Visible to Intel only — GUID: GUID-118DC64D-62BB-4B91-9E76-562387E86B79
Getting Help and Support
What's New
Notational Conventions
Overview
OpenMP* Offload
BLAS and Sparse BLAS Routines
LAPACK Routines
ScaLAPACK Routines
Sparse Solver Routines
Extended Eigensolver Routines
Vector Mathematical Functions
Statistical Functions
Fourier Transform Functions
PBLAS Routines
Partial Differential Equations Support
Nonlinear Optimization Problem Solvers
Support Functions
BLACS Routines
Data Fitting Functions
Appendix A: Linear Solvers Basics
Appendix B: Routine and Function Arguments
Appendix C: FFTW Interface to Intel® Math Kernel Library
Appendix D: Code Examples
Appendix F: oneMKL Functionality
Bibliography
Glossary
Notices and Disclaimers
mkl_?csrgemv
mkl_?bsrgemv
mkl_?coogemv
mkl_?diagemv
mkl_?csrsymv
mkl_?bsrsymv
mkl_?coosymv
mkl_?diasymv
mkl_?csrtrsv
mkl_?bsrtrsv
mkl_?cootrsv
mkl_?diatrsv
mkl_cspblas_?csrgemv
mkl_cspblas_?bsrgemv
mkl_cspblas_?coogemv
mkl_cspblas_?csrsymv
mkl_cspblas_?bsrsymv
mkl_cspblas_?coosymv
mkl_cspblas_?csrtrsv
mkl_cspblas_?bsrtrsv
mkl_cspblas_?cootrsv
mkl_?csrmv
mkl_?bsrmv
mkl_?cscmv
mkl_?coomv
mkl_?csrsv
mkl_?bsrsv
mkl_?cscsv
mkl_?coosv
mkl_?csrmm
mkl_?bsrmm
mkl_?cscmm
mkl_?coomm
mkl_?csrsm
mkl_?cscsm
mkl_?coosm
mkl_?bsrsm
mkl_?diamv
mkl_?skymv
mkl_?diasv
mkl_?skysv
mkl_?diamm
mkl_?skymm
mkl_?diasm
mkl_?skysm
mkl_?dnscsr
mkl_?csrcoo
mkl_?csrbsr
mkl_?csrcsc
mkl_?csrdia
mkl_?csrsky
mkl_?csradd
mkl_?csrmultcsr
mkl_?csrmultd
Naming Conventions in Inspector-Executor Sparse BLAS Routines
Sparse Matrix Storage Formats for Inspector-executor Sparse BLAS Routines
Supported Inspector-executor Sparse BLAS Operations
Two-stage Algorithm in Inspector-Executor Sparse BLAS Routines
Matrix Manipulation Routines
Inspector-Executor Sparse BLAS Analysis Routines
Inspector-Executor Sparse BLAS Execution Routines
mkl_sparse_?_create_csr
mkl_sparse_?_create_csc
mkl_sparse_?_create_coo
mkl_sparse_?_create_bsr
mkl_sparse_copy
mkl_sparse_destroy
mkl_sparse_convert_csr
mkl_sparse_convert_bsr
mkl_sparse_?_export_csr
mkl_sparse_?_export_csc
mkl_sparse_?_export_bsr
mkl_sparse_?_set_value
mkl_sparse_?_update_values
mkl_sparse_order
mkl_sparse_?_lu_smoother
mkl_sparse_?_mv
mkl_sparse_?_trsv
mkl_sparse_?_mm
mkl_sparse_?_trsm
mkl_sparse_?_add
mkl_sparse_spmm
mkl_sparse_?_spmmd
mkl_sparse_sp2m
mkl_sparse_?_sp2md
mkl_sparse_sypr
mkl_sparse_?_syprd
mkl_sparse_?_symgs
mkl_sparse_?_symgs_mv
mkl_sparse_syrk
mkl_sparse_?_syrkd
mkl_sparse_?_dotmv
mkl_sparse_?_sorv
cblas_?axpy_batch
cblas_?axpy_batch_strided
cblas_?axpby
cblas_?copy_batch
cblas_?copy_batch_strided
cblas_?gemmt
cblas_?gemm3m
cblas_?gemm_batch
cblas_?gemm_batch_strided
cblas_?gemm3m_batch_strided
cblas_?gemm3m_batch
cblas_?trsm_batch
