3 Library functions

79,992 pages in section 3.

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p_ to pb
pc
pd to pg
ph to pl
pm
pm to pn
po to pr
ps
pt to py
pz

pd

pdb_AppendRecord add records to a Palm database
pdb_AppendResource add records to a Palm database
pdb_CopyRecord short description
pdb_CopyResource short description
pdb_DeleteRecordByID short description
pdb_FindRecordByID search for records in Palm databases
pdb_FindRecordByIndex search for records in Palm databases
pdb_InsertRecord add records to a Palm database
pdb_InsertResource add records to a Palm database
pdb_LoadHeader read the header of a Palm database file
pdb_Read read, write Palm database files
pdb_Write read, write Palm database files
pddbsv solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pddbtrf compute a LU factorization of an N-by-N real banded diagonally dominant-like distributed matrix with bandwidth BWL, BWU
pddbtrs solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pddbtrsv solve a banded triangular system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pddtsv solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pddttrf compute a LU factorization of an N-by-N real tridiagonal diagonally dominant-like distributed matrix A(1:N, JA:JA+N-1)
pddttrs solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pddttrsv solve a tridiagonal triangular system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pdfio pdf read/write library
pdgbsv solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pdgbtrf compute a LU factorization of an N-by-N real banded distributed matrix with bandwidth BWL, BWU
pdgbtrs solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pdgebd2 reduce a real general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) to upper or lower bidiagonal form B by an orthogonal transformation
pdgebrd reduce a real general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) to upper or lower bidiagonal form B by an orthogonal transformation
pdgecon estimate the reciprocal of the condition number of a general distributed real matrix A(IA:IA+N-1,JA:JA+N-1), in either the 1-norm or the infinity-norm, using the LU factorization computed by PDGETRF
pdgeequ compute row and column scalings intended to equilibrate an M-by-N distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA:JA+N-1) and reduce its condition number
pdgehd2 reduce a real general distributed matrix sub( A ) to upper Hessenberg form H by an orthogonal similarity transforma- tion
pdgehrd reduce a real general distributed matrix sub( A ) to upper Hessenberg form H by an orthogonal similarity transforma- tion
pdgelq2 compute a LQ factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = L * Q
pdgelqf compute a LQ factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = L * Q
pdgels solve overdetermined or underdetermined real linear systems involving an M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1),
pdgeql2 compute a QL factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * L
pdgeqlf compute a QL factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * L
pdgeqpf compute a QR factorization with column pivoting of a M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1)
pdgeqr2 compute a QR factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * R
pdgeqrf compute a QR factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * R
pdgerfs improve the computed solution to a system of linear equations and provides error bounds and backward error estimates for the solutions
pdgerq2 compute a RQ factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = R * Q
pdgerqf compute a RQ factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = R * Q
pdgesv compute the solution to a real system of linear equations sub( A ) * X = sub( B ),
pdgesvd compute the singular value decomposition (SVD) of an M-by-N matrix A, optionally computing the left and/or right singular vectors
pdgesvx use the LU factorization to compute the solution to a real system of linear equations A(IA:IA+N-1,JA:JA+N-1) * X = B(IB:IB+N-1,JB:JB+NRHS-1),
pdgetf2 compute an LU factorization of a general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) using partial pivoting with row interchanges
pdgetrf compute an LU factorization of a general M-by-N distributed matrix sub( A ) = (IA:IA+M-1,JA:JA+N-1) using partial pivoting with row interchanges
pdgetri compute the inverse of a distributed matrix using the LU factorization computed by PDGETRF
pdgetrs solve a system of distributed linear equations op( sub( A ) ) * X = sub( B ) with a general N-by-N distributed matrix sub( A ) using the LU factorization computed by PDGETRF
pdggqrf compute a generalized QR factorization of an N-by-M matrix sub( A ) = A(IA:IA+N-1,JA:JA+M-1) and an N-by-P matrix sub( B ) = B(IB:IB+N-1,JB:JB+P-1)
pdggrqf compute a generalized RQ factorization of an M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1)
pdlabad take as input the values computed by PDLAMCH for underflow and overflow, and returns the square root of each of these values if the log of LARGE is sufficiently large
pdlabrd reduce the first NB rows and columns of a real general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) to upper or lower bidiagonal form by an orthogonal transformation Q' * A * P,
pdlacon estimate the 1-norm of a square, real distributed matrix A
pdlaconsb look for two consecutive small subdiagonal elements by seeing the effect of starting a double shift QR iteration given by H44, H33, & H43H34 and see if this would make a subdiagonal negligible
pdlacp2 copie all or part of a distributed matrix A to another distributed matrix B
pdlacp3 i an auxiliary routine that copies from a global parallel array into a local replicated array or vise versa
