Routine(True,True,0,False,"1","swap",T,[S,D,C,Z,H],["n"],[],[],["x","y"],[xn,yn],[],"","Swap two vectors","Interchanges _n_ elements of vectors _x_ and _y_.",[]),
Routine(True,True,0,False,"1","scal",T,[S,D,C,Z,H],["n"],[],[],["x"],[xn],["alpha"],"","Vector scaling","Multiplies _n_ elements of vector _x_ by a scalar constant _alpha_.",[]),
Routine(True,True,0,False,"1","copy",T,[S,D,C,Z,H],["n"],[],["x"],["y"],[xn,yn],[],"","Vector copy","Copies the contents of vector _x_ into vector _y_.",[]),
Routine(True,True,0,False,"1","axpy",T,[S,D,C,Z,H],["n"],[],["x"],["y"],[xn,yn],["alpha"],"","Vector-times-constant plus vector","Performs the operation _y = alpha * x + y_, in which _x_ and _y_ are vectors and _alpha_ is a scalar constant.",[]),
Routine(True,True,0,False,"1","dot",T,[S,D,H],["n"],[],["x","y"],["dot"],[xn,yn,"1"],[],"n","Dot product of two vectors","Multiplies _n_ elements of the vectors _x_ and _y_ element-wise and accumulates the results. The sum is stored in the _dot_ buffer.",[]),
Routine(True,True,0,False,"1","dotu",T,[C,Z],["n"],[],["x","y"],["dot"],[xn,yn,"1"],[],"n","Dot product of two complex vectors","See the regular xDOT routine.",[]),
Routine(True,True,0,False,"1","dotc",T,[C,Z],["n"],[],["x","y"],["dot"],[xn,yn,"1"],[],"n","Dot product of two complex vectors, one conjugated","See the regular xDOT routine.",[]),
Routine(True,True,0,False,"1","nrm2",T,[S,D,Sc,Dz,H],["n"],[],["x"],["nrm2"],[xn,"1"],[],"2*n","Euclidian norm of a vector","Accumulates the square of _n_ elements in the _x_ vector and takes the square root. The resulting L2 norm is stored in the _nrm2_ buffer.",[]),
Routine(True,True,0,False,"1","asum",T,[S,D,Sc,Dz,H],["n"],[],["x"],["asum"],[xn,"1"],[],"n","Absolute sum of values in a vector","Accumulates the absolute value of _n_ elements in the _x_ vector. The results are stored in the _asum_ buffer.",[]),
Routine(True,False,0,False,"1","sum",T,[S,D,Sc,Dz,H],["n"],[],["x"],["sum"],[xn,"1"],[],"n","Sum of values in a vector (non-BLAS function)","Accumulates the values of _n_ elements in the _x_ vector. The results are stored in the _sum_ buffer. This routine is the non-absolute version of the xASUM BLAS routine.",[]),
Routine(True,True,0,False,"1","amax",T,[iS,iD,iC,iZ,iH],["n"],[],["x"],["imax"],[xn,"1"],[],"2*n","Index of absolute maximum value in a vector","Finds the index of the maximum of the absolute values in the _x_ vector. The resulting integer index is stored in the _imax_ buffer.",[]),
Routine(True,False,0,False,"1","amin",T,[iS,iD,iC,iZ,iH],["n"],[],["x"],["imin"],[xn,"1"],[],"2*n","Index of absolute minimum value in a vector (non-BLAS function)","Finds the index of the minimum of the absolute values in the _x_ vector. The resulting integer index is stored in the _imin_ buffer.",[]),
Routine(True,False,0,False,"1","max",T,[iS,iD,iC,iZ,iH],["n"],[],["x"],["imax"],[xn,"1"],[],"2*n","Index of maximum value in a vector (non-BLAS function)","Finds the index of the maximum of the values in the _x_ vector. The resulting integer index is stored in the _imax_ buffer. This routine is the non-absolute version of the IxAMAX BLAS routine.",[]),