cblas_?trsm_batch_strided
mkl_?imatcopy
mkl_?imatcopy_batch
mkl_?imatcopy_batch_strided
mkl_?omatadd_batch_strided
mkl_?omatcopy
mkl_?omatcopy_batch
mkl_?omatcopy_batch_strided
mkl_?omatcopy2
mkl_?omatadd
cblas_?gemm_pack_get_size, cblas_gemm_*_pack_get_size
cblas_?gemm_pack
cblas_gemm_*_pack
cblas_?gemm_compute
cblas_gemm_*_compute
cblas_gemm_bf16bf16f32_compute
cblas_gemm_bf16bf16f32
cblas_gemm_f16f16f32_compute
cblas_gemm_f16f16f32
cblas_?gemm_free
cblas_gemm_*
cblas_?gemv_batch_strided
cblas_?gemv_batch
cblas_?dgmm_batch_strided
cblas_?dgmm_batch
mkl_jit_create_?gemm
mkl_jit_get_?gemm_ptr
mkl_jit_destroy
C Interface Conventions for LAPACK Routines
Matrix Layout for LAPACK Routines
Matrix Storage Schemes for LAPACK Routines
Mathematical Notation for LAPACK Routines
Error Analysis
LAPACK Linear Equation Routines
LAPACK Least Squares and Eigenvalue Problem Routines
LAPACK Auxiliary Routines
LAPACK Utility Functions and Routines
LAPACK Test Functions and Routines
Additional LAPACK Routines (Included for Compatibility with Netlib LAPACK)
Matrix Factorization: LAPACK Computational Routines
Solving Systems of Linear Equations: LAPACK Computational Routines
Estimating the Condition Number: LAPACK Computational Routines
Refining the Solution and Estimating Its Error: LAPACK Computational Routines
Matrix Inversion: LAPACK Computational Routines
Matrix Equilibration: LAPACK Computational Routines
Orthogonal Factorizations: LAPACK Computational Routines
Singular Value Decomposition: LAPACK Computational Routines
Symmetric Eigenvalue Problems: LAPACK Computational Routines
Generalized Symmetric-Definite Eigenvalue Problems: LAPACK Computational Routines
Nonsymmetric Eigenvalue Problems: LAPACK Computational Routines
Generalized Nonsymmetric Eigenvalue Problems: LAPACK Computational Routines
Generalized Singular Value Decomposition: LAPACK Computational Routines
Cosine-Sine Decomposition: LAPACK Computational Routines
Linear Least Squares (LLS) Problems: LAPACK Driver Routines
Generalized Linear Least Squares (LLS) Problems: LAPACK Driver Routines
Symmetric Eigenvalue Problems: LAPACK Driver Routines
Nonsymmetric Eigenvalue Problems: LAPACK Driver Routines
Singular Value Decomposition: LAPACK Driver Routines
Cosine-Sine Decomposition: LAPACK Driver Routines
Generalized Symmetric Definite Eigenvalue Problems: LAPACK Driver Routines
Generalized Nonsymmetric Eigenvalue Problems: LAPACK Driver Routines
?lacgv
?lacrm
?syconv
?syr
i?max1
?sum1
?gelq2
?geqr2
?geqrt2
?geqrt3
?getf2
?lacn2
?lacpy
?lakf2
?lange
?lansy
?lanhe
?lantr
LAPACKE_set_nancheck
LAPACKE_get_nancheck
?lapmr
?lapmt
?lapy2
?lapy3
?laran
?larfb
?larfg
?larft
?larfx
?large
?larnd
?larnv
?laror
?larot
?lartgp
?lartgs
