From 877e7947862d9d29dcc736aecdcb3b1a2e4147dc Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Sun, 3 Dec 2023 20:40:57 +0000 Subject: [PATCH] build based on 474def3 --- dev/.documenter-siteinfo.json | 2 +- dev/index.html | 10 +++++----- dev/search_index.js | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 0eb23f8..2c8a04e 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-12-03T20:34:18","documenter_version":"1.2.1"}} \ No newline at end of file +{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-12-03T20:40:54","documenter_version":"1.2.1"}} \ No newline at end of file diff --git a/dev/index.html b/dev/index.html index 5cb1871..429be18 100644 --- a/dev/index.html +++ b/dev/index.html @@ -3,14 +3,14 @@ pkg> add https://github.com/exanauts/CUDSS.jl.git pkg> test CUDSS

Types

CUDSS.CudssMatrix — Type
matrix = CudssMatrix(v::CuVector)
 matrix = CudssMatrix(A::CuMatrix)
-matrix = CudssMatrix(A::CuSparseMatrixCSR, struture::Union{Char, String}, view::Char; index::Char='O')

CudssMatrix is a wrapper for CuVector, CuMatrix and CuSparseMatrixCSR. CudssMatrix is used to pass matrix of the linear system, as well as solution and right-hand side.

structure specifies the stucture for sparse matrices:

  • 'G' or "G": General matrix – LDU factorization;
  • 'S' or "S": Real symmetric matrix – LDLᵀ factorization;
  • 'H' or "H": Complex Hermitian matrix – LDLᴴ factorization;
  • "SPD": Symmetric positive-definite matrix – LLᵀ factorization;
  • "HPD": Hermitian positive-definite matrix – LLᴴ factorization.

view specifies matrix view for sparse matrices:

  • 'L': Lower-triangular matrix and all values above the main diagonal are ignored;
  • 'U': Upper-triangular matrix and all values below the main diagonal are ignored;
  • 'F': Full matrix.

index specifies indexing base for sparse matrix indices:

  • 'Z': 0-based indexing;
  • 'O': 1-based indexing.
source
CUDSS.CudssConfig — Type
config = CudssConfig()

CudssConfig stores configuration settings for the solver.

source
CUDSS.CudssData — Type
data = CudssData()

CudssData holds internal data (e.g., LU factors arrays).

source
CUDSS.CudssSolver — Type
solver = CudssSolver(A::CuSparseMatrixCSR, structure::Union{Char, String}, view::Char; index::Char='O')
-solver = CudssSolver(matrix::CudssMatrix, config::CudssConfig, data::CudssData)

CudssSolver contains all structures required to solve linear systems with cuDSS. One constructor of CudssSolver takes as input the same parameters as CudssMatrix.

structure specifies the stucture for sparse matrices:

  • 'G' or "G": General matrix – LDU factorization;
  • 'S' or "S": Real symmetric matrix – LDLᵀ factorization;
  • 'H' or "H": Complex Hermitian matrix – LDLᴴ factorization;
  • "SPD": Symmetric positive-definite matrix – LLᵀ factorization;
  • "HPD": Hermitian positive-definite matrix – LLᴴ factorization.

view specifies matrix view for sparse matrices:

  • 'L': Lower-triangular matrix and all values above the main diagonal are ignored;
  • 'U': Upper-triangular matrix and all values below the main diagonal are ignored;
  • 'F': Full matrix.

index specifies indexing base for sparse matrix indices:

  • 'Z': 0-based indexing;
  • 'O': 1-based indexing.

CudssSolver can be also constructed from the three structures CudssMatrix, CudssConfig and CudssData if needed.

source

Functions

CUDSS.cudss_set — Function
cudss_set(matrix::CudssMatrix, v::CuVector)
+matrix = CudssMatrix(A::CuSparseMatrixCSR, struture::Union{Char, String}, view::Char; index::Char='O')

CudssMatrix is a wrapper for CuVector, CuMatrix and CuSparseMatrixCSR. CudssMatrix is used to pass matrix of the linear system, as well as solution and right-hand side.

structure specifies the stucture for sparse matrices:

