TREX/qmckl

Fork 0

mirror of https://github.com/TREX-CoE/qmckl.git synced 2024-11-19 20:42:50 +01:00

Anthony Scemama 4b36005ca0 Added vectors and matrices in nucleus

2022-01-23 19:07:27 +01:00

73 KiB

Raw Blame History

BLAS functions

Data types
Matrix operations

Data types

Vector

Variable	Type	Description
`size`	`int64_t`	Dimension of the vector
`data`	`double*`	Elements

typedef struct qmckl_vector {
int64_t size;
double* data;
} qmckl_vector;

qmckl_vector
qmckl_vector_alloc( qmckl_context context, 
                 const int64_t size);

Allocates a new vector. If the allocation failed the size is zero.

qmckl_vector
qmckl_vector_alloc( qmckl_context context, 
                 const int64_t size)
{
/* Should always be true by contruction */
assert (size > (int64_t) 0);

qmckl_vector result;
result.size = size;

qmckl_memory_info_struct mem_info = qmckl_memory_info_struct_zero;
mem_info.size = size * sizeof(double);
result.data = (double*) qmckl_malloc (context, mem_info);

if (result.data == NULL) {
 result.size = (int64_t) 0;
}

return result;
}

qmckl_exit_code
qmckl_vector_free( qmckl_context context, 
                qmckl_vector vector);

qmckl_exit_code
qmckl_vector_free( qmckl_context context, 
                qmckl_vector vector)
{
/* Always true */
assert (vector.data != NULL);

qmckl_exit_code rc;

rc = qmckl_free(context, vector.data);
if (rc != QMCKL_SUCCESS) {
 return rc;
}

vector.size = (int64_t) 0;
vector.data = NULL;
return QMCKL_SUCCESS;
}

Matrix

Variable	Type	Description
`size`	`int64_t[2]`	Dimension of each component
`data`	`double*`	Elements

The dimensions use Fortran ordering: two elements differing by one in the first dimension are consecutive in memory.

typedef struct qmckl_matrix {
int64_t size[2];
double* data;
} qmckl_matrix;

qmckl_matrix
qmckl_matrix_alloc( qmckl_context context, 
                 const int64_t size1,
                 const int64_t size2);

Allocates a new matrix. If the allocation failed the sizes are zero.

qmckl_matrix
qmckl_matrix_alloc( qmckl_context context, 
                 const int64_t size1,
                 const int64_t size2)
{
/* Should always be true by contruction */
assert (size1 * size2 > (int64_t) 0);

qmckl_matrix result;

result.size[0] = size1;
result.size[1] = size2;

qmckl_memory_info_struct mem_info = qmckl_memory_info_struct_zero;
mem_info.size = size1 * size2 * sizeof(double);
result.data = (double*) qmckl_malloc (context, mem_info);

if (result.data == NULL) {
 result.size[0] = (int64_t) 0;
 result.size[1] = (int64_t) 0;
}

return result;
}

qmckl_exit_code
qmckl_matrix_free( qmckl_context context, 
                qmckl_matrix matrix);

qmckl_exit_code
qmckl_matrix_free( qmckl_context context, 
                qmckl_matrix matrix)
{
/* Always true */
assert (matrix.data != NULL);

qmckl_exit_code rc;

rc = qmckl_free(context, matrix.data);
if (rc != QMCKL_SUCCESS) {
 return rc;
}
matrix.data = NULL;
matrix.size[0] = (int64_t) 0;
matrix.size[1] = (int64_t) 0;

return QMCKL_SUCCESS;
}

Tensor

Variable	Type	Description
`order`	`int64_t`	Order of the tensor
`size`	`int64_t[QMCKL_TENSOR_ORDER_MAX]`	Dimension of each component
`data`	`double*`	Elements

The dimensions use Fortran ordering: two elements differing by one in the first dimension are consecutive in memory.

