Feature: Add planewave parallization support for BPCG method

docs/advanced/scf/hsolver.md

@@ -4,7 +4,7 @@
 
 Method of explicit solving KS-equation can be chosen by variable "ks_solver" in INPUT file.
 
-When "basis_type = pw", `ks_solver` can be `cg`, `bpcg` or `dav`. The `bpcg` method only supports K-point parallelism currently. The default setting `cg` is recommended, which is band-by-band conjugate gradient diagonalization method. There is a large probability that the use of setting of `dav` , which is block Davidson diagonalization method, can be tried to improve performance.  
+When "basis_type = pw", `ks_solver` can be `cg`, `bpcg` or `dav`. The default setting `cg` is recommended, which is band-by-band conjugate gradient diagonalization method. There is a large probability that the use of setting of `dav` , which is block Davidson diagonalization method, can be tried to improve performance.  
 
 When "basis_type = lcao", `ks_solver` can be `genelpa` or `scalapack_gvx`. The default setting `genelpa` is recommended, which is based on ELPA (EIGENVALUE SOLVERS FOR PETAFLOP APPLICATIONS) (https://elpa.mpcdf.mpg.de/) and the kernel is auto choosed by GENELPA(https://github.com/pplab/GenELPA), usually faster than the setting of "scalapack_gvx", which is based on ScaLAPACK(Scalable Linear Algebra PACKage)  
 

source/module_hsolver/diago_bpcg.cpp

@@ -115,6 +115,8 @@ void DiagoBPCG<T, Device>::orth_cholesky(
                 hsub_out.data<T>(),
                 this->n_band);     //ldc
 
+    Parallel_Reduce::reduce_pool(hsub_out.data<T>(), this->n_band * this->n_band);
+
     // set hsub matrix to lower format;
     ct::kernels::set_matrix<T, ct_Device>()(
         'L', hsub_out.data<T>(), this->n_band);
@@ -184,6 +186,8 @@ void DiagoBPCG<T, Device>::orth_projection(
                 hsub_in.data<T>(),
                 this->n_band);     //ldc
 
+    Parallel_Reduce::reduce_pool(hsub_in.data<T>(), this->n_band * this->n_band);
+
     // set_matrix_op()('L', hsub_in->data<T>(), this->n_band);
     option = ct::EinsumOption(
         /*conj_x=*/false, /*conj_y=*/false, /*alpha=*/-1.0, /*beta=*/1.0, /*Tensor out=*/&grad_out);
@@ -205,6 +209,9 @@ void DiagoBPCG<T, Device>::orth_projection(
                 grad_out.data<T>(),
                 this->n_basis);   //ldc
 
+    // * This type of non inner produce like operation does not need reduce!
+    // Parallel_Reduce::reduce_pool(grad_out.data<T>(), this->n_basis * this->n_band);
+
     return;
 }
 
@@ -216,25 +223,28 @@ void DiagoBPCG<T, Device>::rotate_wf(
 {
     ct::EinsumOption option(
         /*conj_x=*/false, /*conj_y=*/false, /*alpha=*/1.0, /*beta=*/0.0, /*Tensor out=*/&workspace_in);
-    workspace_in = ct::op::einsum("ij,jk->ik", hsub_in, psi_out, option);
+    // workspace_in = ct::op::einsum("ij,jk->ik", hsub_in, psi_out, option);
 
     // this->rotate_wf(hsub_out, psi_out, workspace_in);
     // this->orth_cholesky(this->work, this->psi, this->hpsi, this->hsub);
     // gemm: workspace_in(n_basis x n_band) = psi_out(n_basis x n_band) * hsub_in(n_band x n_band)
-    // gemm_op()(this->ctx,
-    //             'N',
-    //             'N',
-    //             this->n_basis,        //m
-    //             this->n_band,       //n
-    //             this->n_band,       //k
-    //             this->one,          //1.0
-    //             psi_out.data<T>(),
-    //             this->n_basis,      //lda
-    //             hsub_in.data<T>(),
-    //             this->n_band,       //ldb
-    //             this->zero,         //0.0
-    //             workspace_in.data<T>(),
-    //             this->n_basis);     //ldc
+    gemm_op()(this->ctx,
+                'N',
+                'N',
+                this->n_basis,        //m
+                this->n_band,       //n
+                this->n_band,       //k
+                this->one,          //1.0
+                psi_out.data<T>(),
+                this->n_basis,      //lda
+                hsub_in.data<T>(),
+                this->n_band,       //ldb
+                this->zero,         //0.0
+                workspace_in.data<T>(),
+                this->n_basis);     //ldc
+
+    // * This type of non inner produce like operation does not need reduce!
+    // Parallel_Reduce::reduce_pool(workspace_in.data<T>(), this->n_basis * this->n_band);
 
     syncmem_complex_op()(psi_out.template data<T>(), workspace_in.template data<T>(), this->n_band * this->n_basis);
 
@@ -281,6 +291,8 @@ void DiagoBPCG<T, Device>::diag_hsub(
                 hsub_out.data<T>(),
                 this->n_band);      //ldc
 
+    Parallel_Reduce::reduce_pool(hsub_out.data<T>(), this->n_band * this->n_band);
+
     ct::kernels::lapack_dnevd<T, ct_Device>()('V', 'U', hsub_out.data<T>(), this->n_band, eigenvalue_out.data<Real>());
 
     return;

source/module_hsolver/kernels/math_kernel_op.cpp

@@ -24,6 +24,7 @@ struct line_minimize_with_block_op<T, base_device::DEVICE_CPU>
             Real theta = 0.0, cos_theta = 0.0, sin_theta = 0.0;
             auto A = reinterpret_cast<const Real*>(grad_out + band_idx * n_basis_max);
             Real norm = BlasConnector::dot(2 * n_basis, A, 1, A, 1);
+            Parallel_Reduce::reduce_pool(norm);
             norm = 1.0 / sqrt(norm);
             for (int basis_idx = 0; basis_idx < n_basis; basis_idx++)
             {
@@ -34,6 +35,9 @@ struct line_minimize_with_block_op<T, base_device::DEVICE_CPU>
                 epsilo_1 += std::real(grad_out[item] * std::conj(hpsi_out[item]));
                 epsilo_2 += std::real(grad_out[item] * std::conj(hgrad_out[item]));
             }
+            Parallel_Reduce::reduce_pool(epsilo_0);
+            Parallel_Reduce::reduce_pool(epsilo_1);
+            Parallel_Reduce::reduce_pool(epsilo_2);
             theta = 0.5 * std::abs(std::atan(2 * epsilo_1 / (epsilo_0 - epsilo_2)));
             cos_theta = std::cos(theta);
             sin_theta = std::sin(theta);
@@ -71,6 +75,7 @@ struct calc_grad_with_block_op<T, base_device::DEVICE_CPU>
             T grad_1 = {0.0, 0.0};
             auto A = reinterpret_cast<const Real*>(psi_out + band_idx * n_basis_max);
             Real norm = BlasConnector::dot(2 * n_basis, A, 1, A, 1);
+            Parallel_Reduce::reduce_pool(norm);
             norm = 1.0 / sqrt(norm);
             for (int basis_idx = 0; basis_idx < n_basis; basis_idx++)
             {
@@ -79,6 +84,7 @@ struct calc_grad_with_block_op<T, base_device::DEVICE_CPU>
                 hpsi_out[item] *= norm;
                 epsilo += std::real(hpsi_out[item] * std::conj(psi_out[item]));
             }
+            Parallel_Reduce::reduce_pool(epsilo);
             for (int basis_idx = 0; basis_idx < n_basis; basis_idx++)
             {
                 auto item = band_idx * n_basis_max + basis_idx;
@@ -87,6 +93,8 @@ struct calc_grad_with_block_op<T, base_device::DEVICE_CPU>
                 err += grad_2;
                 beta += grad_2 / prec_in[basis_idx]; /// Mark here as we should div the prec?
             }
+            Parallel_Reduce::reduce_pool(err);
+            Parallel_Reduce::reduce_pool(beta);
             for (int basis_idx = 0; basis_idx < n_basis; basis_idx++)
             {
                 auto item = band_idx * n_basis_max + basis_idx;