pdlacpy copie all or part of a distributed matrix A to another distributed matrix B
pdlaevswp move the eigenvectors (potentially unsorted) from where they are computed, to a ScaLAPACK standard block cyclic array, sorted so that the corresponding eigenvalues are sorted
pdlahqr i an auxiliary routine used to find the Schur decomposition and or eigenvalues of a matrix already in Hessenberg form from cols ILO to IHI
pdlahrd reduce the first NB columns of a real general N-by-(N-K+1) distributed matrix A(IA:IA+N-1,JA:JA+N-K) so that elements below the k-th subdiagonal are zero
pdlamch determine double precision machine parameters
pdlange return the value of the one norm, or the Frobenius norm,
pdlanhs return the value of the one norm, or the Frobenius norm,
pdlansy return the value of the one norm, or the Frobenius norm,
pdlantr return the value of the one norm, or the Frobenius norm,
pdlapiv applie either P (permutation matrix indicated by IPIV) or inv( P ) to a general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1), resulting in row or column pivoting
pdlapv2 applie either P (permutation matrix indicated by IPIV) or inv( P ) to a M-by-N distributed matrix sub( A ) denoting A(IA:IA+M-1,JA:JA+N-1), resulting in row or column pivoting
pdlaqge equilibrate a general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) using the row and scaling factors in the vectors R and C
pdlaqsy equilibrate a symmetric distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1) using the scaling factors in the vectors SR and SC
pdlared1d redistribute a 1D array It assumes that the input array, BYCOL, is distributed across rows and that all process column contain the same copy of BYCOL
pdlared2d redistribute a 1D array It assumes that the input array, BYROW, is distributed across columns and that all process rows contain the same copy of BYROW
pdlarf applie a real elementary reflector Q (or Q**T) to a real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1), from either the left or the right
pdlarfb applie a real block reflector Q or its transpose Q**T to a real distributed M-by-N matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1)
pdlarfg generate a real elementary reflector H of order n, such that H * sub( X ) = H * ( x(iax,jax) ) = ( alpha ), H' * H = I
pdlarft form the triangular factor T of a real block reflector H of order n, which is defined as a product of k elementary reflectors
pdlarz applie a real elementary reflector Q (or Q**T) to a real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1), from either the left or the right
pdlarzb applie a real block reflector Q or its transpose Q**T to a real distributed M-by-N matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1)
pdlarzt form the triangular factor T of a real block reflector H of order > n, which is defined as a product of k elementary reflectors as returned by PDTZRZF
pdlascl multiplie the M-by-N real distributed matrix sub( A ) denoting A(IA:IA+M-1,JA:JA+N-1) by the real scalar CTO/CFROM
pdlase2 initialize an M-by-N distributed matrix sub( A ) denoting A(IA:IA+M-1,JA:JA+N-1) to BETA on the diagonal and ALPHA on the offdiagonals
pdlaset initialize an M-by-N distributed matrix sub( A ) denoting A(IA:IA+M-1,JA:JA+N-1) to BETA on the diagonal and ALPHA on the offdiagonals
pdlasmsub look for a small subdiagonal element from the bottom of the matrix that it can safely set to zero
pdlassq return the values scl and smsq such that ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq,
pdlaswp perform a series of row or column interchanges on the distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1)
pdlatra compute the trace of an N-by-N distributed matrix sub( A ) denoting A( IA:IA+N-1, JA:JA+N-1 )
pdlatrd reduce NB rows and columns of a real symmetric distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1) to symmetric tridiagonal form by an orthogonal similarity transformation Q' * sub( A ) * Q,
pdlatrs solve a triangular system
pdlatrz reduce the M-by-N ( M<=N ) real upper trapezoidal matrix sub( A ) = [ A(IA:IA+M-1,JA:JA+M-1) A(IA:IA+M-1,JA+N-L:JA+N-1) ] to upper triangular form by means of orthogonal transformations
pdlauu2 compute the product U * U' or L' * L, where the triangular factor U or L is stored in the upper or lower triangular part of the matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1)
pdlauum compute the product U * U' or L' * L, where the triangular factor U or L is stored in the upper or lower triangular part of the distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1)
pdlawil get the transform given by H44,H33, & H43H34 into V starting at row M
pdorg2l generate an M-by-N real distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal columns, which is defined as the last N columns of a product of K elementary reflectors of order M Q = H(k)
pdorg2r generate an M-by-N real distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal columns, which is defined as the first N columns of a product of K elementary reflectors of order M Q = H(1) H(2)
pdorgl2 generate an M-by-N real distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal rows, which is defined as the first M rows of a product of K elementary reflectors of order N Q = H(k)
pdorglq generate an M-by-N real distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal rows, which is defined as the first M rows of a product of K elementary reflectors of order N Q = H(k)
pdorgql generate an M-by-N real distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal columns, which is defined as the last N columns of a product of K elementary reflectors of order M Q = H(k)