?lascl
?lasd0
?lasd1
?lasd2
?lasd3
?lasd4
?lasd5
?lasd6
?lasd7
?lasd8
?lasd9
?lasda
?lasdq
?lasdt
?laset
?lasrt
?laswp
?latm1
?latm2
?latm3
?latm5
?latm6
?latme
?latmr
?lauum
?syswapr
?heswapr
?sfrk
?hfrk
?tfsm
?tfttp
?tfttr
?tpqrt2
?tprfb
?tpttf
?tpttr
?trttf
?trttp
?lacp2
?larcm
mkl_?tppack
mkl_?tpunpack
Systems of Linear Equations: ScaLAPACK Computational Routines
Matrix Factorization: ScaLAPACK Computational Routines
Solving Systems of Linear Equations: ScaLAPACK Computational Routines
Estimating the Condition Number: ScaLAPACK Computational Routines
Refining the Solution and Estimating Its Error: ScaLAPACK Computational Routines
Matrix Inversion: ScaLAPACK Computational Routines
Matrix Equilibration: ScaLAPACK Computational Routines
Orthogonal Factorizations: ScaLAPACK Computational Routines
Symmetric Eigenvalue Problems: ScaLAPACK Computational Routines
Nonsymmetric Eigenvalue Problems: ScaLAPACK Computational Routines
Singular Value Decomposition: ScaLAPACK Driver Routines
Generalized Symmetric-Definite Eigenvalue Problems: ScaLAPACK Computational Routines
p?lacgv
p?max1
pilaver
pmpcol
pmpim2
?combamax1
p?sum1
p?dbtrsv
p?dttrsv
p?gebal
p?gebd2
p?gehd2
p?gelq2
p?geql2
p?geqr2
p?gerq2
p?getf2
p?labrd
p?lacon
p?laconsb
p?lacp2
p?lacp3
p?lacpy
p?laevswp
p?lahrd
p?laiect
p?lamve
p?lange
p?lanhs
p?lansy, p?lanhe
p?lantr
p?lapiv
p?lapv2
p?laqge
p?laqr0
p?laqr1
p?laqr2
p?laqr3
p?laqr5
p?laqsy
p?lared1d
p?lared2d
p?larf
p?larfb
p?larfc
p?larfg
p?larft
p?larz
p?larzb
p?larzc
p?larzt
p?lascl
p?lase2
p?laset
p?lasmsub
p?lasrt
p?lassq
p?laswp
p?latra
p?latrd
p?latrs
p?latrz
p?lauu2
p?lauum
p?lawil
p?org2l/p?ung2l
p?org2r/p?ung2r
p?orgl2/p?ungl2
p?orgr2/p?ungr2
p?orm2l/p?unm2l
p?orm2r/p?unm2r
p?orml2/p?unml2
p?ormr2/p?unmr2
p?pbtrsv
p?pttrsv
p?potf2
p?rot
p?rscl
p?sygs2/p?hegs2
p?sytd2/p?hetd2
p?trord
p?trsen
p?trti2
?lahqr2
?lamsh
?lapst
?laqr6
?lar1va
?laref
?larrb2
?larrd2
?larre2
?larre2a
?larrf2
?larrv2
?lasorte
?lasrt2
?stegr2
?stegr2a
?stegr2b
?stein2
?dbtf2
?dbtrf
?dttrf
?dttrsv
?pttrsv
?steqr2
?trmvt
pilaenv
pilaenvx
pjlaenv
Additional ScaLAPACK Routines
oneMKL PARDISO - Parallel Direct Sparse Solver Interface
Parallel Direct Sparse Solver for Clusters Interface
Direct Sparse Solver (DSS) Interface Routines
Iterative Sparse Solvers based on Reverse Communication Interface (RCI ISS)
Preconditioners based on Incomplete LU Factorization Technique
Sparse Matrix Checker Routines
pardiso
pardisoinit
pardiso_64
mkl_pardiso_pivot
pardiso_getdiag
pardiso_export
pardiso_handle_store
pardiso_handle_restore
pardiso_handle_delete
pardiso_handle_store_64
pardiso_handle_restore_64
pardiso_handle_delete_64
oneMKL PARDISO Parameters in Tabular Form
pardiso iparm Parameter
PARDISO_DATA_TYPE