Routine(True,False,0,False,"1","min",T,[iS,iD,iC,iZ,iH],["n"],[],["x"],["imin"],[xn,"1"],[],"2*n","Index of minimum value in a vector (non-BLAS function)","Finds the index of the minimum of the values in the _x_ vector. The resulting integer index is stored in the _imin_ buffer. This routine is the non-absolute minimum version of the IxAMAX BLAS routine.",[]),
Routine(True,True,0,False,"2a","gemv",T,[S,D,C,Z,H],["m","n"],["layout","a_transpose"],["a","x"],["y"],[amn,xmn,ynm],["alpha","beta"],"","General matrix-vector multiplication","Performs the operation _y = alpha * A * x + beta * y_, in which _x_ is an input vector, _y_ is an input and output vector, _A_ is an input matrix, and _alpha_ and _beta_ are scalars. The matrix _A_ can optionally be transposed before performing the operation.",[ald_m]),
Routine(True,True,0,False,"2a","gbmv",T,[S,D,C,Z,H],["m","n","kl","ku"],["layout","a_transpose"],["a","x"],["y"],[amn,xmn,ynm],["alpha","beta"],"","General banded matrix-vector multiplication","Same operation as xGEMV, but matrix _A_ is banded instead.",[ald_kl_ku_one]),
Routine(True,True,0,False,"2a","hemv",T,[C,Z],["n"],["layout","triangle"],["a","x"],["y"],[an,xn,yn],["alpha","beta"],"","Hermitian matrix-vector multiplication","Same operation as xGEMV, but matrix _A_ is an Hermitian matrix instead.",[ald_n]),
Routine(True,True,0,False,"2a","hbmv",T,[C,Z],["n","k"],["layout","triangle"],["a","x"],["y"],[an,xn,yn],["alpha","beta"],"","Hermitian banded matrix-vector multiplication","Same operation as xGEMV, but matrix _A_ is an Hermitian banded matrix instead.",[ald_k_one]),
Routine(True,True,0,False,"2a","hpmv",T,[C,Z],["n"],["layout","triangle"],["ap","x"],["y"],[apn,xn,yn],["alpha","beta"],"","Hermitian packed matrix-vector multiplication","Same operation as xGEMV, but matrix _A_ is an Hermitian packed matrix instead and represented as _AP_.",[]),
Routine(True,True,0,False,"2a","symv",T,[S,D,H],["n"],["layout","triangle"],["a","x"],["y"],[an,xn,yn],["alpha","beta"],"","Symmetric matrix-vector multiplication","Same operation as xGEMV, but matrix _A_ is symmetric instead.",[ald_n]),
Routine(True,True,0,False,"2a","sbmv",T,[S,D,H],["n","k"],["layout","triangle"],["a","x"],["y"],[an,xn,yn],["alpha","beta"],"","Symmetric banded matrix-vector multiplication","Same operation as xGEMV, but matrix _A_ is symmetric and banded instead.",[ald_k_one]),
Routine(True,True,0,False,"2a","spmv",T,[S,D,H],["n"],["layout","triangle"],["ap","x"],["y"],[apn,xn,yn],["alpha","beta"],"","Symmetric packed matrix-vector multiplication","Same operation as xGEMV, but matrix _A_ is a symmetric packed matrix instead and represented as _AP_.",[]),
Routine(True,True,0,False,"2a","trmv",T,[S,D,C,Z,H],["n"],["layout","triangle","a_transpose","diagonal"],["a"],["x"],[an,xn],[],"n","Triangular matrix-vector multiplication","Same operation as xGEMV, but matrix _A_ is triangular instead.",[ald_n]),
Routine(True,True,0,False,"2a","tbmv",T,[S,D,C,Z,H],["n","k"],["layout","triangle","a_transpose","diagonal"],["a"],["x"],[an,xn],[],"n","Triangular banded matrix-vector multiplication","Same operation as xGEMV, but matrix _A_ is triangular and banded instead.",[ald_k_one]),
Routine(True,True,0,False,"2a","tpmv",T,[S,D,C,Z,H],["n"],["layout","triangle","a_transpose","diagonal"],["ap"],["x"],[apn,xn],[],"n","Triangular packed matrix-vector multiplication","Same operation as xGEMV, but matrix _A_ is a triangular packed matrix instead and repreented as _AP_.",[]),