  • 'G' or "G": General matrix – LDU factorization;
  • 'S' or "S": Real symmetric matrix – LDLᵀ factorization;
  • 'H' or "H": Complex Hermitian matrix – LDLᴴ factorization;
  • "SPD": Symmetric positive-definite matrix – LLᵀ factorization;
  • "HPD": Hermitian positive-definite matrix – LLᴴ factorization.

view specifies matrix view for sparse matrices:

  • 'L': Lower-triangular matrix and all values above the main diagonal are ignored;
  • 'U': Upper-triangular matrix and all values below the main diagonal are ignored;
  • 'F': Full matrix.

index specifies indexing base for sparse matrix indices:

  • 'Z': 0-based indexing;
  • 'O': 1-based indexing.
source
CUDSS.CudssConfig — Type
config = CudssConfig()

CudssConfig stores configuration settings for the solver.

source
CUDSS.CudssData — Type
data = CudssData()

CudssData holds internal data (e.g., LU factors arrays).

source
CUDSS.CudssSolver — Type
solver = CudssSolver(A::CuSparseMatrixCSR, structure::Union{Char, String}, view::Char; index::Char='O')
+solver = CudssSolver(matrix::CudssMatrix, config::CudssConfig, data::CudssData)

CudssSolver contains all structures required to solve linear systems with cuDSS. One constructor of CudssSolver takes as input the same parameters as CudssMatrix.

structure specifies the stucture for sparse matrices:

  • 'G' or "G": General matrix – LDU factorization;
  • 'S' or "S": Real symmetric matrix – LDLᵀ factorization;
  • 'H' or "H": Complex Hermitian matrix – LDLᴴ factorization;
  • "SPD": Symmetric positive-definite matrix – LLᵀ factorization;
  • "HPD": Hermitian positive-definite matrix – LLᴴ factorization.

view specifies matrix view for sparse matrices:

  • 'L': Lower-triangular matrix and all values above the main diagonal are ignored;
  • 'U': Upper-triangular matrix and all values below the main diagonal are ignored;
  • 'F': Full matrix.

index specifies indexing base for sparse matrix indices:

  • 'Z': 0-based indexing;
  • 'O': 1-based indexing.

CudssSolver can be also constructed from the three structures CudssMatrix, CudssConfig and CudssData if needed.

source

Functions

CUDSS.cudss_set — Function
cudss_set(matrix::CudssMatrix, v::CuVector)
 cudss_set(matrix::CudssMatrix, A::CuMatrix)
 cudss_set(matrix::CudssMatrix, A::CuSparseMatrixCSR)
 cudss_set(data::CudssSolver, param::String, value)
 cudss_set(config::CudssConfig, param::String, value)
-cudss_set(data::CudssData, param::String, value)

The available configuration parameters are:

  • "reordering_alg": Algorithm for the reordering phase;
  • "factorization_alg": Algorithm for the factorization phase;
  • "solve_alg": Algorithm for the solving phase;
  • "matching_type": Type of matching;
  • "solve_mode": Potential modificator on the system matrix (transpose or adjoint);
  • "ir_n_steps": Number of steps during the iterative refinement;
  • "ir_tol": Iterative refinement tolerance;
  • "pivot_type": Type of pivoting ('C', 'R' or 'N');
  • "pivot_threshold": Pivoting threshold which is used to determine if digonal element is subject to pivoting;
  • "pivot_epsilon": Pivoting epsilon, absolute value to replace singular diagonal elements;
  • "max_lu_nnz": Upper limit on the number of nonzero entries in LU factors for non-symmetric matrices.

The available data parameter is:

  • "user_perm": User permutation to be used instead of running the reordering algorithms.
source
CUDSS.cudss_get — Function
value = cudss_get(data::CudssSolver, param::String)
+cudss_set(data::CudssData, param::String, value)

The available configuration parameters are:

  • "reordering_alg": Algorithm for the reordering phase;
  • "factorization_alg": Algorithm for the factorization phase;
  • "solve_alg": Algorithm for the solving phase;
  • "matching_type": Type of matching;
  • "solve_mode": Potential modificator on the system matrix (transpose or adjoint);
  • "ir_n_steps": Number of steps during the iterative refinement;
  • "ir_tol": Iterative refinement tolerance;
  • "pivot_type": Type of pivoting ('C', 'R' or 'N');
  • "pivot_threshold": Pivoting threshold which is used to determine if digonal element is subject to pivoting;
  • "pivot_epsilon": Pivoting epsilon, absolute value to replace singular diagonal elements;
  • "max_lu_nnz": Upper limit on the number of nonzero entries in LU factors for non-symmetric matrices.