vslNewStream
vslNewStreamEx
vsliNewAbstractStream
vsldNewAbstractStream
vslsNewAbstractStream
vslDeleteStream
vslCopyStream
vslCopyStreamState
vslSaveStreamF
vslLoadStreamF
vslSaveStreamM
vslLoadStreamM
vslGetStreamSize
vslLeapfrogStream
vslSkipAheadStream
vslSkipAheadStreamEx
vslGetStreamStateBrng
vslGetNumRegBrngs
Convolution and Correlation Naming Conventions
Convolution and Correlation Data Types
Convolution and Correlation Parameters
Convolution and Correlation Task Status and Error Reporting
Convolution and Correlation Task Constructors
Convolution and Correlation Task Editors
Task Execution Routines
Convolution and Correlation Task Destructors
Convolution and Correlation Task Copiers
Convolution and Correlation Usage Examples
Convolution and Correlation Mathematical Notation and Definitions
Convolution and Correlation Data Allocation
Summary Statistics Naming Conventions
Summary Statistics Data Types
Summary Statistics Parameters
Summary Statistics Task Status and Error Reporting
Summary Statistics Task Constructors
Summary Statistics Task Editors
Summary Statistics Task Computation Routines
Summary Statistics Task Destructor
Summary Statistics Usage Examples
Summary Statistics Mathematical Notation and Definitions
DFTI_PRECISION
DFTI_FORWARD_DOMAIN
DFTI_DIMENSION, DFTI_LENGTHS
DFTI_PLACEMENT
DFTI_FORWARD_SCALE, DFTI_BACKWARD_SCALE
DFTI_NUMBER_OF_USER_THREADS
DFTI_THREAD_LIMIT
DFTI_INPUT_STRIDES, DFTI_OUTPUT_STRIDES
DFTI_NUMBER_OF_TRANSFORMS
DFTI_INPUT_DISTANCE, DFTI_OUTPUT_DISTANCE
DFTI_COMPLEX_STORAGE, DFTI_REAL_STORAGE, DFTI_CONJUGATE_EVEN_STORAGE
DFTI_PACKED_FORMAT
DFTI_WORKSPACE
DFTI_COMMIT_STATUS
DFTI_ORDERING
DFTI_DESTROY_INPUT
Data Fitting Function Naming Conventions
Data Fitting Function Data Types
Mathematical Conventions for Data Fitting Functions
Data Fitting Usage Model
Data Fitting Usage Examples
Data Fitting Function Task Status and Error Reporting
Data Fitting Task Creation and Initialization Routines
Task Configuration Routines
Data Fitting Computational Routines
Data Fitting Task Destructors
DSS Symmetric Matrix Storage
DSS Nonsymmetric Matrix Storage
DSS Structurally Symmetric Matrix Storage
DSS Distributed Symmetric Matrix Storage
Sparse BLAS CSR Matrix Storage Format
Sparse BLAS CSC Matrix Storage Format
Sparse BLAS Coordinate Matrix Storage Format
Sparse BLAS Diagonal Matrix Storage Format
Sparse BLAS Skyline Matrix Storage Format
Sparse BLAS BSR Matrix Storage Format
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LAPACK Auxiliary Routines
Routine naming conventions, mathematical notation, and matrix storage schemes used for LAPACK auxiliary routines are the same as for the driver and computational routines described in previous chapters.
