mirror of
https://github.com/OPM/opm-simulators.git
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Added StandardWell functionality to OpenCL backend
This commit is contained in:
Jose Eduardo Bueno
parent
4b45623333
commit
56c1c0862c
@ -181,7 +181,7 @@ DenseMatrix transposeDenseMatrix(const DenseMatrix& M)
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#else
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const std::string gpu_mode = EWOMS_GET_PARAM(TypeTag, std::string, GpuMode);
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if (gpu_mode.compare("none") != 0) {
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OPM_THROW(std::logic_error,"Error cannot use GPU solver since neither CUDA nor OpenCL was not found by cmake");
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OPM_THROW(std::logic_error,"Error cannot use GPU solver since neither CUDA nor OpenCL were found by cmake");
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}
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#endif
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extractParallelGridInformationToISTL(simulator_.vanguard().grid(), parallelInformation_);
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@ -465,7 +465,8 @@ DenseMatrix transposeDenseMatrix(const DenseMatrix& M)
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#if HAVE_CUDA || HAVE_OPENCL
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bool use_gpu = bdaBridge->getUseGpu();
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if (use_gpu) {
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WellContributions wellContribs;
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const std::string gpu_mode = EWOMS_GET_PARAM(TypeTag, std::string, GpuMode);
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WellContributions wellContribs(gpu_mode);
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if (!useWellConn_) {
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simulator_.problem().wellModel().getWellContributions(wellContribs);
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}
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@ -478,7 +479,13 @@ DenseMatrix transposeDenseMatrix(const DenseMatrix& M)
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// CPU fallback
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use_gpu = bdaBridge->getUseGpu(); // update value, BdaBridge might have disabled cusparseSolver
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if (use_gpu) {
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OpmLog::warning("cusparseSolver did not converge, now trying Dune to solve current linear system...");
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if(gpu_mode.compare("cusparse") == 0){
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OpmLog::warning("cusparseSolver did not converge, now trying Dune to solve current linear system...");
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}
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if(gpu_mode.compare("opencl") == 0){
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OpmLog::warning("openclSolver did not converge, now trying Dune to solve current linear system...");
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}
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}
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// call Dune
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@ -24,79 +24,202 @@
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#include <opm/common/OpmLog/OpmLog.hpp>
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#include <opm/common/ErrorMacros.hpp>
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#include <opm/simulators/linalg/bda/openclKernels.hpp>
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#include "opm/simulators/linalg/bda/WellContributions.hpp"
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namespace Opm
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{
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WellContributions::WellContributions(std::string gpu_mode){
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if(gpu_mode.compare("cusparse") == 0){
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cuda_gpu = true;
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}
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if(gpu_mode.compare("opencl") == 0){
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opencl_gpu = true;
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}
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}
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void WellContributions::alloc()
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{
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if (num_std_wells > 0) {
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#if HAVE_CUDA
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allocStandardWells();
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#else
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OPM_THROW(std::logic_error, "Error cannot allocate on GPU for StandardWells because CUDA was not found by cmake");
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if(cuda_gpu){
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allocStandardWells();
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}
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#endif
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#if HAVE_OPENCL
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if(opencl_gpu){
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h_Cnnzs_ocl = new double[num_blocks * dim * dim_wells];
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h_Dnnzs_ocl = new double[num_std_wells * dim_wells * dim_wells];
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h_Bnnzs_ocl = new double[num_blocks * dim * dim_wells];
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h_Ccols_ocl = new int[num_blocks];
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h_Bcols_ocl = new int[num_blocks];
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val_pointers = new unsigned int[num_std_wells + 1];
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allocated = true;
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}
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#endif
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#if !HAVE_CUDA && !HAVE_OPENCL
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OPM_THROW(std::logic_error, "Error cannot allocate on GPU because neither CUDA nor OpenCL were found by cmake");
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#endif
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}
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}
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WellContributions::~WellContributions()
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{
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#if HAVE_CUDA
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freeCudaMemory();
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#endif
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if (h_x) {
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delete[] h_x;
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delete[] h_y;
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}
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// delete MultisegmentWellContributions
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for (auto ms : multisegments) {
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delete ms;
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}
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multisegments.clear();
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}
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#if HAVE_CUDA
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if(cuda_gpu){
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freeCudaMemory(); // should come before 'delete[] h_x'
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}
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#endif
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#if HAVE_OPENCL
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void WellContributions::apply(cl::Buffer& d_x, cl::Buffer& d_y) {
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if (h_x_ocl) {
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delete[] h_x_ocl;
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delete[] h_y_ocl;
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}
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if(opencl_gpu){
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if(num_std_wells > 0){
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delete[] h_Cnnzs_ocl;
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delete[] h_Dnnzs_ocl;
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delete[] h_Bnnzs_ocl;
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delete[] h_Ccols_ocl;
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delete[] h_Bcols_ocl;
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delete[] val_pointers;
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}
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}
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#endif
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}
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#if HAVE_OPENCL
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void WellContributions::init(cl::Context *context){
