# Data types¶

## Options¶

type sylver_options

The derived data type sylver_options is used to specify the options used within SyLVER. The components, that are automatically given default values in the definition of the type, are:

Type fields:
• % print_level [integer,default=0] ::

the level of printing. The different levels are:

 < 0 No printing. = 0 Error and warning messages only. = 1 As 0, plus basic diagnostic printing. > 1 As 1, plus some additional diagnostic printing.
• % unit_diagnostics [integer,default=6] :: Fortran unit number for diagnostics printing. Printing is suppressed if <0.
• % unit_error [integer,default=6] :: Fortran unit number for printing of error messages. Printing is suppressed if <0.
• % unit_warning [integer,default=6] :: Fortran unit number for printing of warning messages. Printing is suppressed if <0.
• % ordering [integer,default=1] ::

Ordering method to use in analyse phase:

 0 User-supplied ordering is used (order argument to spldlt_analyse() or splu_analyse()). 1 (default) METIS ordering with default settings. 2 Matching-based elimination ordering is computed (the Hungarian algorithm is used to identify large off-diagonal entries. A restricted METIS ordering is then used that forces these on to the subdiagonal).Note: This option should only be chosen for indefinite systems. A scaling is also computed that may be used in spldlt_factor() or splu_factor() (see %scaling below).
• % nemin [integer,default=32] :: supernode amalgamation threshold. Two neighbours in the elimination tree are merged if they both involve fewer than nemin eliminations. The default is used if nemin<1.
• % use_gpu [logical,default=true] :: Use an NVIDIA GPU if present.
• % scaling [integer,default=0] ::

scaling algorithm to use:

 <=0 (default) No scaling (if scale(:) is not present on call to spldlt_factor() or splu_factor(), or user-supplied scaling (if scale(:) is present). =1 Compute using weighted bipartite matching via the Hungarian Algorithm (MC64 algorithm). =2 Compute using a weighted bipartite matching via the Auction Algorithm (may be lower quality than that computed using the Hungarian Algorithm, but can be considerably faster). =3 Use matching-based ordering generated during the analyse phase using options%ordering=2. The scaling will be the same as that generated with options%scaling= 1 if the matrix values have not changed. This option will generate an error if a matching-based ordering was not used during analysis. >=4 Compute using the norm-equilibration algorithm of Ruiz.
• % nb [integer,default=256] :: Block size to use for parallelization of large nodes on CPU resources.
• % pivot_method [integer,default=1] ::

Pivot method to be used on CPU, one of:

 0 Aggressive a posteori pivoting. Cholesky-like communication pattern is used, but a single failed pivot requires restart of node factorization and potential recalculation of all uneliminated entries. 1 (default) Block a posteori pivoting. A failed pivot only requires recalculation of entries within its own block column. 2 Threshold partial pivoting. Not parallel.
• % small [real,default=1d-20] :: threshold below which an entry is treated as equivalent to 0.0.
• % u [real,default=0.01] :: relative pivot threshold used in symmetric indefinite case. Values outside of the range $$[0,0.5]$$ are treated as the closest value in that range.

## Information¶

type sylver_inform

The derived data type sylver_inform is used to return information about the progress and needs of the algorithm that might be of interest for the user.

Type fields: % flag [integer] :: exit status of the algorithm (see table below). % cublas_error [integer] :: CUBLAS error code in the event of a CUBLAS error (0 otherwise). % cuda_error [integer] :: CUDA error code in the event of a CUDA error (0 otherwise). Note that due to asynchronous execution, CUDA errors may not be reported by the call that caused them. % matrix_dup [integer] :: number of duplicate entries encountered (if spldlt_analyse() or splu_analyse() called with check=true). % matrix_missing_diag [integer] :: number of diagonal entries without an explicit value (if spldlt_analyse() or splu_analyse() called with check=true). % matrix_outrange [integer] :: number of out-of-range entries encountered (if spldlt_analyse() or splu_analyse() called with check=true). % matrix_rank [integer] :: (estimated) rank (structural after analyse phase, numerical after factorize phase). % maxdepth [integer] :: maximum depth of the assembly tree. % maxfront [integer] :: maximum front size (without pivoting after analyse phase, with pivoting after factorize phase). % num_delay [integer] :: number of delayed pivots. That is, the total number of fully-summed variables that were passed to the father node because of stability considerations. If a variable is passed further up the tree, it will be counted again. % num_factor [long] :: number of entries in $$L$$ (without pivoting after analyse phase, with pivoting after factorize phase). % num_flops [long] :: number of floating-point operations for Cholesky factorization (indefinte needs slightly more). Without pivoting after analyse phase, with pivoting after factorize phase. % num_neg [integer] :: number of negative eigenvalues of the matrix $$D$$ after factorize phase. % num_sup [integer] :: number of supernodes in assembly tree. % num_two [integer] :: number of $$2 \times 2$$ pivots used by the factorization (i.e. in the matrix $$D$$ in the indefinite case). % stat [integer] :: Fortran allocation status parameter in event of allocation error (0 otherwise).
inform%flag Return status
0 Success.
-1 Error in sequence of calls (may be caused by failure of a preceding call).
-2 n<0 or ne<1.
-3 Error in ptr(:).
-4

CSC format: All variable indices in one or more columns are out-of-range.

Coordinate format: All entries are out-of-range.

-5 Matrix is singular and options%action=.false.
-6 Matrix found not to be positive definite but posdef=true.
-7 ptr(:) and/or row(:) not present although required.
-8 options%ordering out of range, or options%ordering=0 and order parameter not provided or not a valid permutation.
-9 options%ordering=-2 but val(:) was not supplied.
-10 ldx<n or nrhs<1.
-11 job is out-of-range.
-13 Called spldlt_enquire_posdef() on indefinite factorization.
-14 Called spldlt_enquire_indef() on positive-definite factorization.
-15 options%scaling=3 but a matching-based ordering was not performed during analyse phase.
-50 Allocation error. If available, the stat parameter is returned in inform%stat.
-51 CUDA error. The CUDA error return value is returned in inform%cuda_error.
-52 CUBLAS error. The CUBLAS error return value is returned in inform%cublas_error.
+1 Out-of-range variable indices found and ignored in input data. inform%matrix_outrange is set to the number of such entries.
+2 Duplicate entries found and summed in input data. inform%matrix_dup is set to the number of such entries.
+3 Combination of +1 and +2.
+4 One or more diagonal entries of $$A$$ are missing.
+5 Combination of +4 and +1 or +2.
+6 Matrix is found be (structurally) singular during analyse phase. This will overwrite any of the above warning flags.
+7 Matrix is found to be singular during factorize phase.
+8 Matching-based scaling found as side-effect of matching-based ordering ignored (consider setting options%scaling=3).