http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/sparse_matrix_operations_solve.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/sparse_matrix_operations_solve.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/sparse_matrix_operations_solve.hpp deleted file mode 100644 index 24bcf96..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/sparse_matrix_operations_solve.hpp +++ /dev/null @@ -1,761 +0,0 @@ -#ifndef VIENNACL_LINALG_CUDA_SPARSE_MATRIX_OPERATIONS_SOLVE_HPP_ -#define VIENNACL_LINALG_CUDA_SPARSE_MATRIX_OPERATIONS_SOLVE_HPP_ - -/* ========================================================================= - Copyright (c) 2010-2016, Institute for Microelectronics, - Institute for Analysis and Scientific Computing, - TU Wien. - Portions of this software are copyright by UChicago Argonne, LLC. - - ----------------- - ViennaCL - The Vienna Computing Library - ----------------- - - Project Head: Karl Rupp [email protected] - - (A list of authors and contributors can be found in the manual) - - License: MIT (X11), see file LICENSE in the base directory -============================================================================= */ - -/** @file viennacl/linalg/cuda/sparse_matrix_operations_solve.hpp - @brief Implementations of direct triangular solvers for sparse matrices using CUDA -*/ - -#include "viennacl/forwards.h" - -namespace viennacl -{ -namespace linalg -{ -namespace cuda -{ -// -// Compressed matrix -// - -// -// non-transposed -// - -template<typename NumericT> -__global__ void csr_unit_lu_forward_kernel( - const unsigned int * row_indices, - const unsigned int * column_indices, - const NumericT * elements, - NumericT * vector, - unsigned int size) -{ - __shared__ unsigned int col_index_buffer[128]; - __shared__ NumericT element_buffer[128]; - __shared__ NumericT vector_buffer[128]; - - unsigned int nnz = row_indices[size]; - unsigned int current_row = 0; - unsigned int row_at_window_start = 0; - NumericT current_vector_entry = vector[0]; - unsigned int loop_end = (nnz / blockDim.x + 1) * blockDim.x; - unsigned int next_row = row_indices[1]; - - for (unsigned int i = threadIdx.x; i < loop_end; i += blockDim.x) - { - //load into shared memory (coalesced access): - if (i < nnz) - { - element_buffer[threadIdx.x] = elements[i]; - unsigned int tmp = column_indices[i]; - col_index_buffer[threadIdx.x] = tmp; - vector_buffer[threadIdx.x] = vector[tmp]; - } - - __syncthreads(); - - //now a single thread does the remaining work in shared memory: - if (threadIdx.x == 0) - { - // traverse through all the loaded data: - for (unsigned int k=0; k<blockDim.x; ++k) - { - if (current_row < size && i+k == next_row) //current row is finished. Write back result - { - vector[current_row] = current_vector_entry; - ++current_row; - if (current_row < size) //load next row's data - { - next_row = row_indices[current_row+1]; - current_vector_entry = vector[current_row]; - } - } - - if (current_row < size && col_index_buffer[k] < current_row) //substitute - { - if (col_index_buffer[k] < row_at_window_start) //use recently computed results - current_vector_entry -= element_buffer[k] * vector_buffer[k]; - else if (col_index_buffer[k] < current_row) //use buffered data - current_vector_entry -= element_buffer[k] * vector[col_index_buffer[k]]; - } - - } // for k - - row_at_window_start = current_row; - } // if (get_local_id(0) == 0) - - __syncthreads(); - } //for i -} - - - -template<typename NumericT> -__global__ void csr_lu_forward_kernel( - const unsigned int * row_indices, - const unsigned int * column_indices, - const NumericT * elements, - NumericT * vector, - unsigned int size) -{ - __shared__ unsigned int col_index_buffer[128]; - __shared__ NumericT element_buffer[128]; - __shared__ NumericT vector_buffer[128]; - - unsigned int nnz = row_indices[size]; - unsigned int current_row = 0; - unsigned int row_at_window_start = 0; - NumericT current_vector_entry = vector[0]; - NumericT diagonal_entry = 0; - unsigned int loop_end = (nnz / blockDim.x + 1) * blockDim.x; - unsigned int next_row = row_indices[1]; - - for (unsigned int i = threadIdx.x; i < loop_end; i += blockDim.x) - { - //load into shared memory (coalesced access): - if (i < nnz) - { - element_buffer[threadIdx.x] = elements[i]; - unsigned int tmp = column_indices[i]; - col_index_buffer[threadIdx.x] = tmp; - vector_buffer[threadIdx.x] = vector[tmp]; - } - - __syncthreads(); - - //now a single thread does the remaining work in shared memory: - if (threadIdx.x == 0) - { - // traverse through all the loaded data: - for (unsigned int k=0; k<blockDim.x; ++k) - { - if (current_row < size && i+k == next_row) //current row is finished. Write back result - { - vector[current_row] = current_vector_entry / diagonal_entry; - ++current_row; - if (current_row < size) //load next row's data - { - next_row = row_indices[current_row+1]; - current_vector_entry = vector[current_row]; - } - } - - if (current_row < size && col_index_buffer[k] < current_row) //substitute - { - if (col_index_buffer[k] < row_at_window_start) //use recently computed results - current_vector_entry -= element_buffer[k] * vector_buffer[k]; - else if (col_index_buffer[k] < current_row) //use buffered data - current_vector_entry -= element_buffer[k] * vector[col_index_buffer[k]]; - } - else if (col_index_buffer[k] == current_row) - diagonal_entry = element_buffer[k]; - - } // for k - - row_at_window_start = current_row; - } // if (get_local_id(0) == 0) - - __syncthreads(); - } //for i -} - - -template<typename NumericT> -__global__ void csr_unit_lu_backward_kernel( - const unsigned int * row_indices, - const unsigned int * column_indices, - const NumericT * elements, - NumericT * vector, - unsigned int size) -{ - __shared__ unsigned int col_index_buffer[128]; - __shared__ NumericT element_buffer[128]; - __shared__ NumericT vector_buffer[128]; - - unsigned int nnz = row_indices[size]; - unsigned int current_row = size-1; - unsigned int row_at_window_start = size-1; - NumericT current_vector_entry = vector[size-1]; - unsigned int loop_end = ( (nnz - 1) / blockDim.x) * blockDim.x; - unsigned int next_row = row_indices[size-1]; - - unsigned int i = loop_end + threadIdx.x; - while (1) - { - //load into shared memory (coalesced access): - if (i < nnz) - { - element_buffer[threadIdx.x] = elements[i]; - unsigned int tmp = column_indices[i]; - col_index_buffer[threadIdx.x] = tmp; - vector_buffer[threadIdx.x] = vector[tmp]; - } - - __syncthreads(); - - //now a single thread does the remaining work in shared memory: - if (threadIdx.x == 0) - { - // traverse through all the loaded data from back to front: - for (unsigned int k2=0; k2<blockDim.x; ++k2) - { - unsigned int k = (blockDim.x - k2) - 1; - - if (i+k >= nnz) - continue; - - if (col_index_buffer[k] > row_at_window_start) //use recently computed results - current_vector_entry -= element_buffer[k] * vector_buffer[k]; - else if (col_index_buffer[k] > current_row) //use buffered data - current_vector_entry -= element_buffer[k] * vector[col_index_buffer[k]]; - - if (i+k == next_row) //current row is finished. Write back result - { - vector[current_row] = current_vector_entry; - if (current_row > 0) //load next row's data - { - --current_row; - next_row = row_indices[current_row]; - current_vector_entry = vector[current_row]; - } - } - - - } // for k - - row_at_window_start = current_row; - } // if (get_local_id(0) == 0) - - __syncthreads(); - - if (i < blockDim.x) - break; - - i -= blockDim.x; - } //for i -} - - - -template<typename NumericT> -__global__ void csr_lu_backward_kernel( - const unsigned int * row_indices, - const unsigned int * column_indices, - const NumericT * elements, - NumericT * vector, - unsigned int size) -{ - __shared__ unsigned int col_index_buffer[128]; - __shared__ NumericT element_buffer[128]; - __shared__ NumericT vector_buffer[128]; - - unsigned int nnz = row_indices[size]; - unsigned int current_row = size-1; - unsigned int row_at_window_start = size-1; - NumericT current_vector_entry = vector[size-1]; - NumericT diagonal_entry; - unsigned int loop_end = ( (nnz - 1) / blockDim.x) * blockDim.x; - unsigned int next_row = row_indices[size-1]; - - unsigned int i = loop_end + threadIdx.x; - while (1) - { - //load into shared memory (coalesced access): - if (i < nnz) - { - element_buffer[threadIdx.x] = elements[i]; - unsigned int tmp = column_indices[i]; - col_index_buffer[threadIdx.x] = tmp; - vector_buffer[threadIdx.x] = vector[tmp]; - } - - __syncthreads(); - - //now a single thread does the remaining work in shared memory: - if (threadIdx.x == 0) - { - // traverse through all the loaded data from back to front: - for (unsigned int k2=0; k2<blockDim.x; ++k2) - { - unsigned int k = (blockDim.x - k2) - 1; - - if (i+k >= nnz) - continue; - - if (col_index_buffer[k] > row_at_window_start) //use recently computed results - current_vector_entry -= element_buffer[k] * vector_buffer[k]; - else if (col_index_buffer[k] > current_row) //use buffered data - current_vector_entry -= element_buffer[k] * vector[col_index_buffer[k]]; - else if (col_index_buffer[k] == current_row) - diagonal_entry = element_buffer[k]; - - if (i+k == next_row) //current row is finished. Write back result - { - vector[current_row] = current_vector_entry / diagonal_entry; - if (current_row > 0) //load next row's data - { - --current_row; - next_row = row_indices[current_row]; - current_vector_entry = vector[current_row]; - } - } - - - } // for k - - row_at_window_start = current_row; - } // if (get_local_id(0) == 0) - - __syncthreads(); - - if (i < blockDim.x) - break; - - i -= blockDim.x; - } //for i -} - - - -// -// transposed -// - - -template<typename NumericT> -__global__ void csr_trans_lu_forward_kernel2( - const unsigned int * row_indices, - const unsigned int * column_indices, - const NumericT * elements, - NumericT * vector, - unsigned int size) -{ - for (unsigned int row = 0; row < size; ++row) - { - NumericT result_entry = vector[row]; - - unsigned int row_start = row_indices[row]; - unsigned int row_stop = row_indices[row + 1]; - for (unsigned int entry_index = row_start + threadIdx.x; entry_index < row_stop; entry_index += blockDim.x) - { - unsigned int col_index = column_indices[entry_index]; - if (col_index > row) - vector[col_index] -= result_entry * elements[entry_index]; - } - - __syncthreads(); - } -} - -template<typename NumericT> -__global__ void csr_trans_unit_lu_forward_kernel( - const unsigned int * row_indices, - const unsigned int * column_indices, - const NumericT * elements, - NumericT * vector, - unsigned int size) -{ - __shared__ unsigned int row_index_lookahead[256]; - __shared__ unsigned int row_index_buffer[256]; - - unsigned int row_index; - unsigned int col_index; - NumericT matrix_entry; - unsigned int nnz = row_indices[size]; - unsigned int row_at_window_start = 0; - unsigned int row_at_window_end = 0; - unsigned int loop_end = ( (nnz - 1) / blockDim.x + 1) * blockDim.x; - - for (unsigned int i = threadIdx.x; i < loop_end; i += blockDim.x) - { - col_index = (i < nnz) ? column_indices[i] : 0; - matrix_entry = (i < nnz) ? elements[i] : 0; - row_index_lookahead[threadIdx.x] = (row_at_window_start + threadIdx.x < size) ? row_indices[row_at_window_start + threadIdx.x] : nnz; - - __syncthreads(); - - if (i < nnz) - { - unsigned int row_index_inc = 0; - while (i >= row_index_lookahead[row_index_inc + 1]) - ++row_index_inc; - row_index = row_at_window_start + row_index_inc; - row_index_buffer[threadIdx.x] = row_index; - } - else - { - row_index = size+1; - row_index_buffer[threadIdx.x] = size - 1; - } - - __syncthreads(); - - row_at_window_start = row_index_buffer[0]; - row_at_window_end = row_index_buffer[blockDim.x - 1]; - - //forward elimination - for (unsigned int row = row_at_window_start; row <= row_at_window_end; ++row) - { - NumericT result_entry = vector[row]; - - if ( (row_index == row) && (col_index > row) ) - vector[col_index] -= result_entry * matrix_entry; - - __syncthreads(); - } - - row_at_window_start = row_at_window_end; - } - -} - -template<typename NumericT> -__global__ void csr_trans_lu_forward_kernel( - const unsigned int * row_indices, - const unsigned int * column_indices, - const NumericT * elements, - const NumericT * diagonal_entries, - NumericT * vector, - unsigned int size) -{ - __shared__ unsigned int row_index_lookahead[256]; - __shared__ unsigned int row_index_buffer[256]; - - unsigned int row_index; - unsigned int col_index; - NumericT matrix_entry; - unsigned int nnz = row_indices[size]; - unsigned int row_at_window_start = 0; - unsigned int