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d_Cnnzs_ocl = cl::Buffer(*context, CL_MEM_READ_WRITE, sizeof(double) * num_blocks * dim * dim_wells);
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d_Dnnzs_ocl = cl::Buffer(*context, CL_MEM_READ_WRITE, sizeof(double) * num_std_wells * dim_wells * dim_wells);
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d_Bnnzs_ocl = cl::Buffer(*context, CL_MEM_READ_WRITE, sizeof(double) * num_blocks * dim * dim_wells);
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d_Ccols_ocl = cl::Buffer(*context, CL_MEM_READ_WRITE, sizeof(int) * num_blocks);
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d_Bcols_ocl = cl::Buffer(*context, CL_MEM_READ_WRITE, sizeof(int) * num_blocks);
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d_val_pointers_ocl = cl::Buffer(*context, CL_MEM_READ_WRITE, sizeof(unsigned int) * (num_std_wells + 1));
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}
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void WellContributions::copyDataToGPU(cl::CommandQueue *queue){
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cl::Event event;
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queue->enqueueWriteBuffer(d_Cnnzs_ocl, CL_TRUE, 0, sizeof(double) * num_blocks * dim * dim_wells, h_Cnnzs_ocl);
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queue->enqueueWriteBuffer(d_Dnnzs_ocl, CL_TRUE, 0, sizeof(double) * num_std_wells * dim_wells * dim_wells, h_Dnnzs_ocl);
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queue->enqueueWriteBuffer(d_Bnnzs_ocl, CL_TRUE, 0, sizeof(double) * num_blocks * dim * dim_wells, h_Bnnzs_ocl);
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queue->enqueueWriteBuffer(d_Ccols_ocl, CL_TRUE, 0, sizeof(int) * num_blocks, h_Ccols_ocl);
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queue->enqueueWriteBuffer(d_Bcols_ocl, CL_TRUE, 0, sizeof(int) * num_blocks, h_Bcols_ocl);
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queue->enqueueWriteBuffer(d_val_pointers_ocl, CL_TRUE, 0, sizeof(unsigned int) * (num_std_wells + 1), val_pointers, nullptr, &event);
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event.wait();
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}
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void WellContributions::applyMSWell(cl::CommandQueue *queue, cl::Buffer& d_x, cl::Buffer& d_y) {
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// apply MultisegmentWells
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if (num_ms_wells > 0) {
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// allocate pinned memory on host if not yet done
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if (h_x == nullptr) {
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h_x = new double[N];
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h_y = new double[N];
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if (h_x_ocl == nullptr) {
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h_x_ocl = new double[N];
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h_y_ocl = new double[N];
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}
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// copy vectors x and y from GPU to CPU
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queue->enqueueReadBuffer(d_x, CL_TRUE, 0, sizeof(double) * N, h_x);
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queue->enqueueReadBuffer(d_y, CL_TRUE, 0, sizeof(double) * N, h_y);
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queue->enqueueReadBuffer(d_x, CL_TRUE, 0, sizeof(double) * N, h_x_ocl);
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queue->enqueueReadBuffer(d_y, CL_TRUE, 0, sizeof(double) * N, h_y_ocl);
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// actually apply MultisegmentWells
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for (MultisegmentWellContribution *well : multisegments) {
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well->apply(h_x, h_y);
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well->apply(h_x_ocl, h_y_ocl);
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}
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// copy vector y from CPU to GPU
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queue->enqueueWriteBuffer(d_y, CL_TRUE, 0, sizeof(double) * N, h_y);
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}
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// apply StandardWells
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if (num_std_wells > 0) {
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OPM_THROW(std::logic_error, "Error StandardWells are not supported by openclSolver");
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queue->enqueueWriteBuffer(d_y, CL_TRUE, 0, sizeof(double) * N, h_y_ocl);
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}
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}
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void WellContributions::applyStdWell(cl::CommandQueue *queue, cl::Buffer& d_x, cl::Buffer& d_y, kernel_type *kernel){
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const unsigned int work_group_size = 32;
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const unsigned int total_work_items = num_std_wells * work_group_size;
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const unsigned int lmem1 = sizeof(double) * work_group_size;
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const unsigned int lmem2 = sizeof(double) * dim_wells;
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cl::Event event;
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event = (*kernel)(cl::EnqueueArgs(*queue, cl::NDRange(total_work_items), cl::NDRange(work_group_size)),
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d_Cnnzs_ocl, d_Dnnzs_ocl, d_Bnnzs_ocl, d_Ccols_ocl, d_Bcols_ocl, d_x, d_y, dim, dim_wells,
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d_val_pointers_ocl, cl::Local(lmem1), cl::Local(lmem2), cl::Local(lmem2));
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event.wait();
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}
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void WellContributions::apply(cl::CommandQueue *queue, cl::Buffer& d_x, cl::Buffer& d_y, kernel_type *kernel){
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if(num_std_wells > 0){
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applyStdWell(queue, d_x, d_y, kernel);
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}
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if(num_ms_wells > 0){
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applyMSWell(queue, d_x, d_y);
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}
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}
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#endif
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void WellContributions::addMatrix([[maybe_unused]] MatrixType type, [[maybe_unused]]int *colIndices, [[maybe_unused]] double *values, [[maybe_unused]] unsigned int val_size)
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void WellContributions::addMatrix([[maybe_unused]] MatrixType type, [[maybe_unused]] int *colIndices, [[maybe_unused]] double *values, [[maybe_unused]] unsigned int val_size)
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{
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if (!allocated) {
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OPM_THROW(std::logic_error, "Error cannot add wellcontribution before allocating memory in WellContributions");
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}
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#if HAVE_CUDA
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if(cuda_gpu){
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addMatrixGpu(type, colIndices, values, val_size);
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#else
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OPM_THROW(std::logic_error, "Error cannot add StandardWell matrix on GPU because CUDA was not found by cmake");
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}
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#endif
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#if HAVE_OPENCL
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if(opencl_gpu){
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switch (type) {
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case MatrixType::C:
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std::copy(colIndices, colIndices + val_size, h_Ccols_ocl + num_blocks_so_far);
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std::copy(values, values + val_size*dim*dim_wells, h_Cnnzs_ocl + num_blocks_so_far*dim*dim_wells);
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break;
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case MatrixType::D:
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std::copy(values, values + dim_wells*dim_wells, h_Dnnzs_ocl + num_std_wells_so_far*dim_wells*dim_wells);
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break;
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case MatrixType::B:
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std::copy(colIndices, colIndices + val_size, h_Bcols_ocl + num_blocks_so_far);
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std::copy(values, values + val_size*dim*dim_wells, h_Bnnzs_ocl + num_blocks_so_far*dim*dim_wells);
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val_pointers[num_std_wells_so_far] = num_blocks_so_far;
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if(num_std_wells_so_far == num_std_wells - 1){
