mirror of
https://github.com/OPM/opm-simulators.git
synced 2025-02-03 17:00:28 -06:00
641 lines
25 KiB
C++
641 lines
25 KiB
C++
/*
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Copyright 2020 Equinor ASA
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This file is part of the Open Porous Media project (OPM).
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OPM 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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OPM 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 OPM. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include <opm/simulators/linalg/bda/openclKernels.hpp>
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namespace bda
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{
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std::string get_axpy_string() {
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return R"(
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__kernel void axpy(
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__global double *in,
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const double a,
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__global double *out,
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const int N)
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{
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unsigned int NUM_THREADS = get_global_size(0);
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int idx = get_global_id(0);
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while(idx < N){
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out[idx] += a * in[idx];
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idx += NUM_THREADS;
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}
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}
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)";
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}
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// scale vector with scalar
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std::string get_scale_string() {
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return R"(
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__kernel void scale(
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__global double *vec,
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const double a,
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const int N)
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{
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unsigned int NUM_THREADS = get_global_size(0);
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int idx = get_global_id(0);
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while(idx < N){
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vec[idx] *= a;
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idx += NUM_THREADS;
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}
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}
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)";
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}
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// returns partial sums, instead of the final dot product
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std::string get_dot_1_string() {
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return R"(
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__kernel void dot_1(
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__global double *in1,
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__global double *in2,
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__global double *out,
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const unsigned int N,
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__local double *tmp)
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{
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unsigned int tid = get_local_id(0);
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unsigned int bsize = get_local_size(0);
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unsigned int bid = get_global_id(0) / bsize;
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unsigned int i = get_global_id(0);
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unsigned int NUM_THREADS = get_global_size(0);
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double sum = 0.0;
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while(i < N){
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sum += in1[i] * in2[i];
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i += NUM_THREADS;
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}
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tmp[tid] = sum;
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barrier(CLK_LOCAL_MEM_FENCE);
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// do reduction in shared mem
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for(unsigned int s = get_local_size(0) / 2; s > 0; s >>= 1)
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{
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if (tid < s)
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{
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tmp[tid] += tmp[tid + s];
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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}
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// write result for this block to global mem
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if (tid == 0) out[get_group_id(0)] = tmp[0];
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}
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)";
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}
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// returns partial sums, instead of the final norm
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// the square root must be computed on CPU
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std::string get_norm_string() {
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return R"(
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__kernel void norm(
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__global double *in,
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__global double *out,
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const unsigned int N,
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__local double *tmp)
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{
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unsigned int tid = get_local_id(0);
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unsigned int bsize = get_local_size(0);
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unsigned int bid = get_global_id(0) / bsize;
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unsigned int i = get_global_id(0);
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unsigned int NUM_THREADS = get_global_size(0);
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double local_sum = 0.0;
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while(i < N){
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local_sum += in[i] * in[i];
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i += NUM_THREADS;
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}
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tmp[tid] = local_sum;
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barrier(CLK_LOCAL_MEM_FENCE);
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// do reduction in shared mem
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for(unsigned int s = get_local_size(0) / 2; s > 0; s >>= 1)
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{
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if (tid < s)
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{
