * DCONST_REAL(AC_dsz) - AcReal(.5) * DOMAIN_SIZE_Z};
|
|
AcReal inv_len = reciprocal_len(k_vec);
|
|
if (isnan(inv_len) || isinf(inv_len))
|
|
inv_len = 0;
|
|
if (inv_len > 2) // hack to make it cool
|
|
inv_len = 2;
|
|
const AcReal k_dot_x = dot(k_vec, forcing_vec);
|
|
|
|
const AcReal waves = cos(k_dot_x) * cos(forcing_phi) - sin(k_dot_x) * sin(forcing_phi);
|
|
|
|
return inv_len * inv_len * waves * forcing_vec;
|
|
}
|
|
|
|
// Note: LNT0 and LNRHO0 must be set very carefully: if the magnitude is different that other values
|
|
// in the mesh, then we will inherently lose precision
|
|
#define LNT0 (AcReal(0.0))
|
|
#define LNRHO0 (AcReal(0.0))
|
|
|
|
#define H_CONST (AcReal(0.0))
|
|
#define C_CONST (AcReal(0.0))
|
|
|
|
template <int step_number>
|
|
static __device__ __forceinline__ AcReal
|
|
rk3_integrate(const AcReal state_previous, const AcReal state_current, const AcReal rate_of_change,
|
|
const AcReal dt)
|
|
{
|
|
// Williamson (1980)
|
|
const AcReal alpha[] = {0, AcReal(.0), AcReal(-5. / 9.), AcReal(-153. / 128.)};
|
|
const AcReal beta[] = {0, AcReal(1. / 3.), AcReal(15. / 16.), AcReal(8. / 15.)};
|
|
|
|
// Note the indexing: +1 to avoid an unnecessary warning about "out-of-bounds"
|
|
// access (when accessing beta[step_number-1] even when step_number >= 1)
|
|
switch (step_number) {
|
|
case 0:
|
|
return state_current + beta[step_number + 1] * rate_of_change * dt;
|
|
case 1: // Fallthrough
|
|
case 2:
|
|
return state_current +
|
|
beta[step_number + 1] * (alpha[step_number + 1] * (AcReal(1.) / beta[step_number]) *
|
|
(state_current - state_previous) +
|
|
rate_of_change * dt);
|
|
default:
|
|
return NAN;
|
|
}
|
|
}
|
|
/*
|
|
template <int step_number>
|
|
static __device__ __forceinline__ AcReal
|
|
rk3_integrate_scal(const AcReal state_previous, const AcReal state_current,
|
|
const AcReal rate_of_change, const AcReal dt)
|
|
{
|
|
// Williamson (1980)
|
|
const AcReal alpha[] = {AcReal(.0), AcReal(-5. / 9.), AcReal(-153. / 128.)};
|
|
const AcReal beta[] = {AcReal(1. / 3.), AcReal(15. / 16.),
|
|
AcReal(8. / 15.)};
|
|
|
|
|
|
switch (step_number) {
|
|
case 0:
|
|
return state_current + beta[step_number] * rate_of_change * dt;
|
|
case 1: // Fallthrough
|
|
case 2:
|
|
return state_current +
|
|
beta[step_number] *
|
|
(alpha[step_number] * (AcReal(1.) / beta[step_number - 1]) *
|
|
(state_current - state_previous) +
|
|
rate_of_change * dt);
|
|
default:
|
|
return NAN;
|
|
}
|
|
}
|
|
*/
|
|
|
|
template <int step_number>
|
|
static __device__ __forceinline__ AcReal3
|
|
rk3_integrate(const AcReal3 state_previous, const AcReal3 state_current,
|
|
const AcReal3 rate_of_change, const AcReal dt)
|
|
{
|
|
return (AcReal3){
|
|
rk3_integrate<step_number>(state_previous.x, state_current.x, rate_of_change.x, dt),
|
|
rk3_integrate<step_number>(state_previous.y, state_current.y, rate_of_change.y, dt),
|
|
rk3_integrate<step_number>(state_previous.z, state_current.z, rate_of_change.z, dt)};
|
|
}
|
|
|
|
#define rk3(state_previous, state_current, rate_of_change, dt) \
|
|
rk3_integrate<step_number>(state_previous, value(state_current), rate_of_change, dt)