Parent topic: LAPACK Routines
- ?lacgv
Conjugates a complex vector. - ?lacrm
Multiplies a complex matrix by a square real matrix. - ?syconv
Converts a symmetric matrix given by a triangular matrix factorization into two matrices and vice versa. - ?syr
Performs the symmetric rank-1 update of a complex symmetric matrix. - i?max1
Finds the index of the vector element whose real part has maximum absolute value. - ?sum1
Forms the 1-norm of the complex vector using the true absolute value. - ?gelq2
Computes the LQ factorization of a general rectangular matrix using an unblocked algorithm. - ?geqr2
Computes the QR factorization of a general rectangular matrix using an unblocked algorithm. - ?geqrt2
Computes a QR factorization of a general real or complex matrix using the compact WY representation of Q. - ?geqrt3
Recursively computes a QR factorization of a general real or complex matrix using the compact WY representation of Q. - ?getf2
Computes the LU factorization of a general m-by-n matrix using partial pivoting with row interchanges (unblocked algorithm). - ?lacn2
Estimates the 1-norm of a square matrix, using reverse communication for evaluating matrix-vector products. - ?lacpy
Copies all or part of one two-dimensional array to another. - ?lakf2
Forms a matrix containing Kronecker products between the given matrices. - ?lange
Returns the value of the 1-norm, Frobenius norm, infinity-norm, or the largest absolute value of any element of a general rectangular matrix. - ?lansy
Returns the value of the 1-norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a real/complex symmetric matrix. - ?lanhe
Returns the value of the 1-norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a complex Hermitian matrix. - ?lantr
Returns the value of the 1-norm, or the Frobenius norm, or the infinity norm, or the element of largest absolute value of a trapezoidal or triangular matrix. - LAPACKE_set_nancheck
Turns NaN checking off or on - LAPACKE_get_nancheck
Gets the current NaN checking flag, which indicates whether NaN checking has been turned off or on. - ?lapmr
Rearranges rows of a matrix as specified by a permutation vector. - ?lapmt
Performs a forward or backward permutation of the columns of a matrix. - ?lapy2
Returns sqrt(x2+y2). - ?lapy3
Returns sqrt(x2+y2+z2). - ?laran
Returns a random real number from a uniform distribution. - ?larfb
Applies a block reflector or its transpose/conjugate-transpose to a general rectangular matrix. - ?larfg
Generates an elementary reflector (Householder matrix). - ?larft
Forms the triangular factor T of a block reflector H = I - V*T*V**H. - ?larfx
Applies an elementary reflector to a general rectangular matrix, with loop unrolling when the reflector has order less than or equal to 10. - ?large
Pre- and post-multiplies a real general matrix with a random orthogonal matrix. - ?larnd
Returns a random real number from a uniform or normal distribution. - ?larnv
Returns a vector of random numbers from a uniform or normal distribution. - ?laror
Pre- or post-multiplies an m-by-n matrix by a random orthogonal/unitary matrix. - ?larot
Applies a Givens rotation to two adjacent rows or columns. - ?lartgp
Generates a plane rotation. - ?lartgs
Generates a plane rotation designed to introduce a bulge in implicit QR iteration for the bidiagonal SVD problem. - ?lascl
Multiplies a general rectangular matrix by a real scalar defined as cto/cfrom. - ?lasd0
Computes the singular values of a real upper bidiagonal n-by-m matrix B with diagonal d and off-diagonal e. Used by ?bdsdc. - ?lasd1
Computes the SVD of an upper bidiagonal matrix B of the specified size. Used by ?bdsdc. - ?lasd2
Merges the two sets of singular values together into a single sorted set. Used by ?bdsdc. - ?lasd3
Finds all square roots of the roots of the secular equation, as defined by the values in D and Z, and then updates the singular vectors by matrix multiplication. Used by ?bdsdc. - ?lasd4