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tmp[tid] += tmp[tid + s];
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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}
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// write result for this block to global mem
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if (tid == 0) out[get_group_id(0)] = tmp[0];
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}
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)";
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}
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// p = (p - omega * v) * beta + r
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std::string get_custom_string() {
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return R"(
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__kernel void custom(
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__global double *p,
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__global double *v,
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__global double *r,
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const double omega,
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const double beta,
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const int N)
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{
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const unsigned int NUM_THREADS = get_global_size(0);
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unsigned int idx = get_global_id(0);
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while(idx < N){
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double res = p[idx];
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res -= omega * v[idx];
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res *= beta;
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res += r[idx];
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p[idx] = res;
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idx += NUM_THREADS;
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}
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}
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)";
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}
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std::string get_spmv_blocked_string() {
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return R"(
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__kernel void spmv_blocked(
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__global const double *vals,
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__global const int *cols,
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__global const int *rows,
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const int Nb,
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__global const double *x,
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__global double *b,
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const unsigned int block_size,
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__local double *tmp)
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{
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const unsigned int warpsize = 32;
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const unsigned int bsize = get_local_size(0);
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const unsigned int idx_b = get_global_id(0) / bsize;
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const unsigned int idx_t = get_local_id(0);
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unsigned int idx = idx_b * bsize + idx_t;
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const unsigned int bs = block_size;
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const unsigned int num_active_threads = (warpsize/bs/bs)*bs*bs;
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const unsigned int num_blocks_per_warp = warpsize/bs/bs;
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const unsigned int NUM_THREADS = get_global_size(0);
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const unsigned int num_warps_in_grid = NUM_THREADS / warpsize;
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unsigned int target_block_row = idx / warpsize;
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const unsigned int lane = idx_t % warpsize;
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const unsigned int c = (lane / bs) % bs;
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const unsigned int r = lane % bs;
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// for 3x3 blocks:
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// num_active_threads: 27
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// num_blocks_per_warp: 3
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while(target_block_row < Nb){
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unsigned int first_block = rows[target_block_row];
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unsigned int last_block = rows[target_block_row+1];
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unsigned int block = first_block + lane / (bs*bs);
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double local_out = 0.0;
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if(lane < num_active_threads){
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for(; block < last_block; block += num_blocks_per_warp){
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double x_elem = x[cols[block]*bs + c];
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double A_elem = vals[block*bs*bs + c + r*bs];
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local_out += x_elem * A_elem;
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}
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}
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// do reduction in shared mem
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tmp[lane] = local_out;
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barrier(CLK_LOCAL_MEM_FENCE);
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for(unsigned int offset = 3; offset <= 24; offset <<= 1)
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{
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if (lane + offset < warpsize)
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{
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tmp[lane] += tmp[lane + offset];
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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}
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if(lane < bs){
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unsigned int row = target_block_row*bs + lane;
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b[row] = tmp[lane];
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}
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target_block_row += num_warps_in_grid;
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}
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}
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)";
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}
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std::string get_ILU_apply1_string(bool full_matrix) {
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std::string s = R"(
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__kernel void ILU_apply1(
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__global const double *LUvals,
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__global const unsigned int *LUcols,
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__global const unsigned int *LUrows,
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__global const int *diagIndex,
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__global const double *y,
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__global double *x,
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__global const unsigned int *nodesPerColorPrefix,
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const unsigned int color,
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const unsigned int block_size,
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__local double *tmp)
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{
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const unsigned int warpsize = 32;
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const unsigned int bs = block_size;
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const unsigned int idx_t = get_local_id(0);
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const unsigned int num_active_threads = (warpsize/bs/bs)*bs*bs;
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const unsigned int num_blocks_per_warp = warpsize/bs/bs;
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const unsigned int NUM_THREADS = get_global_size(0);
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const unsigned int num_warps_in_grid = NUM_THREADS / warpsize;
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unsigned int idx = get_global_id(0);
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unsigned int target_block_row = idx / warpsize;
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target_block_row += nodesPerColorPrefix[color];
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const unsigned int lane = idx_t % warpsize;
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const unsigned int c = (lane / bs) % bs;
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const unsigned int r = lane % bs;
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while(target_block_row < nodesPerColorPrefix[color+1]){
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const unsigned int first_block = LUrows[target_block_row];
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)";
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if (full_matrix) {
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s += "const unsigned int last_block = diagIndex[target_block_row]; ";
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} else {
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s += "const unsigned int last_block = LUrows[target_block_row+1]; ";
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}
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s += R"(
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unsigned int block = first_block + lane / (bs*bs);
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double local_out = 0.0;
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if(lane < num_active_threads){
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if(lane < bs){
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local_out = y[target_block_row*bs+lane];
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}
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for(; block < last_block; block += num_blocks_per_warp){
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const double x_elem = x[LUcols[block]*bs + c];
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const double A_elem = LUvals[block*bs*bs + c + r*bs];
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local_out -= x_elem * A_elem;
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}
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}
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// do reduction in shared mem
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tmp[lane] = local_out;
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barrier(CLK_LOCAL_MEM_FENCE);
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for(unsigned int offset = 3; offset <= 24; offset <<= 1)
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{
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if (lane + offset < warpsize)
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{
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tmp[lane] += tmp[lane + offset];
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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}
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if(lane < bs){
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const unsigned int row = target_block_row*bs + lane;
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x[row] = tmp[lane];
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}
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target_block_row += num_warps_in_grid;
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}
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}
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)";
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return s;
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}
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std::string get_ILU_apply2_string(bool full_matrix) {
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std::string s = R"(
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__kernel void ILU_apply2(
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__global const double *LUvals,
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__global const int *LUcols,
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__global const int *LUrows,
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__global const int *diagIndex,
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__global const double *invDiagVals,
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__global double *x,
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__global const unsigned int *nodesPerColorPrefix,
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const unsigned int color,
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const unsigned int block_size,
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__local double *tmp)
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{
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const unsigned int warpsize = 32;
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const unsigned int bs = block_size;
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const unsigned int idx_t = get_local_id(0);
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const unsigned int num_active_threads = (warpsize/bs/bs)*bs*bs;
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const unsigned int num_blocks_per_warp = warpsize/bs/bs;
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const unsigned int NUM_THREADS = get_global_size(0);
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const unsigned int num_warps_in_grid = NUM_THREADS / warpsize;
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unsigned int idx_g = get_global_id(0);
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unsigned int target_block_row = idx_g / warpsize;
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target_block_row += nodesPerColorPrefix[color];
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const unsigned int lane = idx_t % warpsize;
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const unsigned int c = (lane / bs) % bs;
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const unsigned int r = lane % bs;
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while(target_block_row < nodesPerColorPrefix[color+1]){
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)";
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if (full_matrix) {
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s += "const unsigned int first_block = diagIndex[target_block_row] + 1; ";
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} else {
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s += "const unsigned int first_block = LUrows[target_block_row]; ";