pdorgqr generate an M-by-N real distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal columns, which is defined as the first N columns of a product of K elementary reflectors of order M Q = H(1) H(2)
pdorgr2 generate an M-by-N real distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal rows, which is defined as the last M rows of a product of K elementary reflectors of order N Q = H(1) H(2)
pdorgrq generate an M-by-N real distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal rows, which is defined as the last M rows of a product of K elementary reflectors of order N Q = H(1) H(2)
pdorm2l overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdorm2r overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdormbr VECT = 'Q', PDORMBR overwrites the general real distributed M-by-N matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdormhr overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdorml2 overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdormlq overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdormql overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdormqr overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdormr2 overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdormr3 overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdormrq overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdormrz overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdormtr overwrite the general real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'
pdpbsv solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pdpbtrf compute a Cholesky factorization of an N-by-N real banded symmetric positive definite distributed matrix with bandwidth BW
pdpbtrs solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pdpbtrsv solve a banded triangular system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pdpocon estimate the reciprocal of the condition number (in the 1-norm) of a real symmetric positive definite distributed matrix using the Cholesky factorization A = U**T*U or A = L*L**T computed by PDPOTRF
pdpoequ compute row and column scalings intended to equilibrate a distributed symmetric positive definite matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1) and reduce its condition number (with respect to the two-norm)
pdporfs improve the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definite and provides error bounds and backward error estimates for the solutions
pdposv compute the solution to a real system of linear equations sub( A ) * X = sub( B ),
pdposvx use the Cholesky factorization A = U**T*U or A = L*L**T to compute the solution to a real system of linear equations A(IA:IA+N-1,JA:JA+N-1) * X = B(IB:IB+N-1,JB:JB+NRHS-1),
pdpotf2 compute the Cholesky factorization of a real symmetric positive definite distributed matrix sub( A )=A(IA:IA+N-1,JA:JA+N-1)
pdpotrf compute the Cholesky factorization of an N-by-N real symmetric positive definite distributed matrix sub( A ) denoting A(IA:IA+N-1, JA:JA+N-1)
pdpotri compute the inverse of a real symmetric positive definite distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1) using the Cholesky factorization sub( A ) = U**T*U or L*L**T computed by PDPOTRF
pdpotrs solve a system of linear equations sub( A ) * X = sub( B ) A(IA:IA+N-1,JA:JA+N-1)*X = B(IB:IB+N-1,JB:JB+NRHS-1)
pdptsv solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pdpttrf compute a Cholesky factorization of an N-by-N real tridiagonal symmetric positive definite distributed matrix A(1:N, JA:JA+N-1)
pdpttrs solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pdpttrsv solve a tridiagonal triangular system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pdrscl multiplie an N-element real distributed vector sub( X ) by the real scalar 1/a
pdstebz compute the eigenvalues of a symmetric tridiagonal matrix in parallel
pdstein compute the eigenvectors of a symmetric tridiagonal matrix in parallel, using inverse iteration
pdstemplates.c Functions for GRIB2 Product Definition Templates used in Section 4 - the Product Definition Section (PDS).
pdsyev compute all eigenvalues and, optionally, eigenvectors of a real symmetric matrix A by calling the recommended sequence of ScaLAPACK routines.
pdsyevx compute selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix A by calling the recommended sequence of ScaLAPACK routines
pdsygs2 reduce a real symmetric-definite generalized eigenproblem to standard form
pdsygst reduce a real symmetric-definite generalized eigenproblem to standard form
pdsygvx compute all the eigenvalues, and optionally, the eigenvectors of a real generalized SY-definite eigenproblem
pdsytd2 reduce a real symmetric matrix sub( A ) to symmetric tridiagonal form T by an orthogonal similarity transformation
pdsytrd reduce a real symmetric matrix sub( A ) to symmetric tridiagonal form T by an orthogonal similarity transformation
pdtrcon estimate the reciprocal of the condition number of a triangular distributed matrix A(IA:IA+N-1,JA:JA+N-1), in either the 1-norm or the infinity-norm
pdtrrfs provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular coefficient matrix
pdtrti2 compute the inverse of a real upper or lower triangular block matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1)
pdtrtri compute the inverse of a upper or lower triangular distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1)
pdtrtrs solve a triangular system of the form sub( A ) * X = sub( B ) or sub( A )**T * X = sub( B ),
pdtzrzf reduce the M-by-N ( M<=N ) real upper trapezoidal matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) to upper triangular form by means of orthogonal transformations
pdzsum1 return the sum of absolute values of a complex distributed vector sub( X ) in ASUM,

