<html><body><div><div><div>Hi Jose,<br></div><div><br></div><div>Since my matrix is two large, I cannot create the Mat on GPU. So I still want to create and compute the eigenvalues of this matrix on CPU using SLEPc.<br></div><div><br></div><div>Best,<br></div><div class="x-apple-signature" style="white-space: pre-wrap">--------------------
Langtian Liu
Institute for Theorectical Physics, Justus-Liebig-University Giessen
Heinrich-Buff-Ring 16, 35392 Giessen Germany
email: <a href="mailto:langtian.liu@icloud.com">langtian.liu@icloud.com</a>
Tel: (+49)641 99 33342<br></div></div><div><br></div><blockquote type="cite"><div>On Oct 2, 2024, at 11:18 AM, Jose E. Roman <jroman@dsic.upv.es> wrote:<br></div><div><br></div><div><br></div><div><div><div>For the CUDA case you should use MatCreateDenseCUDA() instead of MatCreateDense(). With this you pass a pointer with the data on the GPU memory. But I guess "new cuDoubleComplex[dim*dim]" is allocating on the CPU, you should use cudaMalloc() instead.<br></div><div><br></div><div>Jose<br></div><div><br></div><div><br></div><blockquote type="cite"><div>El 2 oct 2024, a las 10:56, 刘浪天 via petsc-users <petsc-users@mcs.anl.gov> escribió:<br></div><div><br></div><div>Hi all,<br></div><div><br></div><div>I am using the PETSc and SLEPc to solve the Faddeev equation of baryons. I encounter a problem of function MatCreateDense when changing from CPU to CPU-GPU computations.<br></div><div>At first, I write the codes in purely CPU computation in the following way and it works.<br></div><div>```<br></div><div>Eigen::MatrixXcd H_KER;<br></div><div>Eigen::MatrixXcd G0;<br></div><div>printf("\nCompute the propagator matrix.\n");<br></div><div>prop_matrix_nucleon_sc_av(Mn, pp_nodes, cos1_nodes);<br></div><div>printf("\nCompute the propagator matrix done.\n");<br></div><div>printf("\nCompute the kernel matrix.\n");<br></div><div>bse_kernel_nucleon_sc_av(Mn, pp_nodes, pp_weights, cos1_nodes, cos1_weights);<br></div><div>printf("\nCompute the kernel matrix done.\n");<br></div><div>printf("\nCompute the full kernel matrix by multiplying kernel and propagator matrix.\n");<br></div><div>MatrixXcd kernel_temp = H_KER * G0;<br></div><div>printf("\nCompute the full kernel matrix done.\n");<br></div><div><br></div><div>// Solve the eigen system with SLEPc<br></div><div>printf("\nSolve the eigen system in the rest frame.\n");<br></div><div>// Get the size of the Eigen matrix<br></div><div>int nRows = (int) kernel_temp.rows();<br></div><div>int nCols = (int) kernel_temp.cols();<br></div><div>// Create PETSc matrix and share the data of kernel_temp<br></div><div>Mat kernel;<br></div><div>PetscCall(MatCreateDense(PETSC_COMM_WORLD, PETSC_DECIDE, PETSC_DECIDE, nRows, nCols, kernel_temp.data(), &kernel));<br></div><div>PetscCall(MatAssemblyBegin(kernel, MAT_FINAL_ASSEMBLY));<br></div><div>PetscCall(MatAssemblyEnd(kernel, MAT_FINAL_ASSEMBLY));<br></div><div>```<br></div><div>Now I change to compute the propagator and kernel matrices in GPU and then compute the largest eigenvalues in CPU using SLEPc in the ways below.<br></div><div>```<br></div><div>cuDoubleComplex *h_propmat;<br></div><div>cuDoubleComplex *h_kernelmat;<br></div><div>int dim = EIGHT * NP * NZ;<br></div><div>printf("\nCompute the propagator matrix.\n");<br></div><div>prop_matrix_nucleon_sc_av_cuda(Mn, pp_nodes.data(), cos1_nodes.data());<br></div><div>printf("\nCompute the propagator matrix done.\n");<br></div><div>printf("\nCompute the kernel matrix.\n");<br></div><div>kernel_matrix_nucleon_sc_av_cuda(Mn, pp_nodes.data(), pp_weights.data(), cos1_nodes.data(), cos1_weights.data());<br></div><div>printf("\nCompute the kernel matrix done.\n");<br></div><div>printf("\nCompute the full kernel matrix by multiplying kernel and propagator matrix.\n");<br></div><div>// Map the raw arrays to Eigen matrices (column-major order)<br></div><div>auto *h_kernel_temp = new cuDoubleComplex [dim*dim];<br></div><div>matmul_cublas_cuDoubleComplex(h_kernelmat,h_propmat,h_kernel_temp,dim,dim,dim);<br></div><div>printf("\nCompute the full kernel matrix done.\n");<br></div><div><br></div><div>// Solve the eigen system with SLEPc<br></div><div>printf("\nSolve the eigen system in the rest frame.\n");<br></div><div>int nRows = dim;<br></div><div>int nCols = dim;<br></div><div>// Create PETSc matrix and share the data of kernel_temp<br></div><div>Mat kernel;<br></div><div>auto* h_kernel = (std::complex<double>*)(h_kernel_temp);<br></div><div>PetscCall(MatCreateDense(PETSC_COMM_WORLD, PETSC_DECIDE, PETSC_DECIDE, nRows, nCols, h_kernel_temp, &kernel));<br></div><div>PetscCall(MatAssemblyBegin(kernel, MAT_FINAL_ASSEMBLY));<br></div><div>PetscCall(MatAssemblyEnd(kernel, MAT_FINAL_ASSEMBLY));<br></div><div>But in this case, the compiler told me that the MatCreateDense function uses the data pointer as type of "thrust::complex<double>" instead of "std::complex<double>".<br></div><div>I am sure I only configured and install PETSc in purely CPU without GPU and this codes are written in the host function.<br></div><div>Why the function changes its behavior? Did you also meet this problem when writing the cuda codes and how to solve this problem.<br></div><div>I tried to copy the data to a new thrust::complex<double> matrix but this is very time consuming since my matrix is very big. Is there a way to create the Mat from the original data without changing the data type to thrust::complex<double> in the cuda applications? Any response will be appreciated. Thank you!<br></div><div><br></div><div>Best wishes,<br></div><div>Langtian Liu<br></div><div><br></div><div>------<br></div><div>Institute for Theorectical Physics, Justus-Liebig-University Giessen<br></div><div>Heinrich-Buff-Ring 16, 35392 Giessen Germany<br></div></blockquote></div></div></blockquote></div><div><br></div></body></html>