From abb0f018db82d05ad90a65ccfa9ac27334ab6ef3 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Sun, 3 Dec 2023 20:45:51 +0000 Subject: [PATCH] build based on 9102bb6 --- dev/.documenter-siteinfo.json | 2 +- dev/index.html | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 2c8a04e..b9568d1 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-12-03T20:40:54","documenter_version":"1.2.1"}} \ No newline at end of file +{"documenter":{"julia_version":"1.9.4","generation_timestamp":"2023-12-03T20:45:49","documenter_version":"1.2.1"}} \ No newline at end of file diff --git a/dev/index.html b/dev/index.html index 429be18..a8fdd49 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