Routine(True,True,0,False,"2a","trsv",T,[S,D,C,Z],["n"],["layout","triangle","a_transpose","diagonal"],["a"],["x"],[an,xn],[],"","Solves a triangular system of equations","",[]),
Routine(False,True,0,False,"2a","tbsv",T,[S,D,C,Z],["n","k"],["layout","triangle","a_transpose","diagonal"],["a"],["x"],[an,xn],[],"","Solves a banded triangular system of equations","",[ald_k_one]),
Routine(False,True,0,False,"2a","tpsv",T,[S,D,C,Z],["n"],["layout","triangle","a_transpose","diagonal"],["ap"],["x"],[apn,xn],[],"","Solves a packed triangular system of equations","",[]),
Routine(True,True,0,False,"2b","ger",T,[S,D,H],["m","n"],["layout"],["x","y"],["a"],[xm,yn,amn],["alpha"],"","General rank-1 matrix update","Performs the operation _A = alpha * x * y^T + A_, in which _x_ is an input vector, _y^T_ is the transpose of the input vector _y_, _A_ is the matrix to be updated, and _alpha_ is a scalar value.",[ald_m]),
Routine(True,True,0,False,"2b","geru",T,[C,Z],["m","n"],["layout"],["x","y"],["a"],[xm,yn,amn],["alpha"],"","General rank-1 complex matrix update","Same operation as xGER, but with complex data-types.",[ald_m]),
Routine(True,True,0,False,"2b","gerc",T,[C,Z],["m","n"],["layout"],["x","y"],["a"],[xm,yn,amn],["alpha"],"","General rank-1 complex conjugated matrix update","Same operation as xGERU, but the update is done based on the complex conjugate of the input vectors.",[ald_m]),
Routine(True,True,0,False,"2b","her",Tc,[Css,Zdd],["n"],["layout","triangle"],["x"],["a"],[xn,an],["alpha"],"","Hermitian rank-1 matrix update","Performs the operation _A = alpha * x * x^T + A_, in which x is an input vector, x^T is the transpose of this vector, _A_ is the triangular Hermetian matrix to be updated, and alpha is a scalar value.",[ald_n]),
Routine(True,True,0,False,"2b","hpr",Tc,[Css,Zdd],["n"],["layout","triangle"],["x"],["ap"],[xn,apn],["alpha"],"","Hermitian packed rank-1 matrix update","Same operation as xHER, but matrix _A_ is an Hermitian packed matrix instead and represented as _AP_.",[]),
Routine(True,True,0,False,"2b","her2",T,[C,Z],["n"],["layout","triangle"],["x","y"],["a"],[xn,yn,an],["alpha"],"","Hermitian rank-2 matrix update","Performs the operation _A = alpha * x * y^T + conj(alpha) * y * x^T + A_, in which _x_ is an input vector and _x^T_ its transpose, _y_ is an input vector and _y^T_ its transpose, _A_ is the triangular Hermetian matrix to be updated, _alpha_ is a scalar value and _conj(alpha)_ its complex conjugate.",[ald_n]),
Routine(True,True,0,False,"2b","hpr2",T,[C,Z],["n"],["layout","triangle"],["x","y"],["ap"],[xn,yn,apn],["alpha"],"","Hermitian packed rank-2 matrix update","Same operation as xHER2, but matrix _A_ is an Hermitian packed matrix instead and represented as _AP_.",[]),
Routine(True,True,0,False,"2b","syr",T,[S,D,H],["n"],["layout","triangle"],["x"],["a"],[xn,an],["alpha"],"","Symmetric rank-1 matrix update","Same operation as xHER, but matrix A is a symmetric matrix instead.",[ald_n]),
Routine(True,True,0,False,"2b","spr",T,[S,D,H],["n"],["layout","triangle"],["x"],["ap"],[xn,apn],["alpha"],"","Symmetric packed rank-1 matrix update","Same operation as xSPR, but matrix _A_ is a symmetric packed matrix instead and represented as _AP_.",[]),