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}
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s += R"(
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const unsigned int last_block = LUrows[target_block_row+1];
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unsigned int block = first_block + lane / (bs*bs);
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double local_out = 0.0;
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if(lane < num_active_threads){
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if(lane < bs){
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const unsigned int row = target_block_row*bs+lane;
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local_out = x[row];
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}
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for(; block < last_block; block += num_blocks_per_warp){
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const double x_elem = x[LUcols[block]*bs + c];
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const double A_elem = LUvals[block*bs*bs + c + r*bs];
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local_out -= x_elem * A_elem;
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}
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}
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// do reduction in shared mem
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tmp[lane] = local_out;
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barrier(CLK_LOCAL_MEM_FENCE);
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for(unsigned int offset = 3; offset <= 24; offset <<= 1)
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{
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if (lane + offset < warpsize)
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{
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tmp[lane] += tmp[lane + offset];
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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}
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local_out = tmp[lane];
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if(lane < bs){
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tmp[lane + bs*idx_t/warpsize] = local_out;
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double sum = 0.0;
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for(int i = 0; i < bs; ++i){
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sum += invDiagVals[target_block_row*bs*bs + i + lane*bs] * tmp[i + bs*idx_t/warpsize];
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}
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const unsigned int row = target_block_row*bs + lane;
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x[row] = sum;
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}
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target_block_row += num_warps_in_grid;
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}
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}
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)";
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return s;
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}
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std::string get_stdwell_apply_string(bool reorder) {
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std::string kernel_name = reorder ? "stdwell_apply" : "stdwell_apply_no_reorder";
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std::string s = "__kernel void " + kernel_name + R"((
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__global const double *Cnnzs,
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__global const double *Dnnzs,
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__global const double *Bnnzs,
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__global const int *Ccols,
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__global const int *Bcols,
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__global const double *x,
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__global double *y,
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)";
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if (reorder) {
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s += R"(__global const int *toOrder,
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)";
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}
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s += R"(const unsigned int dim,
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const unsigned int dim_wells,
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__global const unsigned int *val_pointers,
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__local double *localSum,
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__local double *z1,
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__local double *z2){
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int wgId = get_group_id(0);
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int wiId = get_local_id(0);
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int valSize = val_pointers[wgId + 1] - val_pointers[wgId];
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int valsPerBlock = dim*dim_wells;
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int numActiveWorkItems = (get_local_size(0)/valsPerBlock)*valsPerBlock;
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int numBlocksPerWarp = get_local_size(0)/valsPerBlock;
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int c = wiId % dim;
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int r = (wiId/dim) % dim_wells;
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double temp;
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barrier(CLK_LOCAL_MEM_FENCE);
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localSum[wiId] = 0;
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if(wiId < numActiveWorkItems){
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int b = wiId/valsPerBlock + val_pointers[wgId];
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while(b < valSize + val_pointers[wgId]){
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)";
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if (reorder) {
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s += "int colIdx = toOrder[Bcols[b]]; ";
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} else {
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s += "int colIdx = Bcols[b]; ";
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}
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s += R"(
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localSum[wiId] += Bnnzs[b*dim*dim_wells + r*dim + c]*x[colIdx*dim + c];
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b += numBlocksPerWarp;
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}
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if(wiId < valsPerBlock){
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localSum[wiId] += localSum[wiId + valsPerBlock];
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}
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b = wiId/valsPerBlock + val_pointers[wgId];
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if(c == 0 && wiId < valsPerBlock){
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for(unsigned int stride = 2; stride > 0; stride >>= 1){
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localSum[wiId] += localSum[wiId + stride];
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}
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z1[r] = localSum[wiId];
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}
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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if(wiId < dim_wells){
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temp = 0.0;