|
|
|
|
/*
|
|
template <int step_number>
|
|
static __device__ __forceinline__ AcReal
|
|
rk3_integrate(const int idx, const AcReal out, const int handle,
|
|
const AcRealData& in_cached, const AcReal rate_of_change, const AcReal dt)
|
|
{
|
|
return rk3_integrate_scal<step_number>(out, value(in_cached), rate_of_change, dt);
|
|
}
|
|
|
|
template <int step_number>
|
|
static __device__ __forceinline__ AcReal3
|
|
rk3_integrate(const int idx, const AcReal3 out, const int3& handle,
|
|
const AcReal3Data& in_cached, const AcReal3& rate_of_change, const AcReal dt)
|
|
{
|
|
return (AcReal3) {
|
|
rk3_integrate<step_number>(idx, out, handle.x, in_cached.x, rate_of_change.x, dt),
|
|
rk3_integrate<step_number>(idx, out, handle.y, in_cached.y, rate_of_change.y, dt),
|
|
rk3_integrate<step_number>(idx, out, handle.z, in_cached.z, rate_of_change.z, dt)
|
|
};
|
|
}
|
|
|
|
#define RK3(handle, in_cached, rate_of_change, dt) \
|
|
rk3_integrate<step_number>(idx, buffer.out, handle, in_cached, rate_of_change, dt)
|
|
*/
|
|
|
|
/*
|
|
* =============================================================================
|
|
* Level 1.3 (Kernels)
|
|
* =============================================================================
|
|
*/
|
|
|
|
static __device__ void
|
|
write(AcReal* __restrict__ out[], const int handle, const int idx, const AcReal value)
|
|
{
|
|
out[handle][idx] = value;
|
|
}
|
|
|
|
static __device__ void
|
|
write(AcReal* __restrict__ out[], const int3 vec, const int idx, const AcReal3 value)
|
|
{
|
|
write(out, vec.x, idx, value.x);
|
|
write(out, vec.y, idx, value.y);
|
|
write(out, vec.z, idx, value.z);
|
|
}
|
|
|
|
static __device__ AcReal
|
|
read_out(const int idx, AcReal* __restrict__ field[], const int handle)
|
|
{
|
|
return field[handle][idx];
|
|
}
|
|
|
|
static __device__ AcReal3
|
|
read_out(const int idx, AcReal* __restrict__ field[], const int3 handle)
|
|
{
|
|
return (AcReal3){read_out(idx, field, handle.x), read_out(idx, field, handle.y),
|
|
read_out(idx, field, handle.z)};
|
|
}
|
|
|
|
#define WRITE_OUT(handle, value) (write(buffer.out, handle, idx, value))
|
|
#define READ(handle) (read_data(vertexIdx, buffer.in, handle))
|
|
#define READ_OUT(handle) (read_out(idx, buffer.out, handle))
|
|
|
|
// also write for clarity here also, not for the DSL
|
|
//#define WRITE(HANDLE) (write(idx, ...)) s.t. we don't have to insert boilerplat in the mid of the
|
|
// function
|
|
|
|
#define GEN_KERNEL_PARAM_BOILERPLATE const int3 start, const int3 end, VertexBufferArray buffer
|
|
|
|
#define GEN_KERNEL_BUILTIN_VARIABLES_BOILERPLATE() \
|
|
const int3 vertexIdx = (int3){threadIdx.x + blockIdx.x * blockDim.x + start.x, \
|
|
threadIdx.y + blockIdx.y * blockDim.y + start.y, \
|
|
threadIdx.z + blockIdx.z * blockDim.z + start.z}; \
|
|
const int3 globalVertexIdx = (int3){d_multigpu_offset.x + vertexIdx.x, \
|
|
d_multigpu_offset.y + vertexIdx.y, \
|
|
d_multigpu_offset.z + vertexIdx.z}; \
|
|
if (vertexIdx.x >= end.x || vertexIdx.y >= end.y || vertexIdx.z >= end.z) \
|
|
return; \
|
|
\
|
|