#define QMCKL_TENSOR_ORDER_MAX 16

typedef struct qmckl_tensor {
int64_t order;
int64_t size[QMCKL_TENSOR_ORDER_MAX];
double* data;
} qmckl_tensor;

qmckl_tensor
qmckl_tensor_alloc( qmckl_context context, 
                 const int64_t order,
                 const int64_t* size);

Allocates memory for a tensor. If the allocation failed, the size is zero.

qmckl_tensor
qmckl_tensor_alloc( qmckl_context context, 
                 const int64_t  order,
                 const int64_t* size)
{
/* Should always be true by contruction */
assert (order > 0);
assert (order <= QMCKL_TENSOR_ORDER_MAX);
assert (size  != NULL);

qmckl_tensor result;
result.order = order;

int64_t prod_size = (int64_t) 1;
for (int64_t i=0 ; i<order ; ++i) {
 assert (size[i] > (int64_t) 0);
 result.size[i] = size[i];
 prod_size *= size[i];
}

qmckl_memory_info_struct mem_info = qmckl_memory_info_struct_zero;
mem_info.size = prod_size * sizeof(double);

result.data = (double*) qmckl_malloc (context, mem_info);

if (result.data == NULL) {
 memset(&result, 0, sizeof(qmckl_tensor));
}

return result;
}

qmckl_exit_code
qmckl_tensor_free( qmckl_context context, 
                qmckl_tensor tensor);

qmckl_exit_code
qmckl_tensor_free( qmckl_context context, 
                qmckl_tensor tensor)
{
/* Always true */
assert (tensor.data != NULL);

qmckl_exit_code rc;

rc = qmckl_free(context, tensor.data);
if (rc != QMCKL_SUCCESS) {
 return rc;
}

memset(&tensor, 0, sizeof(qmckl_tensor));

return QMCKL_SUCCESS;
}

Reshaping

Reshaping occurs in-place and the pointer to the data is copied.

Vector -> Matrix

qmckl_matrix
qmckl_matrix_of_vector(const qmckl_vector vector,
                   const int64_t size1,
                   const int64_t size2);

Reshapes a vector into a matrix.

qmckl_matrix
qmckl_matrix_of_vector(const qmckl_vector vector,
                   const int64_t size1,
                   const int64_t size2)
{ 
/* Always true */
assert (size1 * size2 == vector.size);

qmckl_matrix result;

result.size[0] = size1;
result.size[1] = size2;
result.data    = vector.data;

return result;
}

Vector -> Tensor

qmckl_tensor
qmckl_tensor_of_vector(const qmckl_vector vector,
                   const int64_t order,
                   const int64_t* size);

Reshapes a vector into a tensor.

qmckl_tensor
qmckl_tensor_of_vector(const qmckl_vector vector,
                   const int64_t order,
                   const int64_t* size)
{ 
qmckl_tensor result;

int64_t prod_size = 1;
for (int64_t i=0 ; i<order ; ++i) {
result.size[i] = size[i];
prod_size *= size[i];
}
assert (prod_size == vector.size);

result.data = vector.data;

return result;
}

Matrix -> Vector

qmckl_vector
qmckl_vector_of_matrix(const qmckl_matrix matrix);

Reshapes a matrix into a vector.

qmckl_vector
qmckl_vector_of_matrix(const qmckl_matrix matrix)
{ 
qmckl_vector result;

result.size = matrix.size[0] * matrix.size[1];
result.data = matrix.data;

return result;
}

Matrix -> Tensor

qmckl_tensor
qmckl_tensor_of_matrix(const qmckl_matrix matrix,
                   const int64_t order,
                   const int64_t* size);

Reshapes a matrix into a tensor.