The available data parameter is:

  • "user_perm": User permutation to be used instead of running the reordering algorithms.
source
CUDSS.cudss_get — Function
value = cudss_get(data::CudssSolver, param::String)
 value = cudss_get(config::CudssConfig, param::String)
-value = cudss_get(data::CudssData, param::String)

The available configuration parameters are:

  • "reordering_alg": Algorithm for the reordering phase;
  • "factorization_alg": Algorithm for the factorization phase;
  • "solve_alg": Algorithm for the solving phase;
  • "matching_type": Type of matching;
  • "solve_mode": Potential modificator on the system matrix (transpose or adjoint);
  • "ir_n_steps": Number of steps during the iterative refinement;
  • "ir_tol": Iterative refinement tolerance;
  • "pivot_type": Type of pivoting ('C', 'R' or 'N');
  • "pivot_threshold": Pivoting threshold which is used to determine if digonal element is subject to pivoting;
  • "pivot_epsilon": Pivoting epsilon, absolute value to replace singular diagonal elements;
  • "max_lu_nnz": Upper limit on the number of nonzero entries in LU factors for non-symmetric matrices.

The available data parameters are:

  • "info": Device-side error information;
  • "lu_nnz": Number of non-zero entries in LU factors;
  • "npivots": Number of pivots encountered during factorization;
  • "inertia": Tuple of positive and negative indices of inertia for symmetric and hermitian non positive-definite matrix types;
  • "perm_reorder": Reordering permutation;
  • "perm_row": Final row permutation (which includes effects of both reordering and pivoting);
  • "perm_col": Final column permutation (which includes effects of both reordering and pivoting);
  • "diag": Diagonal of the factorized matrix.

The data parameters "lu_nnz" and "perm_reorder" requires the phase "analyse" performed by cudss. The data parameters "npivots", "inertia" and "diag" requires the phases "analyse" and "factorization" performed by cudss. The data parameters "perm_row" and "perm_col" are available but not yet functional.

source
CUDSS.cudss — Function
cudss(phase::String, solver::CudssSolver, x::CuVector, b::CuVector)
+value = cudss_get(data::CudssData, param::String)

The available configuration parameters are:

  • "reordering_alg": Algorithm for the reordering phase;
  • "factorization_alg": Algorithm for the factorization phase;
  • "solve_alg": Algorithm for the solving phase;
  • "matching_type": Type of matching;
  • "solve_mode": Potential modificator on the system matrix (transpose or adjoint);
  • "ir_n_steps": Number of steps during the iterative refinement;
  • "ir_tol": Iterative refinement tolerance;
  • "pivot_type": Type of pivoting ('C', 'R' or 'N');
  • "pivot_threshold": Pivoting threshold which is used to determine if digonal element is subject to pivoting;
  • "pivot_epsilon": Pivoting epsilon, absolute value to replace singular diagonal elements;
  • "max_lu_nnz": Upper limit on the number of nonzero entries in LU factors for non-symmetric matrices.

The available data parameters are:

  • "info": Device-side error information;
  • "lu_nnz": Number of non-zero entries in LU factors;
  • "npivots": Number of pivots encountered during factorization;
  • "inertia": Tuple of positive and negative indices of inertia for symmetric and hermitian non positive-definite matrix types;
  • "perm_reorder": Reordering permutation;
  • "perm_row": Final row permutation (which includes effects of both reordering and pivoting);
  • "perm_col": Final column permutation (which includes effects of both reordering and pivoting);
  • "diag": Diagonal of the factorized matrix.