row_at_window_end = 0; - unsigned int loop_end = ( (nnz - 1) / blockDim.x + 1) * blockDim.x; - - for (unsigned int i = threadIdx.x; i < loop_end; i += blockDim.x) - { - col_index = (i < nnz) ? column_indices[i] : 0; - matrix_entry = (i < nnz) ? elements[i] : 0; - row_index_lookahead[threadIdx.x] = (row_at_window_start + threadIdx.x < size) ? row_indices[row_at_window_start + threadIdx.x] : nnz; - - __syncthreads(); - - if (i < nnz) - { - unsigned int row_index_inc = 0; - while (i >= row_index_lookahead[row_index_inc + 1]) - ++row_index_inc; - row_index = row_at_window_start + row_index_inc; - row_index_buffer[threadIdx.x] = row_index; - } - else - { - row_index = size+1; - row_index_buffer[threadIdx.x] = size - 1; - } - - __syncthreads(); - - row_at_window_start = row_index_buffer[0]; - row_at_window_end = row_index_buffer[blockDim.x - 1]; - - //forward elimination - for (unsigned int row = row_at_window_start; row <= row_at_window_end; ++row) - { - NumericT result_entry = vector[row] / diagonal_entries[row]; - - if ( (row_index == row) && (col_index > row) ) - vector[col_index] -= result_entry * matrix_entry; - - __syncthreads(); - } - - row_at_window_start = row_at_window_end; - } - - // final step: Divide vector by diagonal entries: - for (unsigned int i = threadIdx.x; i < size; i += blockDim.x) - vector[i] /= diagonal_entries[i]; - -} - - -template<typename NumericT> -__global__ void csr_trans_unit_lu_backward_kernel( - const unsigned int * row_indices, - const unsigned int * column_indices, - const NumericT * elements, - NumericT * vector, - unsigned int size) -{ - __shared__ unsigned int row_index_lookahead[256]; - __shared__ unsigned int row_index_buffer[256]; - - unsigned int row_index; - unsigned int col_index; - NumericT matrix_entry; - unsigned int nnz = row_indices[size]; - unsigned int row_at_window_start = size; - unsigned int row_at_window_end; - unsigned int loop_end = ( (nnz - 1) / blockDim.x + 1) * blockDim.x; - - for (unsigned int i2 = threadIdx.x; i2 < loop_end; i2 += blockDim.x) - { - unsigned int i = (nnz - i2) - 1; - col_index = (i2 < nnz) ? column_indices[i] : 0; - matrix_entry = (i2 < nnz) ? elements[i] : 0; - row_index_lookahead[threadIdx.x] = (row_at_window_start >= threadIdx.x) ? row_indices[row_at_window_start - threadIdx.x] : 0; - - __syncthreads(); - - if (i2 < nnz) - { - unsigned int row_index_dec = 0; - while (row_index_lookahead[row_index_dec] > i) - ++row_index_dec; - row_index = row_at_window_start - row_index_dec; - row_index_buffer[threadIdx.x] = row_index; - } - else - { - row_index = size+1; - row_index_buffer[threadIdx.x] = 0; - } - - __syncthreads(); - - row_at_window_start = row_index_buffer[0]; - row_at_window_end = row_index_buffer[blockDim.x - 1]; - - //backward elimination - for (unsigned int row2 = 0; row2 <= (row_at_window_start - row_at_window_end); ++row2) - { - unsigned int row = row_at_window_start - row2; - NumericT result_entry = vector[row]; - - if ( (row_index == row) && (col_index < row) ) - vector[col_index] -= result_entry * matrix_entry; - - __syncthreads(); - } - - row_at_window_start = row_at_window_end; - } - -} - - - -template<typename NumericT> -__global__ void csr_trans_lu_backward_kernel2( - const unsigned int * row_indices, - const unsigned int * column_indices, - const NumericT * elements, - const NumericT * diagonal_entries, - NumericT * vector, - unsigned int size) -{ - NumericT result_entry = 0; - - //backward elimination, using U and D: - for (unsigned int row2 = 0; row2 < size; ++row2) - { - unsigned int row = (size - row2) - 1; - result_entry = vector[row] / diagonal_entries[row]; - - unsigned int row_start = row_indices[row]; - unsigned int row_stop = row_indices[row + 1]; - for (unsigned int entry_index = row_start + threadIdx.x; entry_index < row_stop; ++entry_index) - { - unsigned int col_index = column_indices[entry_index]; - if (col_index < row) - vector[col_index] -= result_entry * elements[entry_index]; - } - - __syncthreads(); - - if (threadIdx.x == 0) - vector[row] = result_entry; - } -} - - -template<typename NumericT> -__global__ void csr_trans_lu_backward_kernel( - const unsigned int * row_indices, - const unsigned int * column_indices, - const NumericT * elements, - const NumericT * diagonal_entries, - NumericT * vector, - unsigned int size) -{ - __shared__ unsigned int row_index_lookahead[256]; - __shared__ unsigned int row_index_buffer[256]; - - unsigned int row_index; - unsigned int col_index; - NumericT matrix_entry; - unsigned int nnz = row_indices[size]; - unsigned int row_at_window_start = size; - unsigned int row_at_window_end; - unsigned int loop_end = ( (nnz - 1) / blockDim.x + 1) * blockDim.x; - - for (unsigned int i2 = threadIdx.x; i2 < loop_end; i2 += blockDim.x) - { - unsigned int i = (nnz - i2) - 1; - col_index = (i2 < nnz) ? column_indices[i] : 0; - matrix_entry = (i2 < nnz) ? elements[i] : 0; - row_index_lookahead[threadIdx.x] = (row_at_window_start >= threadIdx.x) ? row_indices[row_at_window_start - threadIdx.x] : 0; - - __syncthreads(); - - if (i2 < nnz) - { - unsigned int row_index_dec = 0; - while (row_index_lookahead[row_index_dec] > i) - ++row_index_dec; - row_index = row_at_window_start - row_index_dec; - row_index_buffer[threadIdx.x] = row_index; - } - else - { - row_index = size+1; - row_index_buffer[threadIdx.x] = 0; - } - - __syncthreads(); - - row_at_window_start = row_index_buffer[0]; - row_at_window_end = row_index_buffer[blockDim.x - 1]; - - //backward elimination - for (unsigned int row2 = 0; row2 <= (row_at_window_start - row_at_window_end); ++row2) - { - unsigned int row = row_at_window_start - row2; - NumericT result_entry = vector[row] / diagonal_entries[row]; - - if ( (row_index == row) && (col_index < row) ) - vector[col_index] -= result_entry * matrix_entry; - - __syncthreads(); - } - - row_at_window_start = row_at_window_end; - } - - - // final step: Divide vector by diagonal entries: - for (unsigned int i = threadIdx.x; i < size; i += blockDim.x) - vector[i] /= diagonal_entries[i]; - -} - - -template<typename NumericT> -__global__ void csr_block_trans_unit_lu_forward( - const unsigned int * row_jumper_L, //L part (note that L is transposed in memory) - const unsigned int * column_indices_L, - const NumericT * elements_L, - const unsigned int * block_offsets, - NumericT * result, - unsigned int size) -{ - unsigned int col_start = block_offsets[2*blockIdx.x]; - unsigned int col_stop = block_offsets[2*blockIdx.x+1]; - unsigned int row_start = row_jumper_L[col_start]; - unsigned int row_stop; - NumericT result_entry = 0; - - if (col_start >= col_stop) - return; - - //forward elimination, using L: - for (unsigned int col = col_start; col < col_stop; ++col) - { - result_entry = result[col]; - row_stop = row_jumper_L[col + 1]; - for (unsigned int buffer_index = row_start + threadIdx.x; buffer_index < row_stop; buffer_index += blockDim.x) - result[column_indices_L[buffer_index]] -= result_entry * elements_L[buffer_index]; - row_start = row_stop; //for next iteration (avoid unnecessary loads from GPU RAM) - __syncthreads(); - } - -} - - -template<typename NumericT> -__global__ void csr_block_trans_lu_backward( - const unsigned int * row_jumper_U, //U part (note that U is transposed in memory) - const unsigned int * column_indices_U, - const NumericT * elements_U, - const NumericT * diagonal_U, - const unsigned int * block_offsets, - NumericT * result, - unsigned int size) -{ - unsigned int col_start = block_offsets[2*blockIdx.x]; - unsigned int col_stop = block_offsets[2*blockIdx.x+1]; - unsigned int row_start; - unsigned int row_stop; - NumericT result_entry = 0; - - if (col_start >= col_stop) - return; - - //backward elimination, using U and diagonal_U - for (unsigned int iter = 0; iter < col_stop - col_start; ++iter) - { - unsigned int col = (col_stop - iter) - 1; - result_entry = result[col] / diagonal_U[col]; - row_start = row_jumper_U[col]; - row_stop = row_jumper_U[col + 1]; - for (unsigned int buffer_index = row_start + threadIdx.x; buffer_index < row_stop; buffer_index += blockDim.x) - result[column_indices_U[buffer_index]] -= result_entry * elements_U[buffer_index]; - __syncthreads(); - } - - //divide result vector by diagonal: - for (unsigned int col = col_start + threadIdx.x; col < col_stop; col += blockDim.x) - result[col] /= diagonal_U[col]; -} - - - -// -// Coordinate Matrix -// - - - - -// -// ELL Matrix -// - - - -// -// Hybrid Matrix -// - - - -} // namespace opencl -} //namespace linalg -} //namespace viennacl - - -#endif
http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/spgemm.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/spgemm.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/spgemm.hpp deleted file mode 100644 index 5551cda..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/spgemm.hpp +++ /dev/null @@ -1,793 +0,0 @@ -#ifndef VIENNACL_LINALG_CUDA_SPGEMM_HPP_ -#define VIENNACL_LINALG_CUDA_SPGEMM_HPP_ - -/* ========================================================================= - Copyright (c) 2010-2016, Institute for Microelectronics, - Institute for Analysis and Scientific Computing, - TU Wien. - Portions of this software are copyright by UChicago Argonne, LLC. - - ----------------- - ViennaCL - The Vienna Computing Library - ----------------- - - Project Head: Karl Rupp [email protected] - - (A list of authors and contributors can be found in the manual) - - License: MIT (X11), see file LICENSE in the base directory -============================================================================= */ - -/** @file viennacl/linalg/cuda/sparse_matrix_operations.hpp - @brief Implementations of operations using sparse matrices using CUDA -*/ - -#include <stdexcept> - -#include <thrust/scan.h> -#include <thrust/device_ptr.h> - -#include "viennacl/forwards.h" -#include "viennacl/scalar.hpp" -#include "viennacl/vector.hpp" -#include "viennacl/tools/tools.hpp" -#include "viennacl/linalg/cuda/common.hpp" - -#include "viennacl/tools/timer.hpp" - -#include "viennacl/linalg/cuda/sparse_matrix_operations_solve.hpp" - -namespace viennacl -{ -namespace linalg -{ -namespace cuda -{ - -/** @brief Loads a value from the specified address. With CUDA arch 3.5 and above the value is also stored in global constant memory for later reuse */ -template<typename NumericT> -static inline __device__ NumericT load_and_cache(const NumericT *address) -{ -#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350 - return __ldg(address); -#else - return *address; -#endif -} - - -// -// Stage 1: Obtain upper bound for number of elements per row in C: -// -template<typename IndexT> -__device__ IndexT round_to_next_power_of_2(IndexT val) -{ - if (val > 32) - return 64; // just to indicate that we need to split/factor the matrix! - else if (val > 16) - return 32; - else if (val > 8) - return 16; - else if (val > 4) - return 8; - else if (val > 2) - return 4; - else if (val > 1) - return 2; - else - return 1; -} - -template<typename IndexT> -__global__ void compressed_matrix_gemm_stage_1( - const IndexT * A_row_indices, - const IndexT * A_col_indices, - IndexT A_size1, - const IndexT * B_row_indices, - IndexT *subwarpsize_per_group, - IndexT *max_nnz_row_A_per_group, - IndexT *max_nnz_row_B_per_group) -{ - unsigned int subwarpsize_in_thread = 0; - unsigned int max_nnz_row_A = 0; - unsigned int max_nnz_row_B = 0; - - unsigned int rows_per_group = (A_size1 - 1) / gridDim.x + 1; - unsigned int row_per_group_end = min(A_size1, rows_per_group * (blockIdx.x + 1)); - - for (unsigned int row = rows_per_group * blockIdx.x + threadIdx.x; row < row_per_group_end; row += blockDim.x) - { - unsigned int A_row_start = A_row_indices[row]; - unsigned int A_row_end = A_row_indices[row+1]; - unsigned int row_num = A_row_end - A_row_start; - if (row_num > 32) // too many rows in B need to be merged for a single pass - { - unsigned int subwarp_sqrt = (unsigned int)sqrt(double(row_num)) + 1; - subwarpsize_in_thread = max(subwarp_sqrt, subwarpsize_in_thread); - } - else - subwarpsize_in_thread = max(A_row_end - A_row_start, subwarpsize_in_thread); - max_nnz_row_A = max(max_nnz_row_A, row_num); - for (unsigned int j = A_row_start; j < A_row_end; ++j) - { - unsigned int col = A_col_indices[j]; - unsigned int row_len_B = B_row_indices[col + 1] - B_row_indices[col]; - max_nnz_row_B = max(row_len_B, max_nnz_row_B); - } - } - - // reduction to obtain maximum in thread block - __shared__ unsigned int shared_subwarpsize[256]; - __shared__ unsigned int shared_max_nnz_row_A[256]; - __shared__ unsigned int