single precision
[Sparse unfiled]

Functions

magma_int_t magma_sjacobisetup_matrix (magma_s_sparse_matrix A, magma_s_sparse_matrix *M, magma_s_vector *d, magma_queue_t queue)
 Prepares the Matrix M for the Jacobi Iteration according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.
magma_int_t magma_sjacobisetup_diagscal (magma_s_sparse_matrix A, magma_s_vector *d, magma_queue_t queue)
 It returns a vector d containing the inverse diagonal elements.
magma_int_t magma_sjacobisetup_vector (magma_s_vector b, magma_s_vector d, magma_s_vector *c, magma_queue_t queue)
 Prepares the Jacobi Iteration according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.
magma_int_t magma_sjacobisetup (magma_s_sparse_matrix A, magma_s_vector b, magma_s_sparse_matrix *M, magma_s_vector *c, magma_queue_t queue)
 Prepares the Jacobi Iteration according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.
magma_int_t magma_sjacobiiter (magma_s_sparse_matrix M, magma_s_vector c, magma_s_vector *x, magma_s_solver_par *solver_par, magma_queue_t queue)
 Iterates the solution approximation according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.
magma_int_t magma_sjacobiiter_precond (magma_s_sparse_matrix M, magma_s_vector *x, magma_s_solver_par *solver_par, magma_s_preconditioner *precond, magma_queue_t queue)
 Iterates the solution approximation according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.
magma_int_t magma_s_spmv (float alpha, magma_s_sparse_matrix A, magma_s_vector x, float beta, magma_s_vector y, magma_queue_t queue)
 For a given input matrix A and vectors x, y and scalars alpha, beta the wrapper determines the suitable SpMV computing y = alpha * A * x + beta * y.
void magma_scompactActive (magma_int_t m, magma_int_t n, magmaFloat_ptr dA, magma_int_t ldda, magmaInt_ptr active, magma_queue_t queue)
 ZCOMPACTACTIVE takes a set of n vectors of size m (in dA) and an array of 1s and 0sindicating which vectors to compact (for 1s) and which to disregard (for 0s).
magma_int_t magma_sgeelltmv (magma_trans_t transA, magma_int_t m, magma_int_t n, magma_int_t nnz_per_row, float alpha, magmaFloat_ptr dval, magmaIndex_ptr dcolind, magmaFloat_ptr dx, float beta, magmaFloat_ptr dy, magma_queue_t queue)
 This routine computes y = alpha * A^t * x + beta * y on the GPU.
magma_int_t magma_sjacobi_diagscal (int num_rows, magma_s_vector d, magma_s_vector b, magma_s_vector *c, magma_queue_t queue)
 Prepares the Jacobi Iteration according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.
magma_int_t magma_sgemvmdot (int n, int k, magmaFloat_ptr v, magmaFloat_ptr r, magmaFloat_ptr d1, magmaFloat_ptr d2, magmaFloat_ptr skp, magma_queue_t queue)
 This is an extension of the merged dot product above by chunking the set of vectors v_i such that the data always fits into cache.

Function Documentation

magma_int_t magma_s_spmv ( float  alpha,
magma_s_sparse_matrix  A,
magma_s_vector  x,
float  beta,
magma_s_vector  y,
magma_queue_t  queue 
)

For a given input matrix A and vectors x, y and scalars alpha, beta the wrapper determines the suitable SpMV computing y = alpha * A * x + beta * y.

Parameters:
[in] alpha float scalar alpha
[in] A magma_s_sparse_matrix sparse matrix A
[in] x magma_s_vector input vector x
[in] beta float scalar beta
[out] y magma_s_vector output vector y
[in] queue magma_queue_t Queue to execute in.
void magma_scompactActive ( magma_int_t  m,
magma_int_t  n,
magmaFloat_ptr  dA,
magma_int_t  ldda,
magmaInt_ptr  active,
magma_queue_t  queue 
)

ZCOMPACTACTIVE takes a set of n vectors of size m (in dA) and an array of 1s and 0sindicating which vectors to compact (for 1s) and which to disregard (for 0s).

Parameters:
[in] m INTEGER The number of rows of the matrix dA. M >= 0.
[in] n INTEGER The number of columns of the matrix dA. N >= 0.
[in] in,out] dA COMPLEX REAL array, dimension (LDDA,N) The m by n matrix dA.
[in] ldda INTEGER The leading dimension of the array dA. LDDA >= max(1,M).
[in] active INTEGER array, dimension N A mask of 1s and 0s showing if a vector remains or has been removed
[in] queue magma_queue_t Queue to execute in.
magma_int_t magma_sgeelltmv ( magma_trans_t  transA,
magma_int_t  m,
magma_int_t  n,
magma_int_t  nnz_per_row,
float  alpha,
magmaFloat_ptr  dval,
magmaIndex_ptr  dcolind,
magmaFloat_ptr  dx,
float  beta,
magmaFloat_ptr  dy,
magma_queue_t  queue 
)

This routine computes y = alpha * A^t * x + beta * y on the GPU.

Input format is ELL.

Parameters:
[in] transA magma_trans_t transposition parameter for A
[in] m magma_int_t number of rows in A
[in] n magma_int_t number of columns in A
[in] nnz_per_row magma_int_t number of elements in the longest row
[in] alpha float scalar multiplier
[in] dval magmaFloat_ptr array containing values of A in ELL
[in] dcolind magmaIndex_ptr columnindices of A in ELL
[in] dx magmaFloat_ptr input vector x
[in] beta float scalar multiplier
[out] dy magmaFloat_ptr input/output vector y
[in] queue magma_queue_t Queue to execute in.
magma_int_t magma_sgemvmdot ( int  n,
int  k,
magmaFloat_ptr  v,
magmaFloat_ptr  r,
magmaFloat_ptr  d1,
magmaFloat_ptr  d2,
magmaFloat_ptr  skp,
magma_queue_t  queue 
)

This is an extension of the merged dot product above by chunking the set of vectors v_i such that the data always fits into cache.

It is equivalent to a matrix vecor product Vr where V contains few rows and many columns. The computation is the same:

skp = ( <v_0,r>, <v_1,r>, .. )

Returns the vector skp.

Parameters:
[in] n int length of v_i and r
[in] k int # vectors v_i
[in] v magmaFloat_ptr v = (v_0 .. v_i.. v_k)
[in] r magmaFloat_ptr r
[in] d1 magmaFloat_ptr workspace
[in] d2 magmaFloat_ptr workspace
[out] skp magmaFloat_ptr vector[k] of scalar products (<v_i,r>...)
[in] queue magma_queue_t Queue to execute in.
magma_int_t magma_sjacobi_diagscal ( int  num_rows,
magma_s_vector  d,
magma_s_vector  b,
magma_s_vector *  c,
magma_queue_t  queue 
)

Prepares the Jacobi Iteration according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.

Returns the vector c. It calls a GPU kernel

Parameters:
[in] num_rows magma_int_t number of rows
[in] b magma_s_vector RHS b
[in] d magma_s_vector vector with diagonal entries
[out] c magma_s_vector* c = D^(-1) * b
[in] queue magma_queue_t Queue to execute in.
magma_int_t magma_sjacobiiter ( magma_s_sparse_matrix  M,
magma_s_vector  c,
magma_s_vector *  x,
magma_s_solver_par *  solver_par,
magma_queue_t  queue 
)

Iterates the solution approximation according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.

Parameters:
[in] M magma_s_sparse_matrix input matrix M = D^(-1) * (L+U)
[in] c magma_s_vector c = D^(-1) * b
[in,out] x magma_s_vector* iteration vector x
[in,out] solver_par magma_s_solver_par* solver parameters
[in] queue magma_queue_t Queue to execute in.
magma_int_t magma_sjacobiiter_precond ( magma_s_sparse_matrix  M,
magma_s_vector *  x,
magma_s_solver_par *  solver_par,
magma_s_preconditioner *  precond,
magma_queue_t  queue 
)

Iterates the solution approximation according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.

Parameters:
[in] M magma_s_sparse_matrix input matrix M = D^(-1) * (L+U)
[in] c magma_s_vector c = D^(-1) * b
[in,out] x magma_s_vector* iteration vector x
[in,out] solver_par magma_s_solver_par* solver parameters
[in] solver_par magma_s_precond_par* precond parameters
[in] queue magma_queue_t Queue to execute in.
magma_int_t magma_sjacobisetup ( magma_s_sparse_matrix  A,
magma_s_vector  b,
magma_s_sparse_matrix *  M,
magma_s_vector *  c,
magma_queue_t  queue 
)

Prepares the Jacobi Iteration according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.

Parameters:
[in] A magma_s_sparse_matrix input matrix A
[in] b magma_s_vector RHS b
[in] M magma_s_sparse_matrix* M = D^(-1) * (L+U)
[in] c magma_s_vector* c = D^(-1) * b
[in] queue magma_queue_t Queue to execute in.
magma_int_t magma_sjacobisetup_diagscal ( magma_s_sparse_matrix  A,
magma_s_vector *  d,
magma_queue_t  queue 
)

It returns a vector d containing the inverse diagonal elements.

Parameters:
[in] A magma_s_sparse_matrix input matrix A
[in,out] d magma_s_vector* vector with diagonal elements
[in] queue magma_queue_t Queue to execute in.
magma_int_t magma_sjacobisetup_matrix ( magma_s_sparse_matrix  A,
magma_s_sparse_matrix *  M,
magma_s_vector *  d,
magma_queue_t  queue 
)

Prepares the Matrix M for the Jacobi Iteration according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.

It returns the preconditioner Matrix M and a vector d containing the diagonal elements.

Parameters:
[in] A magma_s_sparse_matrix input matrix A
[in] M magma_s_sparse_matrix* M = D^(-1) * (L+U)
[in,out] d magma_s_vector* vector with diagonal elements of A
[in] queue magma_queue_t Queue to execute in.
magma_int_t magma_sjacobisetup_vector ( magma_s_vector  b,
magma_s_vector  d,
magma_s_vector *  c,
magma_queue_t  queue 
)

Prepares the Jacobi Iteration according to x^(k+1) = D^(-1) * b - D^(-1) * (L+U) * x^k x^(k+1) = c - M * x^k.

Returns the vector c

Parameters:
[in] b magma_s_vector RHS b
[in] d magma_s_vector vector with diagonal entries
[in] c magma_s_vector* c = D^(-1) * b
[in] queue magma_queue_t Queue to execute in.

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