The data parameters "lu_nnz" and "perm_reorder" requires the phase "analyse" performed by cudss. The data parameters "npivots", "inertia" and "diag" requires the phases "analyse" and "factorization" performed by cudss. The data parameters "perm_row" and "perm_col" are available but not yet functional.

source
CUDSS.cudss — Function
cudss(phase::String, solver::CudssSolver, x::CuVector, b::CuVector)
 cudss(phase::String, solver::CudssSolver, X::CuMatrix, B::CuMatrix)
-cudss(phase::String, solver::CudssSolver, X::CudssMatrix, B::CudssMatrix)

The available phases are "analysis", "factorization", "refactorization" and "solve". The phases "solve\_fwd", "solve_diag" and "solve_bwd" are available but not yet functional.

source
+cudss(phase::String, solver::CudssSolver, X::CudssMatrix, B::CudssMatrix)

The available phases are "analysis", "factorization", "refactorization" and "solve". The phases "solve_fwd", "solve_diag" and "solve_bwd" are available but not yet functional.

source diff --git a/dev/search_index.js b/dev/search_index.js index ac7b791..04d0c52 100644 --- a/dev/search_index.js +++ b/dev/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"#Home","page":"Home","title":"CUDSS.jl documentation","text":"","category":"section"},{"location":"#Installation","page":"Home","title":"Installation","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"julia> ]\npkg> add https://github.com/exanauts/CUDSS.jl.git\npkg> test CUDSS","category":"page"},{"location":"#Types","page":"Home","title":"Types","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"CudssMatrix\nCudssConfig\nCudssData\nCudssSolver","category":"page"},{"location":"#CUDSS.CudssMatrix","page":"Home","title":"CUDSS.CudssMatrix","text":"matrix = CudssMatrix(v::CuVector)\nmatrix = CudssMatrix(A::CuMatrix)\nmatrix = CudssMatrix(A::CuSparseMatrixCSR, struture::Union{Char, String}, view::Char; index::Char='O')\n\nCudssMatrix is a wrapper for CuVector, CuMatrix and CuSparseMatrixCSR. CudssMatrix is used to pass matrix of the linear system, as well as solution and right-hand side.\n\nstructure specifies the stucture for sparse matrices:\n\n'G' or \"G\": General matrix – LDU factorization;\n'S' or \"S\": Real symmetric matrix – LDLᵀ factorization;\n'H' or \"H\": Complex Hermitian matrix – LDLᴴ factorization;\n\"SPD\": Symmetric positive-definite matrix – LLᵀ factorization;\n\"HPD\": Hermitian positive-definite matrix – LLᴴ factorization.\n\nview specifies matrix view for sparse matrices:\n\n'L': Lower-triangular matrix and all values above the main diagonal are ignored;\n'U': Upper-triangular matrix and all values below the main diagonal are ignored;\n'F': Full matrix.\n\nindex specifies indexing base for sparse matrix indices:\n\n'Z': 0-based indexing;\n'O': 1-based indexing.\n\n\n\n\n\n","category":"type"},{"location":"#CUDSS.CudssConfig","page":"Home","title":"CUDSS.CudssConfig","text":"config = CudssConfig()\n\nCudssConfig stores configuration settings for the solver.\n\n\n\n\n\n","category":"type"},{"location":"#CUDSS.CudssData","page":"Home","title":"CUDSS.CudssData","text":"data = CudssData()\n\nCudssData holds internal data (e.g., LU factors arrays).\n\n\n\n\n\n","category":"type"},{"location":"#CUDSS.CudssSolver","page":"Home","title":"CUDSS.CudssSolver","text":"solver = CudssSolver(A::CuSparseMatrixCSR, structure::Union{Char, String}, view::Char; index::Char='O')\nsolver = CudssSolver(matrix::CudssMatrix, config::CudssConfig, data::CudssData)\n\nCudssSolver contains all structures required to solve linear systems with cuDSS. One constructor of CudssSolver takes as input the same parameters as CudssMatrix.\n\nstructure specifies the stucture for sparse matrices:\n\n'G' or \"G\": General matrix – LDU factorization;\n'S' or \"S\": Real symmetric matrix – LDLᵀ factorization;\n'H' or \"H\": Complex Hermitian matrix – LDLᴴ factorization;\n\"SPD\": Symmetric positive-definite matrix – LLᵀ factorization;\n\"HPD\": Hermitian positive-definite matrix – LLᴴ factorization.