qmckl_tensor
qmckl_tensor_of_matrix(const qmckl_matrix matrix,
                   const int64_t order,
                   const int64_t* size)
{ 
qmckl_tensor result;

int64_t prod_size = 1;
for (int64_t i=0 ; i<order ; ++i) {
result.size[i] = size[i];
prod_size *= size[i];
}
assert (prod_size == matrix.size[0] * matrix.size[1]);

result.data = matrix.data;

return result;
}

Tensor -> Vector

qmckl_vector
qmckl_vector_of_tensor(const qmckl_tensor tensor);

Reshapes a tensor into a vector.

qmckl_vector
qmckl_vector_of_tensor(const qmckl_tensor tensor)
{ 
int64_t prod_size = (int64_t) tensor.size[0];
for (int64_t i=1 ; i<tensor.order ; i++) {
prod_size *= tensor.size[i];
}

qmckl_vector result;

result.size = prod_size;
result.data = tensor.data;

return result;
}

Tensor -> Matrix

qmckl_matrix
qmckl_matrix_of_tensor(const qmckl_tensor tensor,
                   const int64_t size1,
                   const int64_t size2);

Reshapes a tensor into a vector.

qmckl_matrix
qmckl_matrix_of_tensor(const qmckl_tensor tensor,
                   const int64_t size1,
                   const int64_t size2)
{ 
/* Always true */
int64_t prod_size = (int64_t) 1;
for (int64_t i=0 ; i<tensor.order ; i++) {
prod_size *= tensor.size[i];
}
assert (prod_size == size1 * size2);

qmckl_matrix result;

result.size[0] = size1;
result.size[1] = size2;
result.data = tensor.data;

return result;
}

Access macros

#define qmckl_vec(v, i) v.data[i]
#define qmckl_mat(m, i, j) m.data[(i) + (j)*m.size[0]]

#define qmckl_ten3(t, i, j, k) t.data[(i) + m.size[0]*((j) + size[1]*(k))]
#define qmckl_ten4(t, i, j, k, l) t.data[(i) + m.size[0]*((j) + size[1]*((k) + size[2]*(l)))]
#define qmckl_ten5(t, i, j, k, l, m) t.data[(i) + m.size[0]*((j) + size[1]*((k) + size[2]*((l) + size[3]*(m))))]

Copy to/from to `double*`

qmckl_exit_code
qmckl_double_of_vector(const qmckl_context context,
                   const qmckl_vector vector,
                   double* const target,
                   const int64_t size_max);

Converts a vector to a double*.

qmckl_exit_code
qmckl_double_of_vector(const qmckl_context context,
                   const qmckl_vector vector,
                   double* const target,
                   const int64_t size_max)
{
/* Always true by construction */
assert (qmckl_context_check(context) != QMCKL_NULL_CONTEXT);
assert (vector.size > (int64_t) 0);
assert (target != NULL);
assert (size_max > (int64_t) 0);
assert (size_max >= vector.size);
for (int64_t i=0 ; i<vector.size ; ++i) {
target[i] = vector.data[i];
}
return QMCKL_SUCCESS;

}

qmckl_exit_code
qmckl_double_of_matrix(const qmckl_context context,
                   const qmckl_matrix matrix,
                   double* const target,
                   const int64_t size_max);

Converts a matrix to a double*.

qmckl_exit_code
qmckl_double_of_matrix(const qmckl_context context,
                   const qmckl_matrix matrix,
                   double* const target,
                   const int64_t size_max)
{
qmckl_vector vector = qmckl_vector_of_matrix(matrix);
return qmckl_double_of_vector(context, vector, target, size_max);
}

qmckl_exit_code
qmckl_double_of_tensor(const qmckl_context context,
                   const qmckl_tensor tensor,
                   double* const target,
                   const int64_t size_max);

Converts a tensor to a double*.

qmckl_exit_code
qmckl_double_of_tensor(const qmckl_context context,
                   const qmckl_tensor tensor,
                   double* const target,
                   const int64_t size_max)
{
qmckl_vector vector = qmckl_vector_of_tensor(tensor);
return qmckl_double_of_vector(context, vector, target, size_max);
}

qmckl_exit_code
qmckl_vector_of_double(const qmckl_context context,
                   const double* target,
                   const int64_t size_max,
                   qmckl_vector* vector);