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val_pointers[num_std_wells] = num_blocks;
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}
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break;
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default:
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OPM_THROW(std::logic_error, "Error unsupported matrix ID for WellContributions::addMatrix()");
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}
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if (MatrixType::B == type) {
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num_blocks_so_far += val_size;
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num_std_wells_so_far++;
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}
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}
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#endif
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#if !HAVE_CUDA && !HAVE_OPENCL
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OPM_THROW(std::logic_error, "Error cannot add StandardWell matrix on GPU because neither CUDA nor OpenCL were found by cmake");
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#endif
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}
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@ -107,23 +230,23 @@ void WellContributions::setBlockSize(unsigned int dim_, unsigned int dim_wells_)
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dim_wells = dim_wells_;
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}
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void WellContributions::addNumBlocks(unsigned int nnz)
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void WellContributions::addNumBlocks(unsigned int numBlocks)
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{
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if (allocated) {
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OPM_THROW(std::logic_error, "Error cannot add more sizes after allocated in WellContributions");
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}
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num_blocks += nnz;
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num_blocks += numBlocks;
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num_std_wells++;
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}
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void WellContributions::addMultisegmentWellContribution(unsigned int dim_, unsigned int dim_wells_,
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void WellContributions::addMultisegmentWellContribution(unsigned int dim, unsigned int dim_wells,
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unsigned int Nb, unsigned int Mb,
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unsigned int BnumBlocks, std::vector<double> &Bvalues, std::vector<unsigned int> &BcolIndices, std::vector<unsigned int> &BrowPointers,
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unsigned int DnumBlocks, double *Dvalues, int *DcolPointers, int *DrowIndices,
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std::vector<double> &Cvalues)
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{
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this->N = Nb * dim_;
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MultisegmentWellContribution *well = new MultisegmentWellContribution(dim, dim_wells_, Nb, Mb, BnumBlocks, Bvalues, BcolIndices, BrowPointers, DnumBlocks, Dvalues, DcolPointers, DrowIndices, Cvalues);
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this->N = Nb * dim;
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MultisegmentWellContribution *well = new MultisegmentWellContribution(dim, dim_wells, Nb, Mb, BnumBlocks, Bvalues, BcolIndices, BrowPointers, DnumBlocks, Dvalues, DcolPointers, DrowIndices, Cvalues);
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multisegments.emplace_back(well);
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++num_ms_wells;
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}
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@ -138,12 +261,5 @@ void WellContributions::setReordering(int *toOrder_, bool reorder_)
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}
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}
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#if HAVE_OPENCL
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void WellContributions::setOpenCLQueue(cl::CommandQueue *queue_)
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{
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this->queue = queue_;
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}
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#endif
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} //namespace Opm
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@ -57,7 +57,6 @@ class WellContributions
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{
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public:
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/// StandardWell has C, D and B matrices that need to be copied
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enum class MatrixType {
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C,
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@ -78,17 +77,16 @@ private:
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bool allocated = false;
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std::vector<MultisegmentWellContribution*> multisegments;
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int *toOrder = nullptr;
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bool reorder = false;
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bool opencl_gpu = false;
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bool cuda_gpu = false;
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#if HAVE_CUDA
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cudaStream_t stream;
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#endif
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#if HAVE_OPENCL
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cl::CommandQueue *queue = nullptr;
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#endif
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#if HAVE_CUDA
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// data for StandardWells, could remain nullptrs if not used
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// StandardWells are only supported for cusparseSolver now
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double *d_Cnnzs = nullptr;
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double *d_Dnnzs = nullptr;
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double *d_Bnnzs = nullptr;
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@ -97,13 +95,29 @@ private:
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double *d_z1 = nullptr;
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double *d_z2 = nullptr;
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unsigned int *d_val_pointers = nullptr;
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double *h_x = nullptr;
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double *h_y = nullptr;
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#endif
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double *h_x = nullptr, *h_y = nullptr; // CUDA pinned memory for GPU memcpy
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#if HAVE_OPENCL
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double *h_Cnnzs_ocl = nullptr;
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double *h_Dnnzs_ocl = nullptr;
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double *h_Bnnzs_ocl = nullptr;
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int *h_Ccols_ocl = nullptr;
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int *h_Bcols_ocl = nullptr;
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double *h_x_ocl = nullptr;
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double *h_y_ocl = nullptr;
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int *toOrder = nullptr;
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bool reorder = false;
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cl::Buffer d_Cnnzs_ocl, d_Dnnzs_ocl, d_Bnnzs_ocl;
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cl::Buffer d_Ccols_ocl, d_Bcols_ocl, d_val_pointers_ocl;
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typedef cl::make_kernel<cl::Buffer&, cl::Buffer&, cl::Buffer&,
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cl::Buffer&, cl::Buffer&, cl::Buffer&,
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cl::Buffer&, const unsigned int, const unsigned int,
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cl::Buffer&, cl::LocalSpaceArg, cl::LocalSpaceArg, cl::LocalSpaceArg> kernel_type;
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#endif
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#if HAVE_CUDA
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/// Store a matrix in this object, in blocked csr format, can only be called after alloc() is called
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/// \param[in] type indicate if C, D or B is sent
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/// \param[in] colIndices columnindices of blocks in C or B, ignored for D
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@ -116,32 +130,43 @@ private:
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/// Free GPU memory allocated with cuda.