\n\nview specifies matrix view for sparse matrices:\n\n'L': Lower-triangular matrix and all values above the main diagonal are ignored;\n'U': Upper-triangular matrix and all values below the main diagonal are ignored;\n'F': Full matrix.\n\nindex specifies indexing base for sparse matrix indices:\n\n'Z': 0-based indexing;\n'O': 1-based indexing.\n\nCudssSolver can be also constructed from the three structures CudssMatrix, CudssConfig and CudssData if needed.\n\n\n\n\n\n","category":"type"},{"location":"#Functions","page":"Home","title":"Functions","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"cudss_set\ncudss_get\ncudss","category":"page"},{"location":"#CUDSS.cudss_set","page":"Home","title":"CUDSS.cudss_set","text":"cudss_set(matrix::CudssMatrix, v::CuVector)\ncudss_set(matrix::CudssMatrix, A::CuMatrix)\ncudss_set(matrix::CudssMatrix, A::CuSparseMatrixCSR)\ncudss_set(data::CudssSolver, param::String, value)\ncudss_set(config::CudssConfig, param::String, value)\ncudss_set(data::CudssData, param::String, value)\n\nThe available configuration parameters are:\n\n\"reordering_alg\": Algorithm for the reordering phase;\n\"factorization_alg\": Algorithm for the factorization phase;\n\"solve_alg\": Algorithm for the solving phase;\n\"matching_type\": Type of matching;\n\"solve_mode\": Potential modificator on the system matrix (transpose or adjoint);\n\"ir_n_steps\": Number of steps during the iterative refinement;\n\"ir_tol\": Iterative refinement tolerance;\n\"pivot_type\": Type of pivoting ('C', 'R' or 'N');\n\"pivot_threshold\": Pivoting threshold which is used to determine if digonal element is subject to pivoting;\n\"pivot_epsilon\": Pivoting epsilon, absolute value to replace singular diagonal elements;\n\"max_lu_nnz\": Upper limit on the number of nonzero entries in LU factors for non-symmetric matrices.\n\nThe available data parameter is:\n\n\"user_perm\": User permutation to be used instead of running the reordering algorithms.\n\n\n\n\n\n","category":"function"},{"location":"#CUDSS.cudss_get","page":"Home","title":"CUDSS.cudss_get","text":"value = cudss_get(data::CudssSolver, param::String)\nvalue = cudss_get(config::CudssConfig, param::String)\nvalue = cudss_get(data::CudssData, param::String)\n\nThe available configuration parameters are:\n\n\"reordering_alg\": Algorithm for the reordering phase;\n\"factorization_alg\": Algorithm for the factorization phase;\n\"solve_alg\": Algorithm for the solving phase;\n\"matching_type\": Type of matching;\n\"solve_mode\": Potential modificator on the system matrix (transpose or adjoint);\n\"ir_n_steps\": Number of steps during the iterative refinement;\n\"ir_tol\": Iterative refinement tolerance;\n\"pivot_type\": Type of pivoting ('C', 'R' or 'N');\n\"pivot_threshold\": Pivoting threshold which is used to determine if digonal element is subject to pivoting;\n\"pivot_epsilon\": Pivoting epsilon, absolute value to replace singular diagonal elements;\n\"max_lu_nnz\": Upper limit on the number of nonzero entries in LU factors for non-symmetric matrices.\n\nThe available data parameters are:\n\n\"info\": Device-side error information;\n\"lu_nnz\": Number of non-zero entries in LU factors;\n\"npivots\": Number of pivots encountered during factorization;\n\"inertia\": Tuple of positive and negative indices of inertia for symmetric and hermitian non positive-definite matrix types;\n\"perm_reorder\": Reordering permutation;\n\"perm_row\": Final row permutation (which includes effects of both reordering and pivoting);\n\"perm_col\": Final column permutation (which includes effects of both reordering and pivoting);\n\"diag\": Diagonal of the factorized matrix.\n\nThe data parameters \"lu_nnz\" and \"perm_reorder\" requires the phase \"analyse\" performed by cudss. The data parameters \"npivots\", \"inertia\" and \"diag\" requires the phases \"analyse\" and \"factorization\" performed by cudss. The data parameters \"perm_row\" and \"perm_col\" are available but not yet functional.