assert(vertexIdx.x < DCONST_INT(AC_nx_max) && vertexIdx.y < DCONST_INT(AC_ny_max) && \
|
|
vertexIdx.z < DCONST_INT(AC_nz_max)); \
|
|
\
|
|
assert(vertexIdx.x >= DCONST_INT(AC_nx_min) && vertexIdx.y >= DCONST_INT(AC_ny_min) && \
|
|
vertexIdx.z >= DCONST_INT(AC_nz_min)); \
|
|
\
|
|
const int idx = IDX(vertexIdx.x, vertexIdx.y, vertexIdx.z);
|
|
|
|
#include "stencil_process.cuh"
|
|
|
|
/*
|
|
* =============================================================================
|
|
* Level 2 (Host calls)
|
|
* =============================================================================
|
|
*/
|
|
|
|
static AcReal
|
|
randf(void)
|
|
{
|
|
return AcReal(rand()) / AcReal(RAND_MAX);
|
|
}
|
|
|
|
AcResult
|
|
rk3_step_async(const cudaStream_t stream, const int& step_number, const int3& start,
|
|
const int3& end, const AcReal dt, VertexBufferArray* buffer)
|
|
{
|
|
const dim3 tpb(32, 1, 4);
|
|
/////////////////// Forcing
|
|
#if LFORCING
|
|
const AcReal ff_scale = AcReal(.2);
|
|
static AcReal3 ff = ff_scale * (AcReal3){1, 0, 0};
|
|
const AcReal radians = randf() * AcReal(2 * M_PI) / 360 / 8;
|
|
const AcMatrix rotz = create_rotz(radians);
|
|
ff = mul(rotz, ff);
|
|
cudaMemcpyToSymbolAsync(forcing_vec, &ff, sizeof(ff), 0, cudaMemcpyHostToDevice, stream);
|
|
|
|
const AcReal ff_phi = AcReal(M_PI); // AcReal(2 * M_PI) * randf();
|
|
cudaMemcpyToSymbolAsync(forcing_phi, &ff_phi, sizeof(ff_phi), 0, cudaMemcpyHostToDevice,
|
|
stream);
|
|
#endif // LFORCING
|
|
//////////////////////////
|
|
|
|
const int nx = end.x - start.x;
|
|
const int ny = end.y - start.y;
|
|
const int nz = end.z - start.z;
|
|
|
|
const dim3 bpg((unsigned int)ceil(nx / AcReal(tpb.x)), (unsigned int)ceil(ny / AcReal(tpb.y)),
|
|
(unsigned int)ceil(nz / AcReal(tpb.z)));
|
|
|
|
if (step_number == 0)
|
|
solve<0><<<bpg, tpb, 0, stream>>>(start, end, *buffer, dt);
|
|
else if (step_number == 1)
|
|
solve<1><<<bpg, tpb, 0, stream>>>(start, end, *buffer, dt);
|
|
else
|
|
solve<2><<<bpg, tpb, 0, stream>>>(start, end, *buffer, dt);
|
|
|
|
ERRCHK_CUDA_KERNEL();
|
|
return AC_SUCCESS;
|
|
}
|
|
|
|
////////////////REDUCE///////////////////////////
|
|
#include "src/core/math_utils.h" // is_power_of_two
|
|
|
|
/*
|
|
Reduction steps:
|
|
1 of 3: Compute the initial value (a, a*a or exp(a)*exp(a)) and put the result in scratchpad
|
|
2 of 3: Compute most of the reductions into a single block of data
|
|
3 of 3: After all results have been stored into the final block, reduce the data in the final block
|
|
*/
|
|
|
|
// Function pointer definitions
|
|
typedef AcReal (*FilterFunc)(const AcReal&);
|
|
typedef AcReal (*FilterFuncVec)(const AcReal&, const AcReal&, const AcReal&);
|
|
typedef AcReal (*ReduceFunc)(const AcReal&, const AcReal&);
|
|
|
|
// clang-format off
|
|
/* Comparison funcs */
|
|
static __device__ inline AcReal
|
|
dmax(const AcReal& a, const AcReal& b) { return a > b ? a : b; }
|
|
|
|
static __device__ inline AcReal
|
|
dmin(const AcReal& a, const AcReal& b) { return a < b ? a : b; }
|
|
|
|
static __device__ inline AcReal
|
|
dsum(const AcReal& a, const AcReal& b) { return a + b; }