Converts a double* to a vector.

qmckl_exit_code
qmckl_vector_of_double(const qmckl_context context,
                   const double* target,
                   const int64_t size_max,
                   qmckl_vector* vector_out)
{
qmckl_vector vector = *vector_out;
/* Always true by construction */
assert (qmckl_context_check(context) != QMCKL_NULL_CONTEXT);

if (vector.size == 0) {
return qmckl_failwith( context,
                       QMCKL_INVALID_ARG_4,
                       "qmckl_double_of_vector",
                       "Vector not allocated");
}

if (vector.size != size_max) {
  return qmckl_failwith( context,
                         QMCKL_INVALID_ARG_4,
                         "qmckl_double_of_vector",
                         "Wrong vector size");
}

for (int64_t i=0 ; i<vector.size ; ++i) {
vector.data[i] = target[i];
}

*vector_out = vector;
return QMCKL_SUCCESS;

}

qmckl_exit_code
qmckl_matrix_of_double(const qmckl_context context,
                   const double* target,
                   const int64_t size_max,
                   qmckl_matrix* matrix);

Converts a matrix to a double*.

qmckl_exit_code
qmckl_matrix_of_double(const qmckl_context context,
                   const double* target,
                   const int64_t size_max,
                   qmckl_matrix* matrix)
{
qmckl_vector vector = qmckl_vector_of_matrix(*matrix);
qmckl_exit_code rc =
qmckl_vector_of_double(context, target, size_max, &vector);
,*matrix = qmckl_matrix_of_vector(vector, matrix->size[0], matrix->size[1]);
return rc;
}

qmckl_exit_code
qmckl_tensor_of_double(const qmckl_context context,
                   const double* target,
                   const int64_t size_max,
                   qmckl_tensor* tensor);

Converts a matrix to a double*.

qmckl_exit_code
qmckl_tensor_of_double(const qmckl_context context,
                   const double* target,
                   const int64_t size_max,
                   qmckl_tensor* tensor)
{
qmckl_vector vector = qmckl_vector_of_tensor(*tensor);
qmckl_exit_code rc =
qmckl_vector_of_double(context, target, size_max, &vector);
*tensor = qmckl_tensor_of_vector(vector, tensor->order, tensor->size);
return rc;
}

Tests

{
int64_t m = 3;
int64_t n = 4;
int64_t p = m*n;
qmckl_vector vec = qmckl_vector_alloc(context, p);

for (int64_t i=0 ; i<p ; ++i) 
qmckl_vec(vec, i) = (double) i;

for (int64_t i=0 ; i<p ; ++i) 
assert( vec.data[i] == (double) i );

qmckl_matrix mat = qmckl_matrix_of_vector(vec, m, n);
assert (mat.size[0] == m);
assert (mat.size[1] == n);
assert (mat.data == vec.data);

for (int64_t j=0 ; j<n ; ++j)
for (int64_t i=0 ; i<m ; ++i)
  assert ( qmckl_mat(mat, i, j) == qmckl_vec(vec, i+j*m)) ;

qmckl_vector vec2 = qmckl_vector_of_matrix(mat);
assert (vec2.size == p);
assert (vec2.data == vec.data);
for (int64_t i=0 ; i<p ; ++i) 
  assert ( qmckl_vec(vec2, i) == qmckl_vec(vec, i) ) ;

qmckl_vector_free(context, vec);

}

Matrix operations

`qmckl_dgemm`

Matrix multiplication:

\[ C_{ij} = \beta C_{ij} + \alpha \sum_{k} A_{ik} \cdot B_{kj} \]