Routine(True,True,0,False,"2b","syr2",T,[S,D,H],["n"],["layout","triangle"],["x","y"],["a"],[xn,yn,an],["alpha"],"","Symmetric rank-2 matrix update","Same operation as xHER2, but matrix _A_ is a symmetric matrix instead.",[ald_n]),
Routine(True,True,0,False,"2b","spr2",T,[S,D,H],["n"],["layout","triangle"],["x","y"],["ap"],[xn,yn,apn],["alpha"],"","Symmetric packed rank-2 matrix update","Same operation as xSPR2, but matrix _A_ is a symmetric packed matrix instead and represented as _AP_.",[]),
Routine(True,True,0,True,"3","gemm",T,[S,D,C,Z,H],["m","n","k"],["layout","a_transpose","b_transpose"],["a","b"],["c"],[amk,bkn,cmn],["alpha","beta"],"","General matrix-matrix multiplication","Performs the matrix product _C = alpha * A * B + beta * C_, in which _A_ (_m_ by _k_) and _B_ (_k_ by _n_) are two general rectangular input matrices, _C_ (_m_ by _n_) is the matrix to be updated, and _alpha_ and _beta_ are scalar values. The matrices _A_ and/or _B_ can optionally be transposed before performing the operation.",[ald_transa_m_k,bld_transb_k_n,cld_m]),
Routine(True,True,0,False,"3","symm",T,[S,D,C,Z,H],["m","n"],["layout","side","triangle"],["a","b"],["c"],[ammn,bmnn,cmn],["alpha","beta"],"","Symmetric matrix-matrix multiplication","Same operation as xGEMM, but _A_ is symmetric instead. In case of `side == kLeft`, _A_ is a symmetric _m_ by _m_ matrix and _C = alpha * A * B + beta * C_ is performed. Otherwise, in case of `side == kRight`, _A_ is a symmtric _n_ by _n_ matrix and _C = alpha * B * A + beta * C_ is performed.",[ald_side_m_n,bld_m,cld_m]),
Routine(True,True,0,False,"3","hemm",T,[C,Z],["m","n"],["layout","side","triangle"],["a","b"],["c"],[ammn,bmnn,cmn],["alpha","beta"],"","Hermitian matrix-matrix multiplication","Same operation as xSYMM, but _A_ is an Hermitian matrix instead.",[ald_side_m_n,bld_m,cld_m]),
Routine(True,True,0,False,"3","syrk",T,[S,D,C,Z,H],["n","k"],["layout","triangle","a_transpose"],["a"],["c"],[ank,cn],["alpha","beta"],"","Rank-K update of a symmetric matrix","Performs the matrix product _C = alpha * A * A^T + beta * C_ or _C = alpha * A^T * A + beta * C_, in which _A_ is a general matrix and _A^T_ is its transpose, _C_ (_n_ by _n_) is the symmetric matrix to be updated, and _alpha_ and _beta_ are scalar values.",[ald_trans_n_k,cld_m]),
Routine(True,True,0,False,"3","herk",Tc,[Css,Zdd],["n","k"],["layout","triangle","a_transpose"],["a"],["c"],[ank,cn],["alpha","beta"],"","Rank-K update of a hermitian matrix","Same operation as xSYRK, but _C_ is an Hermitian matrix instead.",[ald_trans_n_k,cld_m]),
Routine(True,True,0,False,"3","syr2k",T,[S,D,C,Z,H],["n","k"],["layout","triangle","ab_transpose"],["a","b"],["c"],[ankab,bnkab,cn],["alpha","beta"],"","Rank-2K update of a symmetric matrix","Performs the matrix product _C = alpha * A * B^T + alpha * B * A^T + beta * C_ or _C = alpha * A^T * B + alpha * B^T * A + beta * C_, in which _A_ and _B_ are general matrices and _A^T_ and _B^T_ are their transposed versions, _C_ (_n_ by _n_) is the symmetric matrix to be updated, and _alpha_ and _beta_ are scalar values.",[ald_trans_n_k,bld_trans_n_k,cld_n]),
Routine(True,True,0,False,"3","her2k",TU,[Ccs,Zzd],["n","k"],["layout","triangle","ab_transpose"],["a","b"],["c"],[ankab,bnkab,cn],["alpha","beta"],"","Rank-2K update of a hermitian matrix","Same operation as xSYR2K, but _C_ is an Hermitian matrix instead.",[ald_trans_n_k,bld_trans_n_k,cld_n]),
Routine(True,True,0,False,"3","trmm",T,[S,D,C,Z,H],["m","n"],["layout","side","triangle","a_transpose","diagonal"],["a"],["b"],[amns,bmn],["alpha"],"","Triangular matrix-matrix multiplication","Performs the matrix product _B = alpha * A * B_ or _B = alpha * B * A_, in which _A_ is a unit or non-unit triangular matrix, _B_ (_m_ by _n_) is the general matrix to be updated, and _alpha_ is a scalar value.",[ald_side_m_n,bld_m]),
Routine(True,True,0,False,"3","trsm",T,[S,D,C,Z],["m","n"],["layout","side","triangle","a_transpose","diagonal"],["a"],["b"],[amns,bmn],["alpha"],"","Solves a triangular system of equations","Solves the equation _A * X = alpha * B_ for the unknown _m_ by _n_ matrix X, in which _A_ is an _n_ by _n_ unit or non-unit triangular matrix and B is an _m_ by _n_ matrix. The matrix _B_ is overwritten by the solution _X_.",[]),
Routine(True,True,0,False,"x","had",T,[S,D,C,Z,H],["n"],[],["x","y"],["z"],[xn,yn,zn],["alpha","beta"],"","Element-wise vector product (Hadamard)","Performs the Hadamard element-wise product _z = alpha * x * y + beta * z_, in which _x_, _y_, and _z_ are vectors and _alpha_ and _beta_ are scalar constants.",[]),
Routine(True,True,0,False,"x","omatcopy",T,[S,D,C,Z,H],["m","n"],["layout","a_transpose"],["a"],["b"],[amn,bnma],["alpha"],"","Scaling and out-place transpose/copy (non-BLAS function)","Performs scaling and out-of-place transposition/copying of matrices according to _B = alpha*op(A)_, in which _A_ is an input matrix (_m_ rows by _n_ columns), _B_ an output matrix, and _alpha_ a scalar value. The operation _op_ can be a normal matrix copy, a transposition or a conjugate transposition.",[ald_m,bld_n]),
Routine(True,True,0,False,"x","im2col",T,[S,D,C,Z,H],im2col_constants,[],["im"],["col"],[im,col],[""],"","Im2col function (non-BLAS function)","Performs the im2col algorithm, in which _im_ is the input matrix and _col_ is the output matrix.",[]),
Routine(True,True,0,False,"x","convgemm",T,[S,D,H],convgemm_constants,[],["im","kernel"],["result"],[imb,kernel,result],[""],"","Batched convolution as GEMM (non-BLAS function)","Integrates im2col and GEMM for batched 3D convolution, in which _im_ is the 4D input tensor (NCHW - batch-channelin-height-width), _kernel_ the 4D kernel weights tensor (KCHW - channelout-channelin-height-width), and _result_ the 4D output tensor (NCHW - batch-channelout-height-width).",[]),
Routine(True,True,1,False,"x","axpy",T,[S,D,C,Z,H],["n"],[],["x"],["y"],[xn,yn],["alpha"],"","Batched version of AXPY","As AXPY, but multiple operations are batched together for better performance.",[]),
Routine(True,True,1,False,"x","gemm",T,[S,D,C,Z,H],["m","n","k"],["layout","a_transpose","b_transpose"],["a","b"],["c"],[amk,bkn,cmn],["alpha","beta"],"","Batched version of GEMM","As GEMM, but multiple operations are batched together for better performance.",[ald_transa_m_k,bld_transb_k_n,cld_m]),
Routine(True,True,2,False,"x","gemm",T,[S,D,C,Z,H],["m","n","k"],["layout","a_transpose","b_transpose"],["a","b"],["c"],[amk,bkn,cmn],["alpha","beta"],"","StridedBatched version of GEMM","As GEMM, but multiple strided operations are batched together for better performance.",[ald_transa_m_k,bld_transb_k_n,cld_m]),