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Clean up CUDA solvers and use override specifier. #101

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Dec 6, 2023
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Clean up CUDA solvers and use override specifier. #101

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30 changes: 15 additions & 15 deletions resolve/LinSolverDirectCuSolverGLU.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -59,11 +59,11 @@ namespace ReSolve
n,
nnz,
descr_A_,
A_->getRowData(memory::HOST), //kRowPtr_,
A_->getColData(memory::HOST), //jCol_,
P, /* base-0 */
A_->getRowData(memory::HOST),
A_->getColData(memory::HOST),
P, /* base-0 */
Q, /* base-0 */
M_->getNnz(), /* nnzM */
M_->getNnz(), /* nnzM */
descr_M_,
M_->getRowData(memory::HOST),
M_->getColData(memory::HOST),
Expand All @@ -86,9 +86,9 @@ namespace ReSolve
/* A is original matrix */
nnz,
descr_A_,
A_->getValues( memory::DEVICE), //da_,
A_->getRowData(memory::DEVICE), //kRowPtr_,
A_->getColData(memory::DEVICE), //jCol_,
A_->getValues( memory::DEVICE),
A_->getRowData(memory::DEVICE),
A_->getColData(memory::DEVICE),
info_M_);
error_sum += status_cusolver_;

Expand All @@ -100,7 +100,7 @@ namespace ReSolve

void LinSolverDirectCuSolverGLU::addFactors(matrix::Sparse* L, matrix::Sparse* U)
{
// L and U need to be in CSC format
// L and U need to be in CSC format
index_type n = L->getNumRows();
index_type* Lp = L->getColData(memory::HOST);
index_type* Li = L->getRowData(memory::HOST);
Expand Down Expand Up @@ -162,9 +162,9 @@ namespace ReSolve
/* A is original matrix */
A_->getNnzExpanded(),
descr_A_,
A_->getValues( memory::DEVICE), //da_,
A_->getRowData(memory::DEVICE), //kRowPtr_,
A_->getColData(memory::DEVICE), //jCol_,
A_->getValues( memory::DEVICE),
A_->getRowData(memory::DEVICE),
A_->getColData(memory::DEVICE),
info_M_);
error_sum += status_cusolver_;

Expand All @@ -181,11 +181,11 @@ namespace ReSolve
/* A is original matrix */
A_->getNnz(),
descr_A_,
A_->getValues( memory::DEVICE), //da_,
A_->getRowData(memory::DEVICE), //kRowPtr_,
A_->getColData(memory::DEVICE), //jCol_,
A_->getValues( memory::DEVICE),
A_->getRowData(memory::DEVICE),
A_->getColData(memory::DEVICE),
rhs->getData(memory::DEVICE),/* right hand side */
x->getData(memory::DEVICE),/* left hand side */
x->getData(memory::DEVICE), /* left hand side */
&ite_refine_succ_,
&r_nrminf_,
info_M_,
Expand Down
14 changes: 7 additions & 7 deletions resolve/LinSolverDirectCuSolverGLU.hpp
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Expand Up @@ -28,24 +28,24 @@ namespace ReSolve
LinSolverDirectCuSolverGLU(LinAlgWorkspaceCUDA* workspace);
~LinSolverDirectCuSolverGLU();

int refactorize();
int solve(vector_type* rhs, vector_type* x);
int solve(vector_type* x);
int refactorize() override;
int solve(vector_type* rhs, vector_type* x) override;
int solve(vector_type* x) override;

int setup(matrix::Sparse* A,
matrix::Sparse* L,
matrix::Sparse* U,
index_type* P,
index_type* Q,
vector_type* rhs = nullptr);
vector_type* rhs = nullptr) override;

private:
void addFactors(matrix::Sparse* L, matrix::Sparse* U); //create L+U from sepeate L, U factors
matrix::Sparse* M_;//the matrix that contains added factors
void addFactors(matrix::Sparse* L, matrix::Sparse* U); ///< creates L+U from sepeate L, U factors
matrix::Sparse* M_; ///< the matrix that contains added factors
//note: we need cuSolver handle, we can copy it from the workspace to avoid double allocation
cusparseMatDescr_t descr_M_; //this is NOT sparse matrix descriptor
cusparseMatDescr_t descr_A_; //this is NOT sparse matrix descriptor
LinAlgWorkspaceCUDA* workspace_;// so we can copy cusparse handle
LinAlgWorkspaceCUDA* workspace_; ///< Workspace access so we can copy cusparse handle
cusolverSpHandle_t handle_cusolversp_;
cusolverStatus_t status_cusolver_;
cusparseStatus_t status_cusparse_;
Expand Down
14 changes: 7 additions & 7 deletions resolve/LinSolverDirectCuSolverRf.cpp
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Expand Up @@ -40,9 +40,9 @@ namespace ReSolve
error_sum += status_cusolverrf_;
status_cusolverrf_ = cusolverRfSetupDevice(n,
A_->getNnzExpanded(),
A_->getRowData(memory::DEVICE), //dia_,
A_->getColData(memory::DEVICE), //dja_,
A_->getValues( memory::DEVICE), //da_,
A_->getRowData(memory::DEVICE),
A_->getColData(memory::DEVICE),
A_->getValues( memory::DEVICE),
L->getNnz(),
L->getRowData(memory::DEVICE),
L->getColData(memory::DEVICE),
Expand Down Expand Up @@ -81,9 +81,9 @@ namespace ReSolve
int error_sum = 0;
status_cusolverrf_ = cusolverRfResetValues(A_->getNumRows(),
A_->getNnzExpanded(),
A_->getRowData(memory::DEVICE), //dia_,
A_->getColData(memory::DEVICE), //dja_,
A_->getValues( memory::DEVICE), //da_,
A_->getRowData(memory::DEVICE),
A_->getColData(memory::DEVICE),
A_->getValues( memory::DEVICE),
d_P_,
d_Q_,
handle_cusolverrf_);
Expand Down Expand Up @@ -128,6 +128,6 @@ namespace ReSolve
int LinSolverDirectCuSolverRf::setNumericalProperties(double nzero, double nboost)
{
status_cusolverrf_ = cusolverRfSetNumericProperties(handle_cusolverrf_, nzero, nboost);
return status_cusolverrf_;
return status_cusolverrf_;
}
}// namespace resolve
10 changes: 5 additions & 5 deletions resolve/LinSolverDirectCuSolverRf.hpp
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Expand Up @@ -31,13 +31,13 @@ namespace ReSolve
matrix::Sparse* U,
index_type* P,
index_type* Q,
vector_type* rhs = nullptr);
vector_type* rhs = nullptr) override;

int refactorize() override;
int solve(vector_type* rhs, vector_type* x) override;
int solve(vector_type* rhs) override; // rhs overwritten by solution

void setAlgorithms(cusolverRfFactorization_t fact_alg, cusolverRfTriangularSolve_t solve_alg);

int refactorize();
int solve(vector_type* rhs, vector_type* x);
int solve(vector_type* rhs);// the solutuon is returned IN RHS (rhs is overwritten)
int setNumericalProperties(double nzero, double nboost);//these two NEED TO BE DOUBLE
private:
cusolverRfHandle_t handle_cusolverrf_;
Expand Down
4 changes: 3 additions & 1 deletion resolve/LinSolverDirectCuSparseILU0.cpp
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Expand Up @@ -234,6 +234,7 @@ namespace ReSolve

return error_sum;
}

// solution is returned in RHS
int LinSolverDirectCuSparseILU0::solve(vector_type* rhs)
{
Expand Down Expand Up @@ -309,4 +310,5 @@ namespace ReSolve

return error_sum;
}
}// namespace resolve

} // namespace resolve
28 changes: 14 additions & 14 deletions resolve/LinSolverDirectCuSparseILU0.hpp
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Expand Up @@ -34,40 +34,40 @@ namespace ReSolve
matrix::Sparse* U = nullptr,
index_type* P = nullptr,
index_type* Q = nullptr,
vector_type* rhs = nullptr);
vector_type* rhs = nullptr) override;
// if values of A change, but the nnz pattern does not, redo the analysis only (reuse buffers though)
int reset(matrix::Sparse* A);

int solve(vector_type* rhs, vector_type* x);
int solve(vector_type* rhs);// the solutuon is returned IN RHS (rhs is overwritten)
int solve(vector_type* rhs, vector_type* x) override;
int solve(vector_type* rhs) override;


private:
cusparseStatus_t status_cusparse_;

MemoryHandler mem_; ///< Device memory manager object
LinAlgWorkspaceCUDA* workspace_;
LinAlgWorkspaceCUDA* workspace_{nullptr};

cusparseMatDescr_t descr_A_{nullptr};

cusparseSpMatDescr_t mat_L_;
cusparseSpMatDescr_t mat_U_;
cusparseSpMatDescr_t mat_L_{nullptr};
cusparseSpMatDescr_t mat_U_{nullptr};

cusparseSpSVDescr_t descr_spsv_L_{nullptr};
cusparseSpSVDescr_t descr_spsv_U_{nullptr};
csrilu02Info_t info_A_{nullptr};

void* buffer_;
void* buffer_L_;
void* buffer_U_;
void* buffer_{nullptr};
void* buffer_L_{nullptr};
void* buffer_U_{nullptr};

real_type* d_aux1_;
real_type* d_aux2_;
real_type* d_aux1_{nullptr};
real_type* d_aux2_{nullptr};

cusparseDnVecDescr_t vec_X_;
cusparseDnVecDescr_t vec_Y_;
cusparseDnVecDescr_t vec_X_{nullptr};
cusparseDnVecDescr_t vec_Y_{nullptr};

// since ILU OVERWRITES THE MATRIX values, we need a buffer to keep the values of ILU decomposition.
real_type* d_ILU_vals_;
real_type* d_ILU_vals_{nullptr};
};
}// namespace
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