shared_max_nnz_row_B[256]; - - shared_subwarpsize[threadIdx.x] = subwarpsize_in_thread; - shared_max_nnz_row_A[threadIdx.x] = max_nnz_row_A; - shared_max_nnz_row_B[threadIdx.x] = max_nnz_row_B; - for (unsigned int stride = blockDim.x/2; stride > 0; stride /= 2) - { - __syncthreads(); - if (threadIdx.x < stride) - { - shared_subwarpsize[threadIdx.x] = max( shared_subwarpsize[threadIdx.x], shared_subwarpsize[threadIdx.x + stride]); - shared_max_nnz_row_A[threadIdx.x] = max(shared_max_nnz_row_A[threadIdx.x], shared_max_nnz_row_A[threadIdx.x + stride]); - shared_max_nnz_row_B[threadIdx.x] = max(shared_max_nnz_row_B[threadIdx.x], shared_max_nnz_row_B[threadIdx.x + stride]); - } - } - - if (threadIdx.x == 0) - { - subwarpsize_per_group[blockIdx.x] = round_to_next_power_of_2(shared_subwarpsize[0]); - max_nnz_row_A_per_group[blockIdx.x] = shared_max_nnz_row_A[0]; - max_nnz_row_B_per_group[blockIdx.x] = shared_max_nnz_row_B[0]; - } -} - -// -// Stage 2: Determine sparsity pattern of C -// -inline __device__ unsigned int merge_subwarp_symbolic(unsigned int row_B_start, unsigned int row_B_end, unsigned int const *B_col_indices, unsigned int B_size2, unsigned int subwarpsize) -{ - unsigned int current_front_index = (row_B_start < row_B_end) ? load_and_cache(B_col_indices + row_B_start) : B_size2; - - unsigned int num_nnz = 0; - while (1) - { - // determine current minimum (warp shuffle) - unsigned int min_index = current_front_index; - for (unsigned int i = subwarpsize/2; i >= 1; i /= 2) - min_index = min(min_index, __shfl_xor((int)min_index, (int)i)); - - if (min_index == B_size2) - break; - - // update front: - current_front_index = (current_front_index == min_index) ? ((++row_B_start < row_B_end) ? load_and_cache(B_col_indices + row_B_start) : B_size2) - : current_front_index; - ++num_nnz; - } - - return num_nnz; -} - -inline __device__ unsigned int merge_subwarp_symbolic_double(unsigned int row_B_start, unsigned int row_B_end, unsigned int const *B_col_indices, unsigned int B_size2, - unsigned int *output_array, unsigned int id_in_warp, unsigned int subwarpsize) -{ - unsigned int current_front_index = (row_B_start < row_B_end) ? load_and_cache(B_col_indices + row_B_start) : B_size2; - - unsigned int num_nnz = 0; - unsigned int index_buffer = 0; - unsigned int buffer_size = 0; - while (1) - { - // determine current minimum (warp shuffle) - unsigned int min_index = current_front_index; - for (unsigned int i = subwarpsize/2; i >= 1; i /= 2) - min_index = min(min_index, __shfl_xor((int)min_index, (int)i)); - - if (min_index == B_size2) - break; - - // update front: - current_front_index = (current_front_index == min_index) ? ((++row_B_start < row_B_end) ? load_and_cache(B_col_indices + row_B_start) : B_size2) - : current_front_index; - - // write output - index_buffer = (id_in_warp == buffer_size) ? min_index : index_buffer; - ++buffer_size; - - if (buffer_size == subwarpsize) // register buffer full? - { - output_array[id_in_warp] = index_buffer; - output_array += subwarpsize; - buffer_size = 0; - } - - ++num_nnz; - } - - // write remaining entries from register buffer: - if (id_in_warp < buffer_size) - output_array[id_in_warp] = index_buffer; - - return num_nnz; -} - -template<typename IndexT> -__global__ void compressed_matrix_gemm_stage_2( - const IndexT * A_row_indices, - const IndexT * A_col_indices, - IndexT A_size1, - const IndexT * B_row_indices, - const IndexT * B_col_indices, - IndexT B_size2, - IndexT * C_row_indices, - unsigned int *subwarpsize_array, - unsigned int *max_row_size_A, - unsigned int *max_row_size_B, - unsigned int *scratchpad_offsets, - unsigned int *scratchpad_indices) -{ - unsigned int subwarpsize = subwarpsize_array[blockIdx.x]; - - unsigned int num_warps = blockDim.x / subwarpsize; - unsigned int warp_id = threadIdx.x / subwarpsize; - unsigned int id_in_warp = threadIdx.x % subwarpsize; - - unsigned int scratchpad_rowlength = max_row_size_B[blockIdx.x] * subwarpsize; - unsigned int scratchpad_rows_per_warp = max_row_size_A[blockIdx.x] / subwarpsize + 1; - unsigned int *subwarp_scratchpad_start = scratchpad_indices + scratchpad_offsets[blockIdx.x] + warp_id * scratchpad_rows_per_warp * scratchpad_rowlength; - - unsigned int rows_per_group = (A_size1 - 1) / gridDim.x + 1; - unsigned int row_per_group_end = min(A_size1, rows_per_group * (blockIdx.x + 1)); - - for (unsigned int row = rows_per_group * blockIdx.x + warp_id; row < row_per_group_end; row += num_warps) - { - unsigned int row_A_start = A_row_indices[row]; - unsigned int row_A_end = A_row_indices[row+1]; - - if (row_A_end - row_A_start > subwarpsize) - { - unsigned int final_merge_start = 0; - unsigned int final_merge_end = 0; - - // merge to temporary scratchpad memory: - unsigned int *subwarp_scratchpad = subwarp_scratchpad_start; - unsigned int iter = 0; - while (row_A_end > row_A_start) - { - unsigned int my_row_B = row_A_start + id_in_warp; - unsigned int row_B_index = (my_row_B < row_A_end) ? A_col_indices[my_row_B] : 0; - unsigned int row_B_start = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index) : 0; - unsigned int row_B_end = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index + 1) : 0; - - unsigned int nnz_in_merge = merge_subwarp_symbolic_double(row_B_start, row_B_end, B_col_indices, B_size2, - subwarp_scratchpad, id_in_warp, subwarpsize); - - final_merge_start = (iter == id_in_warp) ? subwarp_scratchpad - scratchpad_indices : final_merge_start; - final_merge_end = (iter == id_in_warp) ? final_merge_start + nnz_in_merge : final_merge_end; - ++iter; - - row_A_start += subwarpsize; - subwarp_scratchpad += scratchpad_rowlength; // write to next row in scratchpad - } - - // final merge: - unsigned int num_nnz = merge_subwarp_symbolic(final_merge_start, final_merge_end, scratchpad_indices, B_size2, subwarpsize); - - if (id_in_warp == 0) - C_row_indices[row] = num_nnz; - } - else - { - // single merge - unsigned int my_row_B = row_A_start + id_in_warp; - unsigned int row_B_index = (my_row_B < row_A_end) ? A_col_indices[my_row_B] : 0; - unsigned int row_B_start = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index) : 0; - unsigned int row_B_end = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index + 1) : 0; - - unsigned int num_nnz = merge_subwarp_symbolic(row_B_start, row_B_end, B_col_indices, B_size2, subwarpsize); - - if (id_in_warp == 0) - C_row_indices[row] = num_nnz; - } - } - -} - - -// -// Stage 3: Fill C with values -// -template<typename NumericT> -__device__ unsigned int merge_subwarp_numeric(NumericT scaling_factor, - unsigned int input_start, unsigned int input_end, const unsigned int *input_indices, const NumericT *input_values, unsigned int invalid_token, - unsigned int *output_indices, NumericT *output_values, - unsigned int id_in_warp, unsigned int subwarpsize) -{ - unsigned int current_front_index = (input_start < input_end) ? load_and_cache(input_indices + input_start) : invalid_token; - NumericT current_front_value = (input_start < input_end) ? load_and_cache(input_values + input_start) : 0; - - unsigned int index_buffer = 0; - NumericT value_buffer = 0; - unsigned int buffer_size = 0; - unsigned int nnz_written = 0; - while (1) - { - // determine current minimum: - unsigned int min_index = current_front_index; - for (unsigned int i = subwarpsize/2; i >= 1; i /= 2) - min_index = min(min_index, __shfl_xor((int)min_index, (int)i)); - - if (min_index == invalid_token) // done - break; - - // compute entry in C: - NumericT output_value = (current_front_index == min_index) ? scaling_factor * current_front_value : 0; - for (unsigned int i = subwarpsize/2; i >= 1; i /= 2) - output_value += __shfl_xor((int)output_value, (int)i); - - // update front: - if (current_front_index == min_index) - { - ++input_start; - current_front_index = (input_start < input_end) ? load_and_cache(input_indices + input_start) : invalid_token; - current_front_value = (input_start < input_end) ? load_and_cache(input_values + input_start) : 0; - } - - // write current front to register buffer: - index_buffer = (id_in_warp == buffer_size) ? min_index : index_buffer; - value_buffer = (id_in_warp == buffer_size) ? output_value : value_buffer; - ++buffer_size; - - // flush register buffer via a coalesced write once full: - if (buffer_size == subwarpsize) - { - output_indices[id_in_warp] = index_buffer; output_indices += subwarpsize; - output_values[id_in_warp] = value_buffer; output_values += subwarpsize; - buffer_size = 0; - } - - ++nnz_written; - } - - // write remaining entries in register buffer to C: - if (id_in_warp < buffer_size) - { - output_indices[id_in_warp] = index_buffer; - output_values[id_in_warp] = value_buffer; - } - - return nnz_written; -} - -template<typename IndexT, typename NumericT> -__global__ void compressed_matrix_gemm_stage_3( - const IndexT * A_row_indices, - const IndexT * A_col_indices, - const NumericT * A_elements, - IndexT A_size1, - const IndexT * B_row_indices, - const IndexT * B_col_indices, - const NumericT * B_elements, - IndexT B_size2, - IndexT const * C_row_indices, - IndexT * C_col_indices, - NumericT * C_elements, - unsigned int *subwarpsize_array, - unsigned int *max_row_size_A, - unsigned int *max_row_size_B, - unsigned int *scratchpad_offsets, - unsigned int *scratchpad_indices, - NumericT *scratchpad_values) -{ - unsigned int subwarpsize = subwarpsize_array[blockIdx.x]; - - unsigned int num_warps = blockDim.x / subwarpsize; - unsigned int warp_id = threadIdx.x / subwarpsize; - unsigned int id_in_warp = threadIdx.x % subwarpsize; - - unsigned int scratchpad_rowlength = max_row_size_B[blockIdx.x] * subwarpsize; - unsigned int scratchpad_rows_per_warp = max_row_size_A[blockIdx.x] / subwarpsize + 1; - unsigned int subwarp_scratchpad_shift = scratchpad_offsets[blockIdx.x] + warp_id * scratchpad_rows_per_warp * scratchpad_rowlength; - - unsigned int rows_per_group = (A_size1 - 1) / gridDim.x + 1; - unsigned int row_per_group_end = min(A_size1, rows_per_group * (blockIdx.x + 1)); - - for (unsigned int row = rows_per_group * blockIdx.x + warp_id; row < row_per_group_end; row += num_warps) - { - unsigned int row_A_start = A_row_indices[row]; - unsigned int row_A_end = A_row_indices[row+1]; - - if (row_A_end - row_A_start > subwarpsize) - { - // first merge stage: - unsigned int final_merge_start = 0; - unsigned int final_merge_end = 0; - unsigned int iter = 0; - unsigned int *scratchpad_indices_ptr = scratchpad_indices + subwarp_scratchpad_shift; - NumericT *scratchpad_values_ptr = scratchpad_values + subwarp_scratchpad_shift; - - while (row_A_start < row_A_end) - { - unsigned int my_row_B = row_A_start + id_in_warp; - unsigned int row_B_index = (my_row_B < row_A_end) ? A_col_indices[my_row_B] : 0; - unsigned int row_B_start = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index) : 0; - unsigned int row_B_end = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index + 1) : 0; - NumericT val_A = (my_row_B < row_A_end) ? A_elements[my_row_B] : 0; - - unsigned int nnz_written = merge_subwarp_numeric(val_A, - row_B_start, row_B_end, B_col_indices, B_elements, B_size2, - scratchpad_indices_ptr, scratchpad_values_ptr, - id_in_warp, subwarpsize); - - if (iter == id_in_warp) - { - final_merge_start = scratchpad_indices_ptr - scratchpad_indices; - final_merge_end = final_merge_start + nnz_written; - } - ++iter; - - row_A_start += subwarpsize; - scratchpad_indices_ptr += scratchpad_rowlength; - scratchpad_values_ptr += scratchpad_rowlength; - } - - // second merge stage: - unsigned int index_in_C = C_row_indices[row]; - merge_subwarp_numeric(NumericT(1), - final_merge_start, final_merge_end, scratchpad_indices, scratchpad_values, B_size2, - C_col_indices + index_in_C, C_elements + index_in_C, - id_in_warp, subwarpsize); - } - else - { - unsigned int my_row_B = row_A_start + id_in_warp; - unsigned int row_B_index = (my_row_B < row_A_end) ? A_col_indices[my_row_B] : 0; - unsigned int row_B_start = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index) : 0; - unsigned int row_B_end = (my_row_B < row_A_end) ? load_and_cache(B_row_indices + row_B_index + 1) : 0; - NumericT val_A = (my_row_B < row_A_end) ? A_elements[my_row_B] : 0; - - unsigned int index_in_C = C_row_indices[row]; - - merge_subwarp_numeric(val_A, - row_B_start, row_B_end, B_col_indices, B_elements, B_size2, - C_col_indices + index_in_C, C_elements + index_in_C, - id_in_warp, subwarpsize); - } - } - -} - - - - -// -// Decomposition kernels: -// -template<typename IndexT> -__global__ void compressed_matrix_gemm_decompose_1( - const IndexT * A_row_indices, - IndexT A_size1, - IndexT max_per_row, - IndexT *chunks_per_row) -{ - for (IndexT i = blockIdx.x * blockDim.x + threadIdx.x; i < A_size1; i += blockDim.x * gridDim.x) - { - IndexT num_entries = A_row_indices[i+1] - A_row_indices[i]; - chunks_per_row[i] = (num_entries < max_per_row) ? 