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for(unsigned int i = 0; i < dim_wells; ++i){
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temp += Dnnzs[wgId*dim_wells*dim_wells + wiId*dim_wells + i]*z1[i];
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}
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z2[wiId] = temp;
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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if(wiId < dim*valSize){
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temp = 0.0;
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int bb = wiId/dim + val_pointers[wgId];
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for (unsigned int j = 0; j < dim_wells; ++j){
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temp += Cnnzs[bb*dim*dim_wells + j*dim + c]*z2[j];
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}
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)";
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if (reorder) {
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s += "int colIdx = toOrder[Ccols[bb]]; ";
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} else {
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s += "int colIdx = Ccols[bb]; ";
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}
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s += R"(
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y[colIdx*dim + c] -= temp;
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}
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}
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)";
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return s;
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}
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std::string get_ilu_decomp_string() {
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return R"(
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// a = a - (b * c)
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__kernel void block_mult_sub(__global double *a, __local double *b, __global double *c)
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{
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const unsigned int block_size = 3;
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const unsigned int hwarp_size = 16;
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const unsigned int idx_t = get_local_id(0); // thread id in work group
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const unsigned int thread_id_in_hwarp = idx_t % hwarp_size; // thread id in warp (16 threads)
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if(thread_id_in_hwarp < block_size * block_size){
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const unsigned int row = thread_id_in_hwarp / block_size;
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const unsigned int col = thread_id_in_hwarp % block_size;
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double temp = 0.0;
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for (unsigned int k = 0; k < block_size; k++) {
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temp += b[block_size * row + k] * c[block_size * k + col];
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}
|
|
a[block_size * row + col] -= temp;
|
|
}
|
|
}
|
|
|
|
// c = a * b
|
|
__kernel void block_mult(__global double *a, __global double *b, __local double *c) {
|
|
const unsigned int block_size = 3;
|
|
const unsigned int hwarp_size = 16;
|
|
const unsigned int idx_t = get_local_id(0); // thread id in work group
|
|
const unsigned int thread_id_in_hwarp = idx_t % hwarp_size; // thread id in warp (16 threads)
|
|
if(thread_id_in_hwarp < block_size * block_size){
|
|
const unsigned int row = thread_id_in_hwarp / block_size;
|
|
const unsigned int col = thread_id_in_hwarp % block_size;
|
|
double temp = 0.0;
|
|
for (unsigned int k = 0; k < block_size; k++) {
|
|
temp += a[block_size * row + k] * b[block_size * k + col];
|
|
}
|
|
c[block_size * row + col] = temp;
|
|
}
|
|
}
|
|
|
|
// invert 3x3 matrix
|
|
__kernel void inverter(__global double *matrix, __global double *inverse) {
|
|
const unsigned int block_size = 3;
|
|
const unsigned int bs = block_size; // rename to shorter name
|
|
const unsigned int hwarp_size = 16;
|
|
const unsigned int idx_t = get_local_id(0); // thread id in work group
|
|
const unsigned int thread_id_in_hwarp = idx_t % hwarp_size; // thread id in warp (16 threads)
|
|
if(thread_id_in_hwarp < bs * bs){
|
|
double t4 = matrix[0] * matrix[4];
|
|
double t6 = matrix[0] * matrix[5];
|
|
double t8 = matrix[1] * matrix[3];
|
|
double t10 = matrix[2] * matrix[3];
|
|
double t12 = matrix[1] * matrix[6];
|
|
double t14 = matrix[2] * matrix[6];
|
|
|
|
double det = (t4 * matrix[8] - t6 * matrix[7] - t8 * matrix[8] +
|
|
t10 * matrix[7] + t12 * matrix[5] - t14 * matrix[4]);
|
|
double t17 = 1.0 / det;
|
|
|
|
const unsigned int r = thread_id_in_hwarp / bs;
|
|
const unsigned int c = thread_id_in_hwarp % bs;
|
|
const unsigned int r1 = (r+1) % bs;
|
|
const unsigned int c1 = (c+1) % bs;
|
|
const unsigned int r2 = (r+bs-1) % bs;
|
|
const unsigned int c2 = (c+bs-1) % bs;
|
|
inverse[c*bs+r] = ((matrix[r1*bs+c1] * matrix[r2*bs+c2]) - (matrix[r1*bs+c2] * matrix[r2*bs+c1])) * t17;
|
|
}
|
|
}
|
|
|
|
__kernel void ilu_decomp(const unsigned int firstRow,
|
|
const unsigned int lastRow,
|
|
__global double *LUvals,
|
|
__global const int *LUcols,
|
|
__global const int *LUrows,
|
|
__global double *invDiagVals,
|
|
__global int *diagIndex,
|
|
const unsigned int Nb,
|
|
__local double *pivot){
|
|
|
|
const unsigned int bs = 3;
|
|
const unsigned int hwarp_size = 16;
|
|
const unsigned int work_group_size = get_local_size(0);
|
|
const unsigned int work_group_id = get_group_id(0);
|
|
const unsigned int num_groups = get_num_groups(0);
|
|
const unsigned int hwarps_per_group = work_group_size / hwarp_size;
|
|
const unsigned int thread_id_in_group = get_local_id(0); // thread id in work group
|
|
const unsigned int thread_id_in_hwarp = thread_id_in_group % hwarp_size; // thread id in hwarp (16 threads)
|
|
const unsigned int hwarp_id_in_group = thread_id_in_group / hwarp_size;
|
|
const unsigned int lmem_offset = hwarp_id_in_group * bs * bs; // each workgroup gets some lmem, but the workitems have to share it
|
|
// every workitem in a hwarp has the same lmem_offset
|
|
|
|
// go through all rows
|
|
for (int i = firstRow + work_group_id * hwarps_per_group + hwarp_id_in_group; i < lastRow; i += num_groups * hwarps_per_group)
|
|
{
|
|
int iRowStart = LUrows[i];
|
|
int iRowEnd = LUrows[i + 1];
|
|
|
|
// go through all elements of the row
|
|
for (int ij = iRowStart; ij < iRowEnd; ij++) {
|
|
int j = LUcols[ij];
|
|
|
|
if (j < i) {
|
|
// calculate the pivot of this row
|
|
block_mult(LUvals + ij * bs * bs, invDiagVals + j * bs * bs, pivot + lmem_offset);
|
|
|
|
// copy pivot
|
|
if (thread_id_in_hwarp < bs * bs) {
|
|
LUvals[ij * bs * bs + thread_id_in_hwarp] = pivot[lmem_offset + thread_id_in_hwarp];
|
|
}
|
|
|
|
int jRowEnd = LUrows[j + 1];
|
|
int jk = diagIndex[j] + 1;
|
|
int ik = ij + 1;
|
|
// substract that row scaled by the pivot from this row.
|
|
while (ik < iRowEnd && jk < jRowEnd) {
|
|
if (LUcols[ik] == LUcols[jk]) {
|
|
block_mult_sub(LUvals + ik * bs * bs, pivot + lmem_offset, LUvals + jk * bs * bs);
|
|
ik++;
|
|
jk++;
|
|
} else {
|
|
if (LUcols[ik] < LUcols[jk])
|
|
{ ik++; }
|
|
else
|
|
{ jk++; }
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// store the inverse in the diagonal
|
|
inverter(LUvals + diagIndex[i] * bs * bs, invDiagVals + i * bs * bs);
|
|
|
|
// copy inverse
|
|
if (thread_id_in_hwarp < bs * bs) {
|
|
LUvals[diagIndex[i] * bs * bs + thread_id_in_hwarp] = invDiagVals[i * bs * bs + thread_id_in_hwarp];
|
|
}
|
|
|
|
}
|
|
}
|
|
)";
|
|
}
|
|
|
|
} // end namespace bda
|
|
|