\n\n\n\n\n\n","category":"function"},{"location":"#CUDSS.cudss","page":"Home","title":"CUDSS.cudss","text":"cudss(phase::String, solver::CudssSolver, x::CuVector, b::CuVector)\ncudss(phase::String, solver::CudssSolver, X::CuMatrix, B::CuMatrix)\ncudss(phase::String, solver::CudssSolver, X::CudssMatrix, B::CudssMatrix)\n\nThe available phases are \"analysis\", \"factorization\", \"refactorization\" and \"solve\". The phases \"solve\\_fwd\", \"solve_diag\" and \"solve_bwd\" are available but not yet functional.\n\n\n\n\n\n","category":"function"}] +[{"location":"#Home","page":"Home","title":"CUDSS.jl documentation","text":"","category":"section"},{"location":"#Installation","page":"Home","title":"Installation","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"julia> ]\npkg> add https://github.com/exanauts/CUDSS.jl.git\npkg> test CUDSS","category":"page"},{"location":"#Types","page":"Home","title":"Types","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"CudssMatrix\nCudssConfig\nCudssData\nCudssSolver","category":"page"},{"location":"#CUDSS.CudssMatrix","page":"Home","title":"CUDSS.CudssMatrix","text":"matrix = CudssMatrix(v::CuVector)\nmatrix = CudssMatrix(A::CuMatrix)\nmatrix = CudssMatrix(A::CuSparseMatrixCSR, struture::Union{Char, String}, view::Char; index::Char='O')\n\nCudssMatrix is a wrapper for CuVector, CuMatrix and CuSparseMatrixCSR. CudssMatrix is used to pass matrix of the linear system, as well as solution and right-hand side.\n\nstructure specifies the stucture for sparse matrices:\n\n'G' or \"G\": General matrix – LDU factorization;\n'S' or \"S\": Real symmetric matrix – LDLᵀ factorization;\n'H' or \"H\": Complex Hermitian matrix – LDLᴴ factorization;\n\"SPD\": Symmetric positive-definite matrix – LLᵀ factorization;\n\"HPD\": Hermitian positive-definite matrix – LLᴴ factorization.\n\nview specifies matrix view for sparse matrices:\n\n'L': Lower-triangular matrix and all values above the main diagonal are ignored;\n'U': Upper-triangular matrix and all values below the main diagonal are ignored;\n'F': Full matrix.\n\nindex specifies indexing base for sparse matrix indices:\n\n'Z': 0-based indexing;\n'O': 1-based indexing.\n\n\n\n\n\n","category":"type"},{"location":"#CUDSS.CudssConfig","page":"Home","title":"CUDSS.CudssConfig","text":"config = CudssConfig()\n\nCudssConfig stores configuration settings for the solver.\n\n\n\n\n\n","category":"type"},{"location":"#CUDSS.CudssData","page":"Home","title":"CUDSS.CudssData","text":"data = CudssData()\n\nCudssData holds internal data (e.g., LU factors arrays).\n\n\n\n\n\n","category":"type"},{"location":"#CUDSS.CudssSolver","page":"Home","title":"CUDSS.CudssSolver","text":"solver = CudssSolver(A::CuSparseMatrixCSR, structure::Union{Char, String}, view::Char; index::Char='O')\nsolver = CudssSolver(matrix::CudssMatrix, config::CudssConfig, data::CudssData)\n\nCudssSolver contains all structures required to solve linear systems with cuDSS. One constructor of CudssSolver takes as input the same parameters as CudssMatrix.\n\nstructure specifies the stucture for sparse matrices:\n\n'G' or \"G\": General matrix – LDU factorization;\n'S' or \"S\": Real symmetric matrix – LDLᵀ factorization;\n'H' or \"H\": Complex Hermitian matrix – LDLᴴ factorization;\n\"SPD\": Symmetric positive-definite matrix – LLᵀ factorization;\n\"HPD\": Hermitian positive-definite matrix – LLᴴ factorization.\n\nview specifies matrix view for sparse matrices:\n\n'L': Lower-triangular matrix and all values above the main diagonal are ignored;\n'U': Upper-triangular matrix and all values below the main diagonal are ignored;\n'F': Full matrix.\n\nindex specifies indexing base for sparse matrix indices:\n\n'Z': 0-based indexing;\n'O': 1-based indexing.\n\nCudssSolver can be also constructed from the three structures CudssMatrix, CudssConfig and CudssData if needed.