distributedsparsematrixalgebramodule Module Reference

A Module For Performing Distributed Sparse Matrix Algebra Operations. More...

Functions/Subroutines
subroutine, public	computesigma (this, sigma_value)
	Compute sigma for the inversion method. See [9] for details. More...
subroutine, public	distributedgemm (matA, matB, matC, alpha_in, beta_in, threshold_in, memory_pool_in)
	Multiply two matrices together, and add to the third. C := alpha*matA*matB+ beta*matC. More...
real(ntreal) function, public	distributedgrandsum (matA)
	Sum up the elements in a matrix into a single value. More...
subroutine, public	distributedpairwisemultiply (matA, matB, matC)
	Elementwise multiplication. C_ij = A_ij * B_ij. Also known as a Hadamard product. More...
real(ntreal) function, public	distributedsparsenorm (this)
	Compute the norm of a distributed sparse matrix along the rows. More...
real(ntreal) function, public	dotdistributedsparsematrix (matA, matB)
	product = dot(Matrix A,Matrix B) Note that a dot product is the sum of elementwise multiplication, not traditional matrix multiplication. More...
subroutine, public	incrementdistributedsparsematrix (matA, matB, alpha_in, threshold_in)
	Matrix B = alpha*Matrix A + Matrix B (AXPY) This will utilize the sparse vector increment routine. More...
subroutine, public	scaledistributedsparsematrix (this, constant)
	Will scale a distributed sparse matrix by a constant. More...
real(ntreal) function, public	trace (this)
	Compute the trace of the matrix. More...

Detailed Description

A Module For Performing Distributed Sparse Matrix Algebra Operations.

Function/Subroutine Documentation

computesigma()

subroutine, public distributedsparsematrixalgebramodule::computesigma	(	type(distributedsparsematrix_t), intent(in)	this,
		real(ntreal), intent(out)	sigma_value
	)

Compute sigma for the inversion method. See [9] for details.

Parameters

[in]	this	the matrix to compute the sigma value of.
[out]	sigma_value	sigma.

distributedgemm()

subroutine, public distributedsparsematrixalgebramodule::distributedgemm	(	type(distributedsparsematrix_t), intent(in)	matA,
		type(distributedsparsematrix_t), intent(in)	matB,
		type(distributedsparsematrix_t), intent(inout)	matC,
		real(ntreal), intent(in), optional	alpha_in,
		real(ntreal), intent(in), optional	beta_in,
		real(ntreal), intent(in), optional	threshold_in,
		type(distributedmatrixmemorypool_t), intent(inout), optional	memory_pool_in
	)

Multiply two matrices together, and add to the third. C := alpha*matA*matB+ beta*matC.

Parameters

[in]	matA	Matrix A
[in]	matB	Matrix B
[out]	matC	= alpha*matA*matB + beta*matC
[in]	alpha_in	scales the multiplication
[in]	beta_in	scales matrix we sum on to
[in]	threshold_in	for flushing values to zero (Optional, default=0).
[in,out]	memory_pool_in	a memory pool for the calculation (Optional).

distributedgrandsum()

real(ntreal) function, public distributedsparsematrixalgebramodule::distributedgrandsum

(

type(distributedsparsematrix_t), intent(in)

matA

)

Sum up the elements in a matrix into a single value.

Parameters

[in]

matA

Matrix A.

Returns

sum the sum of all elements.

distributedpairwisemultiply()

subroutine, public distributedsparsematrixalgebramodule::distributedpairwisemultiply	(	type(distributedsparsematrix_t), intent(in)	matA,
		type(distributedsparsematrix_t), intent(in)	matB,
		type(distributedsparsematrix_t), intent(inout)	matC
	)

Elementwise multiplication. C_ij = A_ij * B_ij. Also known as a Hadamard product.

Parameters

[in]	matA	Matrix A.
[in]	matB	Matrix B.
[in,out]	matC	= MatA mult MatB.

distributedsparsenorm()

real(ntreal) function, public distributedsparsematrixalgebramodule::distributedsparsenorm

(

type(distributedsparsematrix_t), intent(in)

this

)

Compute the norm of a distributed sparse matrix along the rows.

Parameters

[in]

this

the matrix to compute the norm of.

Returns

the norm value of the full distributed sparse matrix.

dotdistributedsparsematrix()

real(ntreal) function, public distributedsparsematrixalgebramodule::dotdistributedsparsematrix	(	type(distributedsparsematrix_t), intent(in)	matA,
		type(distributedsparsematrix_t), intent(in)	matB
	)

product = dot(Matrix A,Matrix B) Note that a dot product is the sum of elementwise multiplication, not traditional matrix multiplication.

Parameters

[in]	matA	Matrix A.
[in,out]	matB	Matrix B.

Returns

product the dot product.

incrementdistributedsparsematrix()

subroutine, public distributedsparsematrixalgebramodule::incrementdistributedsparsematrix	(	type(distributedsparsematrix_t), intent(in)	matA,
		type(distributedsparsematrix_t), intent(inout)	matB,
		real(ntreal), intent(in), optional	alpha_in,
		real(ntreal), intent(in), optional	threshold_in
	)

Matrix B = alpha*Matrix A + Matrix B (AXPY) This will utilize the sparse vector increment routine.

Parameters

[in]	matA	Matrix A.
[in,out]	matB	Matrix B.
[in]	alpha_in	multiplier. (Optional, default= 1.0)
[in]	threshold_in	for flushing values to zero. (Optional, default=0)

scaledistributedsparsematrix()

subroutine, public distributedsparsematrixalgebramodule::scaledistributedsparsematrix	(	type(distributedsparsematrix_t), intent(inout)	this,
		real(ntreal), intent(in)	constant
	)

Will scale a distributed sparse matrix by a constant.

Parameters

[in,out]	this	Matrix to scale.
[in]	constant	scale factor.

trace()

real(ntreal) function, public distributedsparsematrixalgebramodule::trace

(

type(distributedsparsematrix_t), intent(in)

this

)

Compute the trace of the matrix.

Parameters

[in]

this

the matrix to compute the trace of.

Returns

the trace value of the full distributed sparse matrix.