1 : ((num_entries - 1)/ max_per_row + 1); - } -} - - -template<typename IndexT, typename NumericT> -__global__ void compressed_matrix_gemm_A2( - IndexT * A2_row_indices, - IndexT * A2_col_indices, - NumericT * A2_elements, - IndexT A2_size1, - IndexT *new_row_buffer) -{ - for (IndexT i = blockIdx.x * blockDim.x + threadIdx.x; i < A2_size1; i += blockDim.x * gridDim.x) - { - unsigned int index_start = new_row_buffer[i]; - unsigned int index_stop = new_row_buffer[i+1]; - - A2_row_indices[i] = index_start; - - for (IndexT j = index_start; j < index_stop; ++j) - { - A2_col_indices[j] = j; - A2_elements[j] = NumericT(1); - } - } - - // write last entry in row_buffer with global thread 0: - if (threadIdx.x == 0 && blockIdx.x == 0) - A2_row_indices[A2_size1] = new_row_buffer[A2_size1]; -} - -template<typename IndexT, typename NumericT> -__global__ void compressed_matrix_gemm_G1( - IndexT * G1_row_indices, - IndexT * G1_col_indices, - NumericT * G1_elements, - IndexT G1_size1, - IndexT const *A_row_indices, - IndexT const *A_col_indices, - NumericT const *A_elements, - IndexT A_size1, - IndexT A_nnz, - IndexT max_per_row, - IndexT *new_row_buffer) -{ - // Part 1: Copy column indices and entries: - for (IndexT i = blockIdx.x * blockDim.x + threadIdx.x; i < A_nnz; i += blockDim.x * gridDim.x) - { - G1_col_indices[i] = A_col_indices[i]; - G1_elements[i] = A_elements[i]; - } - - // Part 2: Derive new row indicies: - for (IndexT i = blockIdx.x * blockDim.x + threadIdx.x; i < A_size1; i += blockDim.x * gridDim.x) - { - unsigned int old_start = A_row_indices[i]; - unsigned int new_start = new_row_buffer[i]; - unsigned int row_chunks = new_row_buffer[i+1] - new_start; - - for (IndexT j=0; j<row_chunks; ++j) - G1_row_indices[new_start + j] = old_start + j * max_per_row; - } - - // write last entry in row_buffer with global thread 0: - if (threadIdx.x == 0 && blockIdx.x == 0) - G1_row_indices[G1_size1] = A_row_indices[A_size1]; -} - - - -/** @brief Carries out sparse_matrix-sparse_matrix multiplication for CSR matrices -* -* Implementation of the convenience expression C = prod(A, B); -* Based on computing C(i, :) = A(i, :) * B via merging the respective rows of B -* -* @param A Left factor -* @param B Right factor -* @param C Result matrix -*/ -template<class NumericT, unsigned int AlignmentV> -void prod_impl(viennacl::compressed_matrix<NumericT, AlignmentV> const & A, - viennacl::compressed_matrix<NumericT, AlignmentV> const & B, - viennacl::compressed_matrix<NumericT, AlignmentV> & C) -{ - C.resize(A.size1(), B.size2(), false); - - unsigned int blocknum = 256; - unsigned int threadnum = 128; - - viennacl::vector<unsigned int> subwarp_sizes(blocknum, viennacl::traits::context(A)); // upper bound for the nonzeros per row encountered for each work group - viennacl::vector<unsigned int> max_nnz_row_A(blocknum, viennacl::traits::context(A)); // upper bound for the nonzeros per row encountered for each work group - viennacl::vector<unsigned int> max_nnz_row_B(blocknum, viennacl::traits::context(A)); // upper bound for the nonzeros per row encountered for each work group - -#ifdef VIENNACL_WITH_SPGEMM_CUDA_TIMINGS - viennacl::tools::timer timer; -#endif - - // - // Stage 1: Determine upper bound for number of nonzeros - // -#ifdef VIENNACL_WITH_SPGEMM_CUDA_TIMINGS - cudaDeviceSynchronize(); - timer.start(); -#endif - - compressed_matrix_gemm_stage_1<<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()), - viennacl::cuda_arg<unsigned int>(A.handle2()), - static_cast<unsigned int>(A.size1()), - viennacl::cuda_arg<unsigned int>(B.handle1()), - viennacl::cuda_arg(subwarp_sizes), - viennacl::cuda_arg(max_nnz_row_A), - viennacl::cuda_arg(max_nnz_row_B) - ); - VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_stage_1"); -#ifdef VIENNACL_WITH_SPGEMM_CUDA_TIMINGS - cudaDeviceSynchronize(); - std::cout << "Stage 1 device: " << timer.get() << std::endl; - timer.start(); -#endif - - subwarp_sizes.switch_memory_context(viennacl::context(MAIN_MEMORY)); - unsigned int * subwarp_sizes_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(subwarp_sizes.handle()); - - max_nnz_row_A.switch_memory_context(viennacl::context(MAIN_MEMORY)); - unsigned int const * max_nnz_row_A_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(max_nnz_row_A.handle()); - - max_nnz_row_B.switch_memory_context(viennacl::context(MAIN_MEMORY)); - unsigned int const * max_nnz_row_B_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(max_nnz_row_B.handle()); - - //std::cout << "Subwarp sizes: " << subwarp_sizes << std::endl; - - viennacl::vector<unsigned int> scratchpad_offsets(blocknum, viennacl::context(MAIN_MEMORY)); // upper bound for the nonzeros per row encountered for each work group - unsigned int * scratchpad_offsets_ptr = viennacl::linalg::host_based::detail::extract_raw_pointer<unsigned int>(scratchpad_offsets.handle()); - - unsigned int max_subwarp_size = 0; - unsigned int A_max_nnz_per_row = 0; - unsigned int scratchpad_offset = 0; - //std::cout << "Scratchpad offsets: " << std::endl; - for (std::size_t i=0; i<subwarp_sizes.size(); ++i) - { - max_subwarp_size = std::max(max_subwarp_size, subwarp_sizes_ptr[i]); - A_max_nnz_per_row = std::max(A_max_nnz_per_row, max_nnz_row_A_ptr[i]); - - scratchpad_offsets_ptr[i] = scratchpad_offset; - //std::cout << scratchpad_offset << " (with " << (max_nnz_row_A_ptr[i] / subwarp_sizes_ptr[i] + 1) << " warp reloads per group at " << max_nnz_row_A_ptr[i] << " max rows, " - // << upper_bound_nonzeros_per_row_C_ptr[i] << " row length, " - // << (256 / subwarp_sizes_ptr[i]) << " warps per group " << std::endl; - unsigned int max_warp_reloads = max_nnz_row_A_ptr[i] / subwarp_sizes_ptr[i] + 1; - unsigned int max_row_length_after_warp_merge = subwarp_sizes_ptr[i] * max_nnz_row_B_ptr[i]; - unsigned int warps_in_group = threadnum / subwarp_sizes_ptr[i]; - scratchpad_offset += max_warp_reloads - * max_row_length_after_warp_merge - * warps_in_group; - } - //std::cout << "Scratchpad memory for indices: " << scratchpad_offset << " entries (" << scratchpad_offset * sizeof(unsigned