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void freeCudaMemory();
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public:
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/// Set a cudaStream to be used
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/// \param[in] stream the cudaStream that is used to launch the kernel in
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#if HAVE_CUDA
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void setCudaStream(cudaStream_t stream);
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#endif
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/// Create a new WellContributions, implementation is empty
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WellContributions() {};
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#if HAVE_OPENCL
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void applyStdWell(cl::CommandQueue *queue, cl::Buffer& d_x, cl::Buffer& d_y, kernel_type *kernel);
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void applyMSWell(cl::CommandQueue *queue, cl::Buffer& d_x, cl::Buffer& d_y);
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#endif
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/// Destroy a WellContributions, and free memory
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~WellContributions();
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public:
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#if HAVE_CUDA
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/// Set a cudaStream to be used
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/// \param[in] stream the cudaStream that is used to launch the kernel in
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void setCudaStream(cudaStream_t stream);
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/// Apply all Wells in this object
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/// performs y -= (C^T * (D^-1 * (B*x))) for all Wells
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/// \param[in] d_x vector x, must be on GPU
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/// \param[inout] d_y vector y, must be on GPU
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#if HAVE_CUDA
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void apply(double *d_x, double *d_y);
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unsigned int getNumWells(){
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return num_std_wells + num_ms_wells;
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}
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#endif
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#if HAVE_OPENCL
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void apply(cl::Buffer& x, cl::Buffer& y);
|
||||
void init(cl::Context *context);
|
||||
void copyDataToGPU(cl::CommandQueue *queue);
|
||||
void apply(cl::CommandQueue *queue, cl::Buffer& x, cl::Buffer& y, kernel_type *kernel);
|
||||
#endif
|
||||
|
||||
/// Create a new WellContributions
|
||||
WellContributions(std::string gpu_mode);
|
||||
|
||||
/// Destroy a WellContributions, and free memory
|
||||
~WellContributions();
|
||||
|
||||
|
||||
/// Allocate memory for the StandardWells
|
||||
void alloc();
|
||||
|
||||
@ -182,24 +207,11 @@ public:
|
||||
unsigned int DnumBlocks, double *Dvalues, int *DcolPointers, int *DrowIndices,
|
||||
std::vector<double> &Cvalues);
|
||||
|
||||
/// Return the number of wells added to this object
|
||||
/// \return the number of wells added to this object
|
||||
unsigned int getNumWells() {
|
||||
return num_std_wells + num_ms_wells;
|
||||
}
|
||||
|
||||
|
||||
/// If the rows of the matrix are reordered, the columnindices of the matrixdata are incorrect
|
||||
/// Those indices need to be mapped via toOrder
|
||||
/// \param[in] toOrder array with mappings
|
||||
/// \param[in] reorder whether the columnindices need to be reordered or not
|
||||
void setReordering(int *toOrder, bool reorder);
|
||||
|
||||
#if HAVE_OPENCL
|
||||
/// This object copies some cl::Buffers, it requires a CommandQueue to do that
|
||||
/// \param[in] queue the opencl commandqueue to be used
|
||||
void setOpenCLQueue(cl::CommandQueue *queue);
|
||||
#endif
|
||||
};
|
||||
|
||||
} //namespace Opm
|
||||
|
||||
@ -23,7 +23,7 @@
|
||||
namespace bda
|
||||
{
|
||||
|
||||
const char* axpy_s = R"(