\n\n\n\n\n\n","category":"type"},{"location":"#Functions","page":"Home","title":"Functions","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"cudss_set\ncudss_get\ncudss","category":"page"},{"location":"#CUDSS.cudss_set","page":"Home","title":"CUDSS.cudss_set","text":"cudss_set(matrix::CudssMatrix, v::CuVector)\ncudss_set(matrix::CudssMatrix, A::CuMatrix)\ncudss_set(matrix::CudssMatrix, A::CuSparseMatrixCSR)\ncudss_set(data::CudssSolver, param::String, value)\ncudss_set(config::CudssConfig, param::String, value)\ncudss_set(data::CudssData, param::String, value)\n\nThe available configuration parameters are:\n\n\"reordering_alg\": Algorithm for the reordering phase;\n\"factorization_alg\": Algorithm for the factorization phase;\n\"solve_alg\": Algorithm for the solving phase;\n\"matching_type\": Type of matching;\n\"solve_mode\": Potential modificator on the system matrix (transpose or adjoint);\n\"ir_n_steps\": Number of steps during the iterative refinement;\n\"ir_tol\": Iterative refinement tolerance;\n\"pivot_type\": Type of pivoting ('C', 'R' or 'N');\n\"pivot_threshold\": Pivoting threshold which is used to determine if digonal element is subject to pivoting;\n\"pivot_epsilon\": Pivoting epsilon, absolute value to replace singular diagonal elements;\n\"max_lu_nnz\": Upper limit on the number of nonzero entries in LU factors for non-symmetric matrices.\n\nThe available data parameter is:\n\n\"user_perm\": User permutation to be used instead of running the reordering algorithms.\n\n\n\n\n\n","category":"function"},{"location":"#CUDSS.cudss_get","page":"Home","title":"CUDSS.cudss_get","text":"value = cudss_get(data::CudssSolver, param::String)\nvalue = cudss_get(config::CudssConfig, param::String)\nvalue = cudss_get(data::CudssData, param::String)\n\nThe available configuration parameters are:\n\n\"reordering_alg\": Algorithm for the reordering phase;\n\"factorization_alg\": Algorithm for the factorization phase;\n\"solve_alg\": Algorithm for the solving phase;\n\"matching_type\": Type of matching;\n\"solve_mode\": Potential modificator on the system matrix (transpose or adjoint);\n\"ir_n_steps\": Number of steps during the iterative refinement;\n\"ir_tol\": Iterative refinement tolerance;\n\"pivot_type\": Type of pivoting ('C', 'R' or 'N');\n\"pivot_threshold\": Pivoting threshold which is used to determine if digonal element is subject to pivoting;\n\"pivot_epsilon\": Pivoting epsilon, absolute value to replace singular diagonal elements;\n\"max_lu_nnz\": Upper limit on the number of nonzero entries in LU factors for non-symmetric matrices.\n\nThe available data parameters are:\n\n\"info\": Device-side error information;\n\"lu_nnz\": Number of non-zero entries in LU factors;\n\"npivots\": Number of pivots encountered during factorization;\n\"inertia\": Tuple of positive and negative indices of inertia for symmetric and hermitian non positive-definite matrix types;\n\"perm_reorder\": Reordering permutation;\n\"perm_row\": Final row permutation (which includes effects of both reordering and pivoting);\n\"perm_col\": Final column permutation (which includes effects of both reordering and pivoting);\n\"diag\": Diagonal of the factorized matrix.\n\nThe data parameters \"lu_nnz\" and \"perm_reorder\" requires the phase \"analyse\" performed by cudss. The data parameters \"npivots\", \"inertia\" and \"diag\" requires the phases \"analyse\" and \"factorization\" performed by cudss. The data parameters \"perm_row\" and \"perm_col\" are available but not yet functional.\n\n\n\n\n\n","category":"function"},{"location":"#CUDSS.cudss","page":"Home","title":"CUDSS.cudss","text":"cudss(phase::String, solver::CudssSolver, x::CuVector, b::CuVector)\ncudss(phase::String, solver::CudssSolver, X::CuMatrix, B::CuMatrix)\ncudss(phase::String, solver::CudssSolver, X::CudssMatrix, B::CudssMatrix)\n\nThe available phases are \"analysis\", \"factorization\", \"refactorization\" and \"solve\". The phases \"solve_fwd\", \"solve_diag\" and \"solve_bwd\" are available but not yet functional.\n\n\n\n\n\n","category":"function"}] }