|
|
|
|
/* Function used to determine the values used during reduction */
|
|
static __device__ inline AcReal
|
|
dvalue(const AcReal& a) { return AcReal(a); }
|
|
|
|
static __device__ inline AcReal
|
|
dsquared(const AcReal& a) { return (AcReal)(a*a); }
|
|
|
|
static __device__ inline AcReal
|
|
dexp_squared(const AcReal& a) { return exp(a)*exp(a); }
|
|
|
|
static __device__ inline AcReal
|
|
dlength_vec(const AcReal& a, const AcReal& b, const AcReal& c) { return sqrt(a*a + b*b + c*c); }
|
|
|
|
static __device__ inline AcReal
|
|
dsquared_vec(const AcReal& a, const AcReal& b, const AcReal& c) { return dsquared(a) + dsquared(b) + dsquared(c); }
|
|
|
|
static __device__ inline AcReal
|
|
dexp_squared_vec(const AcReal& a, const AcReal& b, const AcReal& c) { return dexp_squared(a) + dexp_squared(b) + dexp_squared(c); }
|
|
// clang-format on
|
|
|
|
#include <assert.h>
|
|
template <FilterFunc filter>
|
|
__global__ void
|
|
kernel_filter(const __restrict__ AcReal* src, const int3 start, const int3 end, AcReal* dst)
|
|
{
|
|
const int3 src_idx = (int3){start.x + threadIdx.x + blockIdx.x * blockDim.x,
|
|
start.y + threadIdx.y + blockIdx.y * blockDim.y,
|
|
start.z + threadIdx.z + blockIdx.z * blockDim.z};
|
|
|
|
const int nx = end.x - start.x;
|
|
const int ny = end.y - start.y;
|
|
const int nz = end.z - start.z; // MV: Added this because it was undefined
|
|
const int3 dst_idx = (int3){threadIdx.x + blockIdx.x * blockDim.x,
|
|
threadIdx.y + blockIdx.y * blockDim.y,
|
|
threadIdx.z + blockIdx.z * blockDim.z};
|
|
|
|
assert(src_idx.x < DCONST_INT(AC_nx_max) && src_idx.y < DCONST_INT(AC_ny_max) &&
|
|
src_idx.z < DCONST_INT(AC_nz_max));
|
|
assert(dst_idx.x < nx && dst_idx.y < ny && dst_idx.z < nz);
|
|
assert(dst_idx.x + dst_idx.y * nx + dst_idx.z * nx * ny < nx * ny * nz);
|
|
|
|
dst[dst_idx.x + dst_idx.y * nx + dst_idx.z * nx * ny] = filter(src[IDX(src_idx)]);
|
|
}
|
|
|
|
template <FilterFuncVec filter>
|
|
__global__ void
|
|
kernel_filter_vec(const __restrict__ AcReal* src0, const __restrict__ AcReal* src1,
|
|
const __restrict__ AcReal* src2, const int3 start, const int3 end, AcReal* dst)
|
|
{
|
|
const int3 src_idx = (int3){start.x + threadIdx.x + blockIdx.x * blockDim.x,
|
|
start.y + threadIdx.y + blockIdx.y * blockDim.y,
|
|
start.z + threadIdx.z + blockIdx.z * blockDim.z};
|
|
|
|
const int nx = end.x - start.x;
|
|
const int ny = end.y - start.y;
|
|
const int nz = end.z - start.z; // MV: Added this because it was undefined
|
|
const int3 dst_idx = (int3){threadIdx.x + blockIdx.x * blockDim.x,
|
|
threadIdx.y + blockIdx.y * blockDim.y,
|
|
threadIdx.z + blockIdx.z * blockDim.z};
|
|
|
|
assert(src_idx.x < DCONST_INT(AC_nx_max) && src_idx.y < DCONST_INT(AC_ny_max) &&
|
|
src_idx.z < DCONST_INT(AC_nz_max));
|
|
assert(dst_idx.x < nx && dst_idx.y < ny && dst_idx.z < nz);
|
|
assert(dst_idx.x + dst_idx.y * nx + dst_idx.z * nx * ny < nx * ny * nz);
|
|
|
|
dst[dst_idx.x + dst_idx.y * nx + dst_idx.z * nx * ny] = filter(
|
|
src0[IDX(src_idx)], src1[IDX(src_idx)], src2[IDX(src_idx)]);
|
|
}
|
|
|
|
template <ReduceFunc reduce>
|
|