|
||||
inline const char* axpy_s = R"(
|
||||
__kernel void axpy(
|
||||
__global double *in,
|
||||
const double a,
|
||||
@ -42,7 +42,7 @@ namespace bda
|
||||
|
||||
|
||||
// returns partial sums, instead of the final dot product
|
||||
const char* dot_1_s = R"(
|
||||
inline const char* dot_1_s = R"(
|
||||
__kernel void dot_1(
|
||||
__global double *in1,
|
||||
__global double *in2,
|
||||
@ -83,7 +83,7 @@ namespace bda
|
||||
|
||||
// returns partial sums, instead of the final norm
|
||||
// the square root must be computed on CPU
|
||||
const char* norm_s = R"(
|
||||
inline const char* norm_s = R"(
|
||||
__kernel void norm(
|
||||
__global double *in,
|
||||
__global double *out,
|
||||
@ -122,7 +122,7 @@ namespace bda
|
||||
|
||||
|
||||
// p = (p - omega * v) * beta + r
|
||||
const char* custom_s = R"(
|
||||
inline const char* custom_s = R"(
|
||||
__kernel void custom(
|
||||
__global double *p,
|
||||
__global double *v,
|
||||
@ -150,7 +150,7 @@ namespace bda
|
||||
// algorithm based on:
|
||||
// Optimization of Block Sparse Matrix-Vector Multiplication on Shared-MemoryParallel Architectures,
|
||||
// Ryan Eberhardt, Mark Hoemmen, 2016, https://doi.org/10.1109/IPDPSW.2016.42
|
||||
const char* spmv_blocked_s = R"(
|
||||
inline const char* spmv_blocked_s = R"(
|
||||
__kernel void spmv_blocked(
|
||||
__global const double *vals,
|
||||
__global const int *cols,
|
||||
@ -220,7 +220,7 @@ namespace bda
|
||||
|
||||
// ILU apply part 1: forward substitution
|
||||
// solves L*x=y where L is a lower triangular sparse blocked matrix
|
||||
const char* ILU_apply1_s = R"(
|
||||
inline const char* ILU_apply1_s = R"(
|
||||
__kernel void ILU_apply1(
|
||||
__global const double *Lvals,
|
||||
__global const unsigned int *Lcols,
|
||||
@ -290,7 +290,7 @@ namespace bda
|
||||
|
||||
// ILU apply part 2: backward substitution
|
||||
// solves U*x=y where L is a lower triangular sparse blocked matrix
|
||||
const char* ILU_apply2_s = R"(
|
||||
inline const char* ILU_apply2_s = R"(
|
||||
__kernel void ILU_apply2(
|
||||
__global const double *Uvals,
|
||||
__global const int *Ucols,
|
||||
@ -366,7 +366,99 @@ namespace bda
|
||||
}
|
||||
)";
|
||||
|
||||
inline const char* add_well_contributions_s = R"(
|
||||
#pragma OPENCL EXTENSION cl_khr_fp64: enable
|
||||
#pragma OPENCL EXTENSION cl_khr_int64_base_atomics: enable
|
||||
|
||||
void atomicAdd(volatile __global double *val, const double delta){
|
||||
union{
|
||||
double f;
|
||||
ulong i;
|
||||
} old;
|
||||
|
||||
union{
|
||||
double f;
|
||||
ulong i;
|
||||
} new;
|
||||
|
||||
do{
|
||||
old.f = *val;
|
||||
new.f = old.f + delta;
|
||||
} while(atom_cmpxchg((volatile __global ulong *)val, old.i, new.i) != old.i);
|
||||
}
|
||||
|
||||
__kernel void add_well_contributions(__global const double *valsC,
|
||||
__global const double *valsD,
|
||||
__global const double *valsB,
|
||||
__global const int *colsC,
|
||||
__global const int *colsB,
|
||||
__global const double *x,
|
||||
__global double *y,
|
||||
const unsigned int blnc,
|
||||
const unsigned int blnr,
|
||||
__global const unsigned int *rowptr,
|
||||
__local double *localSum,
|
||||
__local double *z1,
|
||||
__local double *z2){
|
||||
int wgId = get_group_id(0);
|
||||
int wiId = get_local_id(0);
|
||||
int valSize = rowptr[wgId + 1] - rowptr[wgId];
|
||||
int valsPerBlock = blnc*blnr;
|
||||
int numActiveWorkItems = (32/valsPerBlock)*valsPerBlock;
|
||||
int numBlocksPerWarp = 32/valsPerBlock;
|
||||
int c = wiId % blnc;
|
||||
int r = (wiId/blnc) % blnr;
|
||||
double temp;
|
||||
|
||||
localSum[wiId] = 0;
|
||||
if(wiId < numActiveWorkItems){
|
||||
int b = wiId/valsPerBlock + rowptr[wgId];
|
||||
while(b < valSize + rowptr[wgId]){
|
||||
int colIdx = colsB[b];
|
||||
localSum[wiId] += valsB[b*blnc*blnr + r*blnc + c]*x[colIdx*blnc + c];
|
||||
b += numBlocksPerWarp;
|
||||
}
|
||||
}
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
int stride = valsPerBlock;
|
||||
if(wiId < stride){
|
||||
localSum[wiId] += localSum[wiId + stride];
|
||||
}
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
if(c == 0 && wiId < valsPerBlock){
|
||||
for(stride = 2; stride > 0; stride /= 2){
|
||||
localSum[wiId] += localSum[wiId + stride];
|
||||
}
|
||||
z1[r] = localSum[wiId];
|
||||
}
|
||||
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
|
||||