pe

pecho_wchar create and display curses pads
pechochar create and display curses pads
peks-auth public key authentication
peks-errnum peks error message handler
peks-keys key and file manipulation functions
peks-misc query user characteristics
peks-passwd handling user passwords
peks-pthreads Posix threads support for peks
peks-session public key encryption tunnel negotiation
peks_admin_equiv key and file manipulation functions
peks_client_authenticate public key authentication
peks_delete_userkey key and file manipulation functions
peks_edit_passwd_file handling user passwords
peks_get_homedir query user characteristics
peks_get_username query user characteristics
peks_keylen key and file manipulation functions
peks_list_keyfile key and file manipulation functions
peks_private_access key and file manipulation functions
peks_private_key key and file manipulation functions
peks_save_private_key key and file manipulation functions
peks_server_auth_accept public key authentication
peks_server_auth_pubkey public key authentication
peks_server_auth_reject public key authentication
peks_strerr peks error message handler
peks_version returns the current peks version
perceptron.hpp
PerlPanel::DesktopEntry an interface to .desktop files for the PerlPanel.
PerlPanel::MenuBase a base class for PerlPanel menu applets.
PermanentEntryID PermanentEntryID structure.
perror print a system error message
persist.h Persistence library classes.
Perspective visible portion of an interactor
petsc4py PETSc for Python