Computes the square root of the i-th updated eigenvalue of a positive symmetric rank-one modification to a positive diagonal matrix. Used by ?bdsdc. - ?lasd5
Computes the square root of the i-th eigenvalue of a positive symmetric rank-one modification of a 2-by-2 diagonal matrix.Used by ?bdsdc. - ?lasd6
Computes the SVD of an updated upper bidiagonal matrix obtained by merging two smaller ones by appending a row. Used by ?bdsdc. - ?lasd7
Merges the two sets of singular values together into a single sorted set. Then it tries to deflate the size of the problem. Used by ?bdsdc. - ?lasd8
Finds the square roots of the roots of the secular equation, and stores, for each element in D, the distance to its two nearest poles. Used by ?bdsdc. - ?lasd9
Finds the square roots of the roots of the secular equation, and stores, for each element in D, the distance to its two nearest poles. Used by ?bdsdc. - ?lasda
Computes the singular value decomposition (SVD) of a real upper bidiagonal matrix with diagonal d and off-diagonal e. Used by ?bdsdc. - ?lasdq
Computes the SVD of a real bidiagonal matrix with diagonal d and off-diagonal e. Used by ?bdsdc. - ?lasdt
Creates a tree of subproblems for bidiagonal divide and conquer. Used by ?bdsdc. - ?laset
Initializes the off-diagonal elements and the diagonal elements of a matrix to given values. - ?lasrt
Sorts numbers in increasing or decreasing order. - ?laswp
Performs a series of row interchanges on a general rectangular matrix. - ?latm1
Computes the entries of a matrix as specified. - ?latm2
Returns an entry of a random matrix. - ?latm3
Returns set entry of a random matrix. - ?latm5
Generates matrices involved in the Generalized Sylvester equation. - ?latm6
Generates test matrices for the generalized eigenvalue problem, their corresponding right and left eigenvector matrices, and also reciprocal condition numbers for all eigenvalues and the reciprocal condition numbers of eigenvectors corresponding to the 1th and 5th eigenvalues. - ?latme
Generates random non-symmetric square matrices with specified eigenvalues. - ?latmr
Generates random matrices of various types. - ?lauum
Computes the product U*UT(U*UH) or LT*L (LH*L), where U and L are upper or lower triangular matrices (blocked algorithm). - ?syswapr
Applies an elementary permutation on the rows and columns of a symmetric matrix. - ?heswapr
Applies an elementary permutation on the rows and columns of a Hermitian matrix. - ?sfrk
Performs a symmetric rank-k operation for matrix in RFP format. - ?hfrk
Performs a Hermitian rank-k operation for matrix in RFP format. - ?tfsm
Solves a matrix equation (one operand is a triangular matrix in RFP format). - ?tfttp
Copies a triangular matrix from the rectangular full packed format (TF) to the standard packed format (TP) . - ?tfttr
Copies a triangular matrix from the rectangular full packed format (TF) to the standard full format (TR) . - ?tpqrt2
Computes a QR factorization of a real or complex "triangular-pentagonal" matrix, which is composed of a triangular block and a pentagonal block, using the compact WY representation for Q. - ?tprfb
Applies a real or complex "triangular-pentagonal" blocked reflector to a real or complex matrix, which is composed of two blocks. - ?tpttf
Copies a triangular matrix from the standard packed format (TP) to the rectangular full packed format (TF). - ?tpttr
Copies a triangular matrix from the standard packed format (TP) to the standard full format (TR) . - ?trttf
Copies a triangular matrix from the standard full format (TR) to the rectangular full packed format (TF). - ?trttp
Copies a triangular matrix from the standard full format (TR) to the standard packed format (TP) . - ?lacp2
Copies all or part of a real two-dimensional array to a complex array. - ?larcm
Multiplies a square real matrix by a complex matrix. - mkl_?tppack
Copies a triangular/symmetric matrix or submatrix from standard full format to standard packed format. - mkl_?tpunpack
Copies a triangular/symmetric matrix or submatrix from standard packed format to full format.