if(wiId < blnr){
|
||||
temp = 0.0;
|
||||
for(unsigned int i = 0; i < blnr; ++i){
|
||||
temp += valsD[wgId*blnr*blnr + wiId*blnr + i]*z1[i];
|
||||
}
|
||||
z2[wiId] = temp;
|
||||
}
|
||||
|
||||
barrier(CLK_GLOBAL_MEM_FENCE);
|
||||
|
||||
if(wiId < blnc*valSize){
|
||||
temp = 0.0;
|
||||
int bb = wiId/blnc + rowptr[wgId];
|
||||
int colIdx = colsC[bb];
|
||||
for (unsigned int j = 0; j < blnr; ++j){
|
||||
temp += valsC[bb*blnc*blnr + j*blnc + c]*z2[j];
|
||||
}
|
||||
atomicAdd(&y[colIdx*blnc + c], temp);
|
||||
}
|
||||
}
|
||||
)";
|
||||
|
||||
} // end namespace bda
|
||||
|
||||
|
||||
@ -20,6 +20,7 @@
|
||||
#include <config.h>
|
||||
#include <cmath>
|
||||
#include <sstream>
|
||||
#include <iostream>
|
||||
|
||||
#include <opm/common/OpmLog/OpmLog.hpp>
|
||||
#include <opm/common/ErrorMacros.hpp>
|
||||
@ -70,7 +71,7 @@ unsigned int openclSolverBackend<block_size>::ceilDivision(const unsigned int A,
|
||||
template <unsigned int block_size>
|
||||
double openclSolverBackend<block_size>::dot_w(cl::Buffer in1, cl::Buffer in2, cl::Buffer out)
|
||||
{
|
||||
const unsigned int work_group_size = 1024;
|
||||
const unsigned int work_group_size = 256;
|
||||
const unsigned int num_work_groups = ceilDivision(N, work_group_size);
|
||||
const unsigned int total_work_items = num_work_groups * work_group_size;
|
||||
const unsigned int lmem_per_work_group = sizeof(double) * work_group_size;
|
||||
@ -98,7 +99,7 @@ double openclSolverBackend<block_size>::dot_w(cl::Buffer in1, cl::Buffer in2, cl
|
||||
template <unsigned int block_size>
|
||||
double openclSolverBackend<block_size>::norm_w(cl::Buffer in, cl::Buffer out)
|
||||
{
|
||||
const unsigned int work_group_size = 1024;
|
||||
const unsigned int work_group_size = 256;
|
||||
const unsigned int num_work_groups = ceilDivision(N, work_group_size);
|
||||
const unsigned int total_work_items = num_work_groups * work_group_size;
|
||||
const unsigned int lmem_per_work_group = sizeof(double) * work_group_size;
|
||||
@ -179,19 +180,14 @@ void openclSolverBackend<block_size>::spmv_blocked_w(cl::Buffer vals, cl::Buffer
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
template <unsigned int block_size>
|
||||
void openclSolverBackend<block_size>::gpu_pbicgstab(WellContributions& wellContribs, BdaResult& res) {
|
||||
|
||||
float it;
|
||||
double rho, rhop, beta, alpha, omega, tmp1, tmp2;
|
||||
double norm, norm_0;
|
||||
|
||||
Timer t_total, t_prec(false), t_spmv(false), t_well(false), t_rest(false);
|
||||
|
||||
wellContribs.setOpenCLQueue(queue.get());
|
||||
wellContribs.setReordering(toOrder, true);
|
||||
|
||||
// set r to the initial residual
|
||||
// if initial x guess is not 0, must call applyblockedscaleadd(), not implemented
|
||||
//applyblockedscaleadd(-1.0, mat, x, r);
|
||||
@ -212,6 +208,8 @@ void openclSolverBackend<block_size>::gpu_pbicgstab(WellContributions& wellContr
|
||||
queue->enqueueCopyBuffer(d_r, d_p, 0, 0, sizeof(double) * N, nullptr, &event);
|
||||
event.wait();
|
||||
|
||||
wellContribs.setReordering(toOrder, true);
|
||||
|
||||
norm = norm_w(d_r, d_tmp);
|
||||
norm_0 = norm;
|
||||
|
||||
@ -243,11 +241,9 @@ void openclSolverBackend<block_size>::gpu_pbicgstab(WellContributions& wellContr
|
||||
t_spmv.stop();
|
||||
|
||||
// apply wellContributions
|
||||
if (wellContribs.getNumWells() > 0) {
|
||||
t_well.start();
|
||||
wellContribs.apply(d_pw, d_v);
|
||||
t_well.stop();
|
||||
}
|
||||
t_well.start();
|
||||
wellContribs.apply(queue.get(), d_pw, d_v, add_well_contributions_k.get());
|
||||
t_well.stop();
|
||||
|
||||
t_rest.start();
|
||||
tmp1 = dot_w(d_rw, d_v, d_tmp);
|
||||
@ -274,11 +270,9 @@ void openclSolverBackend<block_size>::gpu_pbicgstab(WellContributions& wellContr
|
||||
t_spmv.stop();
|
||||
|
||||
// apply wellContributions
|
||||
if (wellContribs.getNumWells() > 0) {
|
||||
t_well.start();
|
||||
wellContribs.apply(d_s, d_t);
|
||||
t_well.stop();
|
||||
}
|
||||
t_well.start();
|
||||
wellContribs.apply(queue.get(), d_s, d_t, add_well_contributions_k.get());
|
||||
t_well.stop();
|
||||
|
||||
t_rest.start();
|
||||
tmp1 = dot_w(d_t, d_r, d_tmp);
|
||||
@ -314,7 +308,8 @@ void openclSolverBackend<block_size>::gpu_pbicgstab(WellContributions& wellContr
|
||||
}
|
||||
if (verbosity >= 4) {
|
||||
std::ostringstream out;
|
||||
out << "openclSolver::ily_apply: " << t_prec.elapsed() << " s\n";
|
||||
out << "openclSolver::ilu_apply: " << t_prec.elapsed() << " s\n";
|
||||
out << "wellContributions::apply: " << t_well.elapsed() << " s\n";
|
||||
out << "openclSolver::spmv: " << t_spmv.elapsed() << " s\n";
|
||||
out << "openclSolver::rest: " << t_rest.elapsed() << " s\n";
|
||||
out << "openclSolver::total_solve: " << res.elapsed << " s\n";
|
||||
@ -324,7 +319,7 @@ void openclSolverBackend<block_size>::gpu_pbicgstab(WellContributions& wellContr
|
||||
|
||||
|
||||
template <unsigned int block_size>
|
||||
void openclSolverBackend<block_size>::initialize(int N_, int nnz_, int dim, double *vals, int *rows, int *cols) {
|
||||
void openclSolverBackend<block_size>::initialize(int N_, int nnz_, int dim, double *vals, int *rows, int *cols, WellContributions& wellContribs) {
|
||||
this->N = N_;
|
||||
this->nnz = nnz_;
|
||||
this->nnzb = nnz_ / block_size / block_size;
|
||||
@ -466,6 +461,7 @@ void openclSolverBackend<block_size>::initialize(int N_, int nnz_, int dim, doub
|
||||
source.emplace_back(std::make_pair(spmv_blocked_s, strlen(spmv_blocked_s)));
|
||||
source.emplace_back(std::make_pair(ILU_apply1_s, strlen(ILU_apply1_s)));
|
||||
source.emplace_back(std::make_pair(ILU_apply2_s, strlen(ILU_apply2_s)));
|
||||
source.emplace_back(std::make_pair(add_well_contributions_s, strlen(add_well_contributions_s)));
|
||||
cl::Program program_ = cl::Program(*context, source);
|
||||
|
||||
program_.build(devices);
|
||||
@ -481,7 +477,6 @@ void openclSolverBackend<block_size>::initialize(int N_, int nnz_, int dim, doub
|
||||
#if COPY_ROW_BY_ROW
|
||||
vals_contiguous = new double[N];
|
||||
#endif
|
||||
|
||||
mat.reset(new BlockedMatrix<block_size>(Nb, nnzb, vals, cols, rows));
|
||||
|
||||
d_x = cl::Buffer(*context, CL_MEM_READ_WRITE, sizeof(double) * N);
|
||||
@ -500,6 +495,8 @@ void openclSolverBackend<block_size>::initialize(int N_, int nnz_, int dim, doub
|
||||
d_Acols = cl::Buffer(*context, CL_MEM_READ_WRITE, sizeof(int) * nnzb);
|
||||
d_Arows = cl::Buffer(*context, CL_MEM_READ_WRITE, sizeof(int) * (Nb + 1));
|
||||
|
||||
wellContribs.init(context.get());
|
||||
|
||||
// queue.enqueueNDRangeKernel() is a blocking/synchronous call, at least for NVIDIA
|
||||
// cl::make_kernel<> myKernel(); myKernel(args, arg1, arg2); is also blocking
|
||||
|
||||
@ -511,6 +508,7 @@ void openclSolverBackend<block_size>::initialize(int N_, int nnz_, int dim, doub
|
||||
spmv_blocked_k.reset(new cl::make_kernel<cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, cl::Buffer&, cl::Buffer&, const unsigned int, cl::LocalSpaceArg>(cl::Kernel(program_, "spmv_blocked")));
|
||||
ILU_apply1_k.reset(new cl::make_kernel<cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, const unsigned int, cl::LocalSpaceArg>(cl::Kernel(program_, "ILU_apply1")));
|
||||
ILU_apply2_k.reset(new cl::make_kernel<cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, const unsigned int, cl::LocalSpaceArg>(cl::Kernel(program_, "ILU_apply2")));
|
||||
add_well_contributions_k.reset(new cl::make_kernel<cl::Buffer&, cl::Buffer&, cl::Buffer&, cl::Buffer&, cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, const unsigned int, cl::Buffer&, cl::LocalSpaceArg, cl::LocalSpaceArg, cl::LocalSpaceArg>(cl::Kernel(program_, "add_well_contributions")));
|
||||
|
||||
prec->setKernels(ILU_apply1_k.get(), ILU_apply2_k.get());
|
||||
|
||||
@ -541,7 +539,7 @@ void openclSolverBackend<block_size>::finalize() {
|
||||
|
||||
|
||||
template <unsigned int block_size>
|
||||
void openclSolverBackend<block_size>::copy_system_to_gpu() {
|
||||
void openclSolverBackend<block_size>::copy_system_to_gpu(WellContributions& wellContribs) {
|
||||
Timer t;
|
||||
cl::Event event;
|
||||
|
||||
@ -563,6 +561,8 @@ void openclSolverBackend<block_size>::copy_system_to_gpu() {
|
||||
queue->enqueueFillBuffer(d_x, 0, 0, sizeof(double) * N, nullptr, &event);
|
||||
event.wait();
|
||||
|
||||
wellContribs.copyDataToGPU(queue.get());
|
||||
|
||||
if (verbosity > 2) {
|
||||
std::ostringstream out;
|
||||
out << "openclSolver::copy_system_to_gpu(): " << t.stop() << " s";
|
||||
@ -706,7 +706,7 @@ void openclSolverBackend<block_size>::get_result(double *x) {
|
||||
template <unsigned int block_size>
|
||||
SolverStatus openclSolverBackend<block_size>::solve_system(int N_, int nnz_, int dim, double *vals, int *rows, int *cols, double *b, WellContributions& wellContribs, BdaResult &res) {