pf

pfm_dispatch_events determine PMC registers values for a set of events to measure
pfm_find_event
pfm_find_event_bycode search for events and unit masks
pfm_find_event_bycode_next search for events and unit masks
pfm_find_event_mask search for events and unit masks
pfm_find_full_event search for events and unit masks
pfm_force_pmu query library about supported PMU models
pfm_get_cycle_event get basic event descriptors
pfm_get_event_attr_info get event attribute information
pfm_get_event_code get event information
pfm_get_event_code_counter get event information
pfm_get_event_counters get event information
pfm_get_event_description get event information
pfm_get_event_encoding get raw event encoding
pfm_get_event_info get event information
pfm_get_event_mask_code get event information
pfm_get_event_mask_description get event information
pfm_get_event_mask_name get event information
pfm_get_event_name get event information
pfm_get_event_next iterate over events
pfm_get_full_event_name get event information
pfm_get_hw_counter_width return bitmask of implemented PMU registers or number of PMU registers
pfm_get_impl_counters return bitmask of implemented PMU registers or number of PMU registers
pfm_get_impl_pmcs return bitmask of implemented PMU registers or number of PMU registers
pfm_get_impl_pmds return bitmask of implemented PMU registers or number of PMU registers
pfm_get_inst_retired get basic event descriptors
pfm_get_max_event_name_len get event information
pfm_get_num_counters return bitmask of implemented PMU registers or number of PMU registers
pfm_get_num_events get event information
pfm_get_num_pmcs return bitmask of implemented PMU registers or number of PMU registers
pfm_get_num_pmds return bitmask of implemented PMU registers or number of PMU registers
pfm_get_os_event_encoding get event encoding for a specific operating system
pfm_get_perf_event_encoding encode event for perf_event API
pfm_get_pmu_info get PMU information
pfm_get_pmu_name query library about supported PMU models
pfm_get_pmu_name_bytype query library about supported PMU models
pfm_get_pmu_type query library about supported PMU models
pfm_get_version get library version
pfm_initialize initialize library
pfm_list_supported_pmus query library about supported PMU models
pfm_pmu_is_supported query library about supported PMU models
pfm_regmask_and operations on pfmlib_regmask_t bitmasks
pfm_regmask_clr operations on pfmlib_regmask_t bitmasks
pfm_regmask_copy operations on pfmlib_regmask_t bitmasks
pfm_regmask_eq operations on pfmlib_regmask_t bitmasks
pfm_regmask_isset operations on pfmlib_regmask_t bitmasks
pfm_regmask_or operations on pfmlib_regmask_t bitmasks
pfm_regmask_set operations on pfmlib_regmask_t bitmasks
pfm_regmask_weight operations on pfmlib_regmask_t bitmasks
pfm_self_start start and stop monitoring for a self-monitoring process
pfm_self_stop start and stop monitoring for a self-monitoring process
pfm_set_options set performance monitoring library debug options
pfm_strerror return constant string describing error code
pfm_terminate free resources used by library
pfsv pfsv: factor and solve [not available]
pfsv_comp --- rectangular full packed (RFP) ---
pfsv_driver --- rectangular full packed (RFP) ---
pftrf pftrf: triangular factor
pftri pftri: triangular inverse
pftrs pftrs: triangular solve using factor

pg

PGABuildDatatype Build an MPI datatype for string p in population pop.
PGAChange Repeatedly apply mutation to a string (with an increasing mutation rate) until one or more mutations have occurred. This routine is usually used with PGADuplicate to modify a duplicate string. It is not intended to replace PGAMutation
PGACheckStoppingConditions returns boolean to indicate if the PGAPack termination conditions -- PGA_STOP_MAXITER, PGA_STOP_TOOSIMILAR, PGA_STOP_NOCHANGE -- have been met.
PGACheckