blas.c File Reference

Basic linear algebra functions. More...

#include "blas.h"

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Functions
static void	gemm_nn (const int M, const int N, const int K, const double ALPHA, const double *A, const int lda, const double *B, const int ldb, double *C, const int ldc)
static void	gemm_nt (const int M, const int N, const int K, const double ALPHA, const double *A, const int lda, const double *B, const int ldb, double *C, const int ldc)
static void	gemm_tn (const int M, const int N, const int K, const double ALPHA, const double *A, const int lda, const double *B, const int ldb, double *C, const int ldc)
static void	gemm_tt (const int M, const int N, const int K, const double ALPHA, const double *A, const int lda, const double *B, const int ldb, double *C, const int ldc)
void	blas_gemm (const int TA, const int TB, const int M, const int N, const int K, const double ALPHA, const double *A, const int lda, const double *B, const int ldb, const double BETA, double *C, const int ldc)
	Performs the matrix-matrix multiplication: \( C = \alpha \mbox{op}(A) \mbox{op}(B) + \beta C \). More...
void	blas_axpy (const int N, const double ALPHA, const double *X, const int INCX, double *Y, const int INCY)
	Multiplies vector X by the scalar ALPHA and adds it to the vector Y. More...
void	blas_scal (const int N, const double ALPHA, double *X, const int INCX)
	Scales vector X by the scalar ALPHA and overwrites it with the result. More...
void	blas_fill (const int N, const double ALPHA, double *X, const int INCX)
	Fills the vector X with the value ALPHA. More...
double	blas_dot (const int N, const double *X, const int INCX, const double *Y, const int INCY)
	Computes the dot product of two vectors. More...
void	blas_mul (const int N, const double *X, const int INCX, double *Y, const int INCY)
	Multiplies vector X by the vector Y and stores the result in vector Y. More...
double	blas_sum (const double *X, const int N)
	Returns the sum of the vector X. More...

Detailed Description

Basic linear algebra functions.

Author

Richard Preen rpree.nosp@m.n@gm.nosp@m.ail.c.nosp@m.om

Copyright

The Authors.

Date

2020.

Definition in file blas.c.

Function Documentation

blas_axpy()

void blas_axpy	(	const int	N,
		const double	ALPHA,
		const double *	X,
		const int	INCX,
		double *	Y,
		const int	INCY
	)

Multiplies vector X by the scalar ALPHA and adds it to the vector Y.

Parameters

[in]	N	The number of elements in vectors X and Y.
[in]	ALPHA	Scalar used for multiplication.
[in]	X	Vector with N elements.
[in]	INCX	Stride between consecutive elements of X.
[in,out]	Y	Vector with N elements.
[in]	INCY	Stride between consecutive elements of Y.

Definition at line 138 of file blas.c.

Referenced by neural_layer_connected_backward(), neural_layer_connected_update(), neural_layer_convolutional_update(), neural_layer_lstm_backward(), neural_layer_lstm_forward(), neural_layer_recurrent_forward(), pred_nlms_update(), and pred_rls_update().

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blas_dot()

double blas_dot	(	const int	N,
		const double *	X,
		const int	INCX,
		const double *	Y,
		const int	INCY
	)

Computes the dot product of two vectors.

Parameters

[in]	N	The number of elements in vectors X and Y.
[in]	X	Vector with N elements.
[in]	INCX	Stride between consecutive elements of X.
[in]	Y	Vector with N elements.
[in]	INCY	Stride between consecutive elements of Y.

Returns

The resulting dot product.

Definition at line 198 of file blas.c.

Referenced by pred_nlms_compute(), pred_nlms_update(), pred_rls_compute(), and pred_rls_update().

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blas_fill()

void blas_fill	(	const int	N,
		const double	ALPHA,
		double *	X,
		const int	INCX
	)

Fills the vector X with the value ALPHA.

Parameters

[in]	N	The number of elements in vector X.
[in]	ALPHA	The value to fill the vector.
[out]	X	Vector with N elements.
[in]	INCX	Stride between consecutive elements of X.

Definition at line 181 of file blas.c.

Referenced by pred_nlms_init(), and pred_rls_init().

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blas_gemm()

void blas_gemm	(	const int	TA,
		const int	TB,
		const int	M,
		const int	N,
		const int	K,
		const double	ALPHA,
		const double *	A,
		const int	lda,
		const double *	B,
		const int	ldb,
		const double	BETA,
		double *	C,
		const int	ldc
	)

Performs the matrix-matrix multiplication: \( C = \alpha \mbox{op}(A) \mbox{op}(B) + \beta C \).

Parameters

[in]	TA	Operation op(A) that is non- or (conj.) transpose.
[in]	TB	Operation op(B) that is non- or (conj.) transpose.
[in]	M	Number of rows of matrix op(A) and C.
[in]	N	Number of columns of matrix op(B) and C.
[in]	K	Number of columns of op(A) and rows of op(B).
[in]	ALPHA	Scalar used for multiplication.
[in]	A	Array of dimension lda × K with lda >= max(1,M) if TA=0 and lda × M with lda >= max(1,K) otherwise.
[in]	lda	Leading dimension of a 2-D array used to store the matrix A.
[in]	B	Array of dimension ldb × N with ldb >= max(1,K) if TB=0 and ldb × K with ldb >= max(1,N) otherwise.
[in]	ldb	Leading dimension of a 2-D array used to store the matrix B.
[in]	BETA	Scalar used for multiplication.
[in,out]	C	Array of dimension ldc × N with ldc >= max(1,M).
[in]	ldc	Leading dimension of a 2-D array used to store the matrix C.

Definition at line 108 of file blas.c.

References gemm_nn(), gemm_nt(), gemm_tn(), and gemm_tt().

Referenced by neural_layer_connected_backward(), neural_layer_connected_forward(), neural_layer_convolutional_backward(), neural_layer_convolutional_forward(), and pred_rls_update().

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blas_mul()

void blas_mul	(	const int	N,
		const double *	X,
		const int	INCX,
		double *	Y,
		const int	INCY
	)

Multiplies vector X by the vector Y and stores the result in vector Y.

Parameters

[in]	N	The number of elements in vectors X and Y.
[in]	X	Vector with N elements.
[in]	INCX	Stride between consecutive elements of X.
[in,out]	Y	Vector with N elements.
[in]	INCY	Stride between consecutive elements of Y.

Definition at line 217 of file blas.c.

Referenced by neural_layer_lstm_backward(), and neural_layer_lstm_forward().

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blas_scal()

void blas_scal	(	const int	N,
		const double	ALPHA,
		double *	X,
		const int	INCX
	)

Scales vector X by the scalar ALPHA and overwrites it with the result.

Parameters

[in]	N	The number of elements in vector X.
[in]	ALPHA	Scalar used for multiplication.
[in,out]	X	Vector with N elements.
[in]	INCX	Stride between consecutive elements of X.

Definition at line 160 of file blas.c.

Referenced by neural_layer_connected_update(), neural_layer_convolutional_update(), and pred_rls_update().

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blas_sum()

double blas_sum	(	const double *	X,
		const int	N
	)

Returns the sum of the vector X.

Parameters

[in]	X	Vector with N elements.
[in]	N	The number of elements in vector X.

Returns

The resulting sum.

Definition at line 232 of file blas.c.

Referenced by neural_layer_convolutional_backward().

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gemm_nn()

static void gemm_nn ( const int  M, const int  N, const int  K, const double  ALPHA, const double *  A, const int  lda, const double *  B, const int  ldb, double *  C, const int  ldc  )

static

Definition at line 27 of file blas.c.

Referenced by blas_gemm().

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gemm_nt()

static void gemm_nt ( const int  M, const int  N, const int  K, const double  ALPHA, const double *  A, const int  lda, const double *  B, const int  ldb, double *  C, const int  ldc  )

static

Definition at line 42 of file blas.c.

Referenced by blas_gemm().

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gemm_tn()

static void gemm_tn ( const int  M, const int  N, const int  K, const double  ALPHA, const double *  A, const int  lda, const double *  B, const int  ldb, double *  C, const int  ldc  )

static

Definition at line 58 of file blas.c.

Referenced by blas_gemm().

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gemm_tt()

static void gemm_tt ( const int  M, const int  N, const int  K, const double  ALPHA, const double *  A, const int  lda, const double *  B, const int  ldb, double *  C, const int  ldc  )

static

Definition at line 73 of file blas.c.

Referenced by blas_gemm().

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