__global__ void
|
|
kernel_reduce(AcReal* scratchpad, const int num_elems)
|
|
{
|
|
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
|
|
|
|
extern __shared__ AcReal smem[];
|
|
if (idx < num_elems) {
|
|
smem[threadIdx.x] = scratchpad[idx];
|
|
}
|
|
else {
|
|
smem[threadIdx.x] = NAN;
|
|
}
|
|
__syncthreads();
|
|
|
|
int offset = blockDim.x / 2;
|
|
assert(offset % 2 == 0);
|
|
while (offset > 0) {
|
|
if (threadIdx.x < offset) {
|
|
smem[threadIdx.x] = reduce(smem[threadIdx.x], smem[threadIdx.x + offset]);
|
|
}
|
|
offset /= 2;
|
|
__syncthreads();
|
|
}
|
|
|
|
if (threadIdx.x == 0) {
|
|
scratchpad[idx] = smem[threadIdx.x];
|
|
}
|
|
}
|
|
|
|
template <ReduceFunc reduce>
|
|
__global__ void
|
|
kernel_reduce_block(const __restrict__ AcReal* scratchpad, const int num_blocks,
|
|
const int block_size, AcReal* result)
|
|
{
|
|
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
|
|
if (idx != 0) {
|
|
return;
|
|
}
|
|
|
|
AcReal res = scratchpad[0];
|
|
for (int i = 1; i < num_blocks; ++i) {
|
|
res = reduce(res, scratchpad[i * block_size]);
|
|
}
|
|
*result = res;
|
|
}
|
|
|
|
AcReal
|
|
reduce_scal(const cudaStream_t stream, const ReductionType rtype, const int3& start,
|
|
const int3& end, const AcReal* vtxbuf, AcReal* scratchpad, AcReal* reduce_result)
|
|
{
|
|
const unsigned nx = end.x - start.x;
|
|
const unsigned ny = end.y - start.y;
|
|
const unsigned nz = end.z - start.z;
|
|
const unsigned num_elems = nx * ny * nz;
|
|
|
|
const dim3 tpb_filter(32, 4, 1);
|
|
const dim3 bpg_filter((unsigned int)ceil(nx / AcReal(tpb_filter.x)),
|
|
(unsigned int)ceil(ny / AcReal(tpb_filter.y)),
|
|
(unsigned int)ceil(nz / AcReal(tpb_filter.z)));
|
|
|
|
const int tpb_reduce = 128;
|
|
const int bpg_reduce = num_elems / tpb_reduce;
|
|
|
|
ERRCHK(nx >= tpb_filter.x);
|
|
ERRCHK(ny >= tpb_filter.y);
|
|
ERRCHK(nz >= tpb_filter.z);
|
|
ERRCHK(tpb_reduce <= num_elems);
|
|
ERRCHK(nx * ny * nz % 2 == 0);
|
|
|
|
// clang-format off
|
|
if (rtype == RTYPE_MAX) {
|
|
kernel_filter<dvalue><<<bpg_filter, tpb_filter, 0, stream>>>(vtxbuf, start, end, scratchpad);
|
|
kernel_reduce<dmax><<<bpg_reduce, tpb_reduce, sizeof(AcReal) * tpb_reduce, stream>>>(scratchpad, num_elems);
|
|
kernel_reduce_block<dmax><<<1, 1, 0, stream>>>(scratchpad, bpg_reduce, tpb_reduce, reduce_result);
|
|
} else if (rtype == RTYPE_MIN) {
|
|
kernel_filter<dvalue><<<bpg_filter, tpb_filter, 0, stream>>>(vtxbuf, start, end, scratchpad);
|
|
kernel_reduce<dmin><<<bpg_reduce, tpb_reduce, sizeof(AcReal) * tpb_reduce, stream>>>(scratchpad, num_elems);
|
|
kernel_reduce_block<dmin><<<1, 1, 0, stream>>>(scratchpad, bpg_reduce, tpb_reduce, reduce_result);
|
|
} else if (rtype == RTYPE_RMS) {
|
|
kernel_filter<dsquared><<<bpg_filter, tpb_filter, 0, stream>>>(vtxbuf, start, end, scratchpad);
|
|
kernel_reduce<dsum><<<bpg_reduce, tpb_reduce, sizeof(AcReal) * tpb_reduce, stream>>>(scratchpad, num_elems);
|
|
kernel_reduce_block<dsum><<<1, 1, 0, stream>>>(scratchpad, bpg_reduce, tpb_reduce, reduce_result);
|
|
} else if (rtype == RTYPE_RMS_EXP) {
|
|
kernel_filter<dexp_squared><<<bpg_filter, tpb_filter, 0, stream>>>(vtxbuf, start, end, scratchpad);
|
|
kernel_reduce<dsum><<<bpg_reduce, tpb_reduce, sizeof(AcReal) * tpb_reduce, stream>>>(scratchpad, num_elems);
|
|
kernel_reduce_block<dsum><<<1, 1, 0, stream>>>(scratchpad, bpg_reduce, tpb_reduce, reduce_result);
|
|
} else {
|
|
ERROR("Unrecognized rtype");
|
|
}
|
|
// clang-format on
|
|
AcReal result;
|
|
cudaMemcpy(&result, reduce_result, sizeof(AcReal), cudaMemcpyDeviceToHost);
|
|
return result;
|
|
}
|
|
|
|
AcReal
|
|
reduce_vec(const cudaStream_t stream, const ReductionType rtype, const int3& start, const int3& end,
|
|
const AcReal* vtxbuf0, const AcReal* vtxbuf1, const AcReal* vtxbuf2, AcReal* scratchpad,
|
|
AcReal* reduce_result)
|
|
{
|
|
const unsigned nx = end.x - start.x;
|
|
const unsigned ny = end.y - start.y;
|
|
const unsigned nz = end.z - start.z;
|
|
const unsigned num_elems = nx * ny * nz;
|
|
|
|
const dim3 tpb_filter(32, 4, 1);
|
|
const dim3 bpg_filter((unsigned int)ceil(nx / AcReal(tpb_filter.x)),
|
|
(unsigned int)ceil(ny / AcReal(tpb_filter.y)),
|
|
(unsigned int)ceil(nz / AcReal(tpb_filter.z)));
|
|
|
|
const int tpb_reduce = 128;
|
|
const int bpg_reduce = num_elems / tpb_reduce;
|
|
|
|
ERRCHK(nx >= tpb_filter.x);
|
|
ERRCHK(ny >= tpb_filter.y);
|
|
ERRCHK(nz >= tpb_filter.z);
|
|
ERRCHK(tpb_reduce <= num_elems);
|
|
ERRCHK(nx * ny * nz % 2 == 0);
|
|
|
|
// clang-format off
|
|
if (rtype == RTYPE_MAX) {
|
|
kernel_filter_vec<dlength_vec><<<bpg_filter, tpb_filter, 0, stream>>>(vtxbuf0, vtxbuf1, vtxbuf2, start, end, scratchpad);
|
|
kernel_reduce<dmax><<<bpg_reduce, tpb_reduce, sizeof(AcReal) * tpb_reduce, stream>>>(scratchpad, num_elems);
|
|
kernel_reduce_block<dmax><<<1, 1, 0, stream>>>(scratchpad, bpg_reduce, tpb_reduce, reduce_result);
|
|
} else if (rtype == RTYPE_MIN) {
|
|
kernel_filter_vec<dlength_vec><<<bpg_filter, tpb_filter, 0, stream>>>(vtxbuf0, vtxbuf1, vtxbuf2, start, end, scratchpad);
|
|
kernel_reduce<dmin><<<bpg_reduce, tpb_reduce, sizeof(AcReal) * tpb_reduce, stream>>>(scratchpad, num_elems);
|
|
kernel_reduce_block<dmin><<<1, 1, 0, stream>>>(scratchpad, bpg_reduce, tpb_reduce, reduce_result);
|
|
} else if (rtype == RTYPE_RMS) {
|
|
kernel_filter_vec<dsquared_vec><<<bpg_filter, tpb_filter, 0, stream>>>(vtxbuf0, vtxbuf1, vtxbuf2, start, end, scratchpad);
|
|
kernel_reduce<dsum><<<bpg_reduce, tpb_reduce, sizeof(AcReal) * tpb_reduce, stream>>>(scratchpad, num_elems);
|
|
kernel_reduce_block<dsum><<<1, 1, 0, stream>>>(scratchpad, bpg_reduce, tpb_reduce, reduce_result);
|
|
} else if (rtype == RTYPE_RMS_EXP) {
|
|
kernel_filter_vec<dexp_squared_vec><<<bpg_filter, tpb_filter, 0, stream>>>(vtxbuf0, vtxbuf1, vtxbuf2, start, end, scratchpad);
|
|
kernel_reduce<dsum><<<bpg_reduce, tpb_reduce, sizeof(AcReal) * tpb_reduce, stream>>>(scratchpad, num_elems);
|
|
kernel_reduce_block<dsum><<<1, 1, 0, stream>>>(scratchpad, bpg_reduce, tpb_reduce, reduce_result);
|
|
} else {
|
|
ERROR("Unrecognized rtype");
|
|
}
|
|
// clang-format on
|
|
AcReal result;
|
|
cudaMemcpy(&result, reduce_result, sizeof(AcReal), cudaMemcpyDeviceToHost);
|
|
return result;
|
|
}
|