Variable	Type	In/Out	Description
`context`	`qmckl_context`	in	Global state
`TransA`	`char`	in	'T' is transposed
`TransB`	`char`	in	'T' is transposed
`m`	`int64_t`	in	Number of rows of the input matrix
`n`	`int64_t`	in	Number of columns of the input matrix
`k`	`int64_t`	in	Number of columns of the input matrix
`alpha`	`double`	in	α
`A`	`double[][lda]`	in	Array containing the matrix $A$
`lda`	`int64_t`	in	Leading dimension of array `A`
`B`	`double[][ldb]`	in	Array containing the matrix $B$
`ldb`	`int64_t`	in	Leading dimension of array `B`
`beta`	`double`	in	β
`C`	`double[][ldc]`	out	Array containing the matrix $C$
`ldc`	`int64_t`	in	Leading dimension of array `C`

Requirements:

context is not QMCKL_NULL_CONTEXT
m > 0
n > 0
k > 0
lda >= m
ldb >= n
ldc >= n
A is allocated with at least $m \times k \times 8$ bytes
B is allocated with at least $k \times n \times 8$ bytes
C is allocated with at least $m \times n \times 8$ bytes

qmckl_exit_code qmckl_dgemm (
      const qmckl_context context,
      const char TransA,
      const char TransB,
      const int64_t m,
      const int64_t n,
      const int64_t k,
      const double alpha,
      const double* A,
      const int64_t lda,
      const double* B,
      const int64_t ldb,
      const double beta,
      double* const C,
      const int64_t ldc );

integer function qmckl_dgemm_f(context, TransA, TransB, &
 m, n, k, alpha, A, LDA, B, LDB, beta, C, LDC) &
 result(info)
use qmckl
implicit none
integer(qmckl_context), intent(in)  :: context
character             , intent(in)  :: TransA, TransB
integer*8             , intent(in)  :: m, n, k
double precision      , intent(in)  :: alpha, beta
integer*8             , intent(in)  :: lda
double precision      , intent(in)  :: A(lda,*)
integer*8             , intent(in)  :: ldb
double precision      , intent(in)  :: B(ldb,*)
integer*8             , intent(in)  :: ldc
double precision      , intent(out) :: C(ldc,*)

info = QMCKL_SUCCESS

if (context == QMCKL_NULL_CONTEXT) then
 info = QMCKL_INVALID_CONTEXT
 return
endif

if (m <= 0_8) then
 info = QMCKL_INVALID_ARG_4
 return
endif

if (n <= 0_8) then
 info = QMCKL_INVALID_ARG_5
 return
endif

if (k <= 0_8) then
 info = QMCKL_INVALID_ARG_6
 return
endif

if (LDA <= 0) then
 info = QMCKL_INVALID_ARG_9
 return
endif

if (LDB <= 0) then
 info = QMCKL_INVALID_ARG_11
 return
endif

if (LDC <= 0) then
 info = QMCKL_INVALID_ARG_14
 return
endif

call dgemm(transA, transB, int(m,4), int(n,4), int(k,4), &
   alpha, A, int(LDA,4), B, int(LDB,4), beta, C, int(LDC,4))

end function qmckl_dgemm_f

`qmckl_matmul`

Matrix multiplication:

\[ C_{ij} = \beta C_{ij} + \alpha \sum_{k} A_{ik} \cdot B_{kj} \]

Variable	Type	In/Out	Description
`context`	`qmckl_context`	in	Global state
`TransA`	`char`	in	'T' is transposed
`TransB`	`char`	in	'T' is transposed
`alpha`	`double`	in	α
`A`	`qmckl_matrix`	in	Matrix $A$
`B`	`qmckl_matrix`	in	Matrix $B$
`beta`	`double`	in	β
`C`	`qmckl_matrix`	out	Matrix $C$

qmckl_exit_code
qmckl_matmul (const qmckl_context context,
          const char TransA,
          const char TransB,
          const double alpha,
          const qmckl_matrix A,
          const qmckl_matrix B,
          const double beta,
          qmckl_matrix* const C );

qmckl_exit_code
qmckl_matmul (const qmckl_context context,
          const char TransA,
          const char TransB,
          const double alpha,
          const qmckl_matrix A,
          const qmckl_matrix B,
          const double beta,
          qmckl_matrix* const C )
{
if (qmckl_context_check(context) == QMCKL_NULL_CONTEXT) {
return QMCKL_INVALID_CONTEXT;
}

qmckl_exit_code rc = QMCKL_SUCCESS;

if (TransA != 'N' && TransA != 'T') {
return qmckl_failwith( context,
                       QMCKL_INVALID_ARG_2,
                       "qmckl_matmul",
                       "TransA should be 'N' or 'T'");
}

if (TransB != 'N' && TransB != 'T') {
return qmckl_failwith( context,
                       QMCKL_INVALID_ARG_3,
                       "qmckl_matmul",
                       "TransB should be 'N' or 'T'");
}

if (A.size[0] < 1) {
return qmckl_failwith( context,
                       QMCKL_INVALID_ARG_5,
                       "qmckl_matmul",
                       "Invalid size for A");
}

if (B.size[0] < 1) {
return qmckl_failwith( context,
                       QMCKL_INVALID_ARG_6,
                       "qmckl_matmul",
                       "Invalid size for B");
}

if (C == NULL) {
return qmckl_failwith( context,
                       QMCKL_INVALID_ARG_8,
                       "qmckl_matmul",
                       "Null pointer");
}

int t = 0;
if (TransA == 'T') t +=1;
if (TransB == 'T') t +=2;
/*
| t | TransA | TransB |
+---+--------+--------+
| 0 | N      | N      |
| 1 | T      | N      |
| 2 | N      | T      |
| 3 | T      | T      |
,*/

switch (t) {
case 0:
if (A.size[1] != B.size[0]) {
  return qmckl_failwith( context,
                         QMCKL_INVALID_ARG_2,
                         "qmckl_matmul",
                         "A and B have incompatible dimensions");
}
C->size[0] = A.size[0];
C->size[1] = B.size[1];
rc = qmckl_dgemm (context, 'N', 'N',
                  C->size[0], C->size[1], A.size[1],
                  alpha,
                  A.data, A.size[0],
                  B.data, B.size[0],
                  beta,
                  C->data, C->size[0]);
break;
case 1:
if (A.size[0] != B.size[0]) { 
  return qmckl_failwith( context,
                         QMCKL_INVALID_ARG_2,
                         "qmckl_matmul",
                         "A and B have incompatible dimensions");
}
C->size[0] = A.size[1];
C->size[1] = B.size[1];
rc = qmckl_dgemm (context, 'T', 'N',
                  C->size[0], C->size[1], A.size[0],
                  alpha,
                  A.data, A.size[0],
                  B.data, B.size[0],
                  beta,
                  C->data, C->size[0]);
break;
case 2:
if (A.size[1] != B.size[1]) {
  return qmckl_failwith( context,
                         QMCKL_INVALID_ARG_2,
                         "qmckl_matmul",
                         "A and B have incompatible dimensions");
}
C->size[0] = A.size[0];
C->size[1] = B.size[0];
rc = qmckl_dgemm (context, 'N', 'T',
                  C->size[0], C->size[1], A.size[1],
                  alpha,
                  A.data, A.size[0],
                  B.data, B.size[0],
                  beta,
                  C->data, C->size[0]);
break;
case 3:
if (A.size[0] != B.size[1]) {
  return qmckl_failwith( context,
                         QMCKL_INVALID_ARG_2,
                         "qmckl_matmul",
                         "A and B have incompatible dimensions");
}
C->size[0] = A.size[1];
C->size[1] = B.size[0];
rc = qmckl_dgemm (context, 'T', 'T',
                  C->size[0], C->size[1], A.size[0],
                  alpha,
                  A.data, A.size[0],
                  B.data, B.size[0],
                  beta,
                  C->data, C->size[0]);
break;
}
return rc;
}

`qmckl_adjugate`

Given a matrix $\mathbf{A}$, the adjugate matrix $\text{adj}(\mathbf{A})$ is the transpose of the cofactors matrix of $\mathbf{A}$.

\[ \mathbf{B} = \text{adj}(\mathbf{A}) = \text{det}(\mathbf{A}) \, \mathbf{A}^{-1} \]

Variable	Type	In/Out	Description
`context`	`qmckl_context`	in	Global state
`n`	`int64_t`	in	Number of rows and columns of the input matrix
`A`	`double[][lda]`	in	Array containing the $n \times n$ matrix $A$
`lda`	`int64_t`	in	Leading dimension of array `A`
`B`	`double[][ldb]`	out	Adjugate of $A$
`ldb`	`int64_t`	in	Leading dimension of array `B`
`det_l`	`double`	inout	determinant of $A$

Requirements:

context is not QMCKL_NULL_CONTEXT
n > 0
lda >= m
A is allocated with at least $m \times m \times 8$ bytes
ldb >= m
B is allocated with at least $m \times m \times 8$ bytes

qmckl_exit_code qmckl_adjugate (
      const qmckl_context context,
      const int64_t n,
      const double* A,
      const int64_t lda,
      double* const B,
      const int64_t ldb,
      double* det_l );

For small matrices (≤ 5× 5), we use specialized routines for performance motivations. For larger sizes, we rely on the LAPACK library.

integer function qmckl_adjugate_f(context, na, A, LDA, B, ldb, det_l) &
 result(info)
use qmckl
implicit none
integer(qmckl_context)  , intent(in)  :: context
double precision, intent(in)          :: A (LDA,*)
integer*8, intent(in)                 :: LDA
double precision, intent(out)         :: B (LDB,*)
integer*8, intent(in)                 :: LDB
integer*8, intent(in)                 :: na
double precision, intent(inout)       :: det_l

integer    :: i,j

info = QMCKL_SUCCESS

if (context == QMCKL_NULL_CONTEXT) then
 info = QMCKL_INVALID_CONTEXT
 return
endif

if (na <= 0_8) then
 info = QMCKL_INVALID_ARG_2
 return
endif

if (LDA <= 0_8) then
 info = QMCKL_INVALID_ARG_4
 return
endif

if (LDA < na) then
 info = QMCKL_INVALID_ARG_4
 return
endif

select case (na)
case (5)
 call adjugate5(A,LDA,B,LDB,na,det_l)
case (4)
 call adjugate4(A,LDA,B,LDB,na,det_l)
case (3)
 call adjugate3(A,LDA,B,LDB,na,det_l)
case (2)
 call adjugate2(A,LDA,B,LDB,na,det_l)
case (1)
det_l = a(1,1)
b(1,1) = 1.d0
case default
 call adjugate_general(context, na, A, LDA, B, LDB, det_l)
end select

end function qmckl_adjugate_f

subroutine adjugate_general(context, na, A, LDA, B, LDB, det_l)
use qmckl
implicit none
integer(qmckl_context), intent(in) :: context
double precision, intent(in)       :: A (LDA,na)
integer*8, intent(in)              :: LDA
double precision, intent(out)      :: B (LDB,na)
integer*8, intent(in)              :: LDB
integer*8, intent(in)              :: na
double precision, intent(inout)    :: det_l

double precision :: work(LDA*max(na,64))
integer          :: inf
integer          :: ipiv(LDA)
integer          :: lwork
integer(8)       :: i, j

We first copy the array A into array B.

B(1:na,1:na) = A(1:na,1:na)

Then, we compute the LU factorization $LU=A$ using the dgetrf routine.

call dgetrf(na, na, B, LDB, ipiv, inf )

By convention, the determinant of $L$ is equal to one, so the determinant of $A$ is equal to the determinant of $U$, which is simply computed as the product of its diagonal elements.

det_l = 1.d0
j=0_8
do i=1,na
j = j+min(abs(ipiv(i)-i),1)
det_l = det_l*B(i,i)
enddo

As dgetrf returns $PLU=A$ where $P$ is a permutation matrix, the sign of the determinant is computed as $-1^m$ where $m$ is the number of permutations.

if (iand(j,1_8) /= 0_8)  then
det_l = -det_l
endif

Then, the inverse of $A$ is computed using dgetri:

lwork = SIZE(work)
call dgetri(na, B, LDB, ipiv, work, lwork, inf )

and the adjugate matrix is computed as the product of the determinant with the inverse:

B(:,:) = B(:,:)*det_l

end subroutine adjugate_general

`qmckl_transpose`

Transposes a matrix: $A^\dagger_{ji} = A_{ij}$.

Variable	Type	In/Out	Description
context	qmckl_context	in	Global state
A	qmckl_matrix	in	Input matrix
At	qmckl_matrix	out	Transposed matrix

qmckl_exit_code
qmckl_transpose (qmckl_context context,
             const qmckl_matrix A,
             qmckl_matrix At );

qmckl_exit_code
qmckl_transpose (qmckl_context context,
             const qmckl_matrix A,
             qmckl_matrix At )
{
if (qmckl_context_check(context) == QMCKL_NULL_CONTEXT) {
return QMCKL_INVALID_CONTEXT;
}

if (A.size[0] < 1) {
return qmckl_failwith( context,
                       QMCKL_INVALID_ARG_2,
                       "qmckl_transpose",
                       "Invalid size for A");
}

if (At.data == NULL) {
return qmckl_failwith( context,
                       QMCKL_INVALID_ARG_3,
                       "qmckl_transpose",
                       "Output matrix not allocated");
}

if (At.size[0] != A.size[1] || At.size[1] != A.size[0]) {
return qmckl_failwith( context,
                       QMCKL_INVALID_ARG_3,
                       "qmckl_transpose",
                       "Invalid size for At");
}

for (int64_t j=0 ; j<At.size[1] ; ++j) 
for (int64_t i=0 ; i<At.size[0] ; ++i) 
  qmckl_mat(At, i, j) = qmckl_mat(A, j, i);

return QMCKL_SUCCESS;
}

Test

{
qmckl_matrix A;
qmckl_matrix At;
A  = qmckl_matrix_alloc(context, 2, 3);
At = qmckl_matrix_alloc(context, 3, 2);
for (int j=0 ; j<3 ; ++j)
for (int i=0 ; i<2 ; ++i)
 qmckl_mat(A, i, j) = (double) 10*i+j;

qmckl_exit_code rc = qmckl_transpose(context, A, At);
assert(rc == QMCKL_SUCCESS);

assert(A.size[0] == At.size[1]);
assert(A.size[1] == At.size[0]);
for (int j=0 ; j<3 ; ++j)
for (int i=0 ; i<2 ; ++i)
 assert (qmckl_mat(A, i, j) == qmckl_mat(At, j, i));

qmckl_matrix_free(context, A);
qmckl_matrix_free(context, At);
}

73 KiB Raw Blame History Unescape Escape

BLAS functions

Data types

Vector

Matrix

Tensor

Reshaping

Vector -> Matrix

Vector -> Tensor

Matrix -> Vector

Matrix -> Tensor

Tensor -> Vector

Tensor -> Matrix

Access macros

Copy to/from to double*

Tests

Matrix operations

qmckl_dgemm

qmckl_matmul

qmckl_adjugate

qmckl_transpose

Test

73 KiB

Raw Blame History

Copy to/from to `double*`

`qmckl_dgemm`

`qmckl_matmul`

`qmckl_adjugate`

`qmckl_transpose`