|
||||
if (initialized == false) {
|
||||
initialize(N_, nnz_, dim, vals, rows, cols);
|
||||
initialize(N_, nnz_, dim, vals, rows, cols, wellContribs);
|
||||
if (analysis_done == false) {
|
||||
if (!analyse_matrix()) {
|
||||
return SolverStatus::BDA_SOLVER_ANALYSIS_FAILED;
|
||||
@ -716,7 +716,7 @@ SolverStatus openclSolverBackend<block_size>::solve_system(int N_, int nnz_, int
|
||||
if (!create_preconditioner()) {
|
||||
return SolverStatus::BDA_SOLVER_CREATE_PRECONDITIONER_FAILED;
|
||||
}
|
||||
copy_system_to_gpu();
|
||||
copy_system_to_gpu(wellContribs);
|
||||
} else {
|
||||
update_system(vals, b);
|
||||
if (!create_preconditioner()) {
|
||||
|
||||
@ -64,6 +64,13 @@ private:
|
||||
cl::Buffer d_tmp; // used as tmp GPU buffer for dot() and norm()
|
||||
double *tmp = nullptr; // used as tmp CPU buffer for dot() and norm()
|
||||
|
||||
//unsigned int num_blocks, dim_, dim_wells, num_std_wells;
|
||||
//unsigned int *h_val_pointers;
|
||||
//int *h_Ccols, *h_Bcols;
|
||||
//double *h_Cnnzs, *h_Dnnzs, *h_Bnnzs;
|
||||
//cl::Buffer d_Cnnzs, d_Dnnzs, d_Bnnzs;
|
||||
//cl::Buffer d_Ccols, d_Bcols, d_val_pointers;
|
||||
|
||||
// shared pointers are also passed to other objects
|
||||
std::shared_ptr<cl::Context> context;
|
||||
std::shared_ptr<cl::CommandQueue> queue;
|
||||
@ -74,6 +81,7 @@ private:
|
||||
std::unique_ptr<cl::make_kernel<cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, cl::Buffer&, cl::Buffer&, const unsigned int, cl::LocalSpaceArg> > spmv_blocked_k;
|
||||
std::shared_ptr<cl::make_kernel<cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, const unsigned int, cl::LocalSpaceArg> > ILU_apply1_k;
|
||||
std::shared_ptr<cl::make_kernel<cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, const unsigned int, cl::LocalSpaceArg> > ILU_apply2_k;
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std::shared_ptr<cl::make_kernel<cl::Buffer&, cl::Buffer&, cl::Buffer&, cl::Buffer&, cl::Buffer&, cl::Buffer&, cl::Buffer&, const unsigned int, const unsigned int, cl::Buffer&, cl::LocalSpaceArg, cl::LocalSpaceArg, cl::LocalSpaceArg> > add_well_contributions_k;
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||||
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Preconditioner *prec = nullptr; // only supported preconditioner is BILU0
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int *toOrder = nullptr, *fromOrder = nullptr; // BILU0 reorders rows of the matrix via these mappings
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@ -127,6 +135,8 @@ private:
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/// \param[out] b output vector
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||||
void spmv_blocked_w(cl::Buffer vals, cl::Buffer cols, cl::Buffer rows, cl::Buffer x, cl::Buffer b);
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||||
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//void add_well_contributions_w(cl::Buffer valsC, cl::Buffer valsD, cl::Buffer valsB, cl::Buffer colsC, cl::Buffer colsB, cl::Buffer x, cl::Buffer y, cl::Buffer val_pointers);
|
||||
|
||||
/// Solve linear system using ilu0-bicgstab
|
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/// \param[in] wellContribs WellContributions, to apply them separately, instead of adding them to matrix A
|
||||
/// \param[inout] res summary of solver result
|
||||
@ -139,13 +149,13 @@ private:
|
||||
/// \param[in] vals array of nonzeroes, each block is stored row-wise and contiguous, contains nnz values
|
||||
/// \param[in] rows array of rowPointers, contains N/dim+1 values
|
||||
/// \param[in] cols array of columnIndices, contains nnz values
|
||||
void initialize(int N, int nnz, int dim, double *vals, int *rows, int *cols);
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||||
void initialize(int N, int nnz, int dim, double *vals, int *rows, int *cols, WellContributions& wellContribs);
|
||||
|
||||
/// Clean memory
|
||||
void finalize();
|
||||
|
||||
/// Copy linear system to GPU
|
||||
void copy_system_to_gpu();
|
||||
void copy_system_to_gpu(WellContributions& wellContribs);
|
||||
|
||||
/// Reorder the linear system so it corresponds with the coloring
|
||||
/// \param[in] vals array of nonzeroes, each block is stored row-wise and contiguous, contains nnz values
|
||||
|
||||
Loading…
Reference in New Issue
Block a user