int) * 1e-6 << " MB)" << std::endl; - - if (max_subwarp_size > 32) - { - // determine augmented size: - unsigned int max_entries_in_G = 1024; - if (A_max_nnz_per_row <= 512*512) - max_entries_in_G = 512; - if (A_max_nnz_per_row <= 256*256) - max_entries_in_G = 256; - if (A_max_nnz_per_row <= 128*128) - max_entries_in_G = 128; - if (A_max_nnz_per_row <= 64*64) - max_entries_in_G = 64; - - viennacl::vector<unsigned int> exclusive_scan_helper(A.size1() + 1, viennacl::traits::context(A)); - compressed_matrix_gemm_decompose_1<<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()), - static_cast<unsigned int>(A.size1()), - static_cast<unsigned int>(max_entries_in_G), - viennacl::cuda_arg(exclusive_scan_helper) - ); - VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_decompose_1"); - - thrust::exclusive_scan(thrust::device_ptr<unsigned int>(viennacl::cuda_arg(exclusive_scan_helper)), - thrust::device_ptr<unsigned int>(viennacl::cuda_arg(exclusive_scan_helper) + exclusive_scan_helper.size()), - thrust::device_ptr<unsigned int>(viennacl::cuda_arg(exclusive_scan_helper))); - - unsigned int augmented_size = exclusive_scan_helper[A.size1()]; - - // split A = A2 * G1 - viennacl::compressed_matrix<NumericT, AlignmentV> A2(A.size1(), augmented_size, augmented_size, viennacl::traits::context(A)); - viennacl::compressed_matrix<NumericT, AlignmentV> G1(augmented_size, A.size2(), A.nnz(), viennacl::traits::context(A)); - - // fill A2: - compressed_matrix_gemm_A2<<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A2.handle1()), - viennacl::cuda_arg<unsigned int>(A2.handle2()), - viennacl::cuda_arg<NumericT>(A2.handle()), - static_cast<unsigned int>(A2.size1()), - viennacl::cuda_arg(exclusive_scan_helper) - ); - VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_A2"); - - // fill G1: - compressed_matrix_gemm_G1<<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(G1.handle1()), - viennacl::cuda_arg<unsigned int>(G1.handle2()), - viennacl::cuda_arg<NumericT>(G1.handle()), - static_cast<unsigned int>(G1.size1()), - viennacl::cuda_arg<unsigned int>(A.handle1()), - viennacl::cuda_arg<unsigned int>(A.handle2()), - viennacl::cuda_arg<NumericT>(A.handle()), - static_cast<unsigned int>(A.size1()), - static_cast<unsigned int>(A.nnz()), - static_cast<unsigned int>(max_entries_in_G), - viennacl::cuda_arg(exclusive_scan_helper) - ); - VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_G1"); - - // compute tmp = G1 * B; - // C = A2 * tmp; - viennacl::compressed_matrix<NumericT, AlignmentV> tmp(G1.size1(), B.size2(), 0, viennacl::traits::context(A)); - prod_impl(G1, B, tmp); // this runs a standard RMerge without decomposition of G1 - prod_impl(A2, tmp, C); // this may split A2 again - return; - } - - subwarp_sizes.switch_memory_context(viennacl::traits::context(A)); - max_nnz_row_A.switch_memory_context(viennacl::traits::context(A)); - max_nnz_row_B.switch_memory_context(viennacl::traits::context(A)); - scratchpad_offsets.switch_memory_context(viennacl::traits::context(A)); - - viennacl::vector<unsigned int> scratchpad_indices(scratchpad_offset, viennacl::traits::context(A)); // upper bound for the nonzeros per row encountered for each work group - -#ifdef VIENNACL_WITH_SPGEMM_CUDA_TIMINGS - std::cout << "Intermediate host stage: " << timer.get() << std::endl; - timer.start(); -#endif - - // - // Stage 2: Determine pattern of C - // - - compressed_matrix_gemm_stage_2<<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()), - viennacl::cuda_arg<unsigned int>(A.handle2()), - static_cast<unsigned int>(A.size1()), - viennacl::cuda_arg<unsigned int>(B.handle1()), - viennacl::cuda_arg<unsigned int>(B.handle2()), - static_cast<unsigned int>(B.size2()), - viennacl::cuda_arg<unsigned int>(C.handle1()), - viennacl::cuda_arg(subwarp_sizes), - viennacl::cuda_arg(max_nnz_row_A), - viennacl::cuda_arg(max_nnz_row_B), - viennacl::cuda_arg(scratchpad_offsets), - viennacl::cuda_arg(scratchpad_indices) - ); - VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_stage_2"); -#ifdef VIENNACL_WITH_SPGEMM_CUDA_TIMINGS - cudaDeviceSynchronize(); - std::cout << "Stage 2: " << timer.get() << std::endl; - timer.start(); -#endif - - - // exclusive scan on C.handle1(), ultimately allowing to allocate remaining memory for C - viennacl::backend::typesafe_host_array<unsigned int> row_buffer(C.handle1(), C.size1() + 1); - viennacl::backend::memory_read(C.handle1(), 0, row_buffer.raw_size(), row_buffer.get()); - unsigned int current_offset = 0; - for (std::size_t i=0; i<C.size1(); ++i) - { - unsigned int tmp = row_buffer[i]; - row_buffer.set(i, current_offset); - current_offset += tmp; - } - row_buffer.set(C.size1(), current_offset); - viennacl::backend::memory_write(C.handle1(), 0, row_buffer.raw_size(), row_buffer.get()); - - - // - // Stage 3: Compute entries in C - // - C.reserve(current_offset, false); - - viennacl::vector<NumericT> scratchpad_values(scratchpad_offset, viennacl::traits::context(A)); // upper bound for the nonzeros per row encountered for each work group - -#ifdef VIENNACL_WITH_SPGEMM_CUDA_TIMINGS - std::cout << "Intermediate stage 2->3: " << timer.get() << std::endl; - timer.start(); -#endif - - compressed_matrix_gemm_stage_3<<<blocknum, threadnum>>>(viennacl::cuda_arg<unsigned int>(A.handle1()), - viennacl::cuda_arg<unsigned int>(A.handle2()), - viennacl::cuda_arg<NumericT>(A.handle()), - static_cast<unsigned int>(A.size1()), - viennacl::cuda_arg<unsigned int>(B.handle1()), - viennacl::cuda_arg<unsigned int>(B.handle2()), - viennacl::cuda_arg<NumericT>(B.handle()), - static_cast<unsigned int>(B.size2()), - viennacl::cuda_arg<unsigned int>(C.handle1()), - viennacl::cuda_arg<unsigned int>(C.handle2()), - viennacl::cuda_arg<NumericT>(C.handle()), - viennacl::cuda_arg(subwarp_sizes), - viennacl::cuda_arg(max_nnz_row_A), - viennacl::cuda_arg(max_nnz_row_B), - viennacl::cuda_arg(scratchpad_offsets), - viennacl::cuda_arg(scratchpad_indices), - viennacl::cuda_arg(scratchpad_values) - ); - VIENNACL_CUDA_LAST_ERROR_CHECK("compressed_matrix_gemm_stage_3"); -#ifdef VIENNACL_WITH_SPGEMM_CUDA_TIMINGS - cudaDeviceSynchronize(); - std::cout << "Stage 3: " << timer.get() << std::endl; - std::cout << "----------" << std::endl; -#endif - -} - -} // namespace cuda -} //namespace linalg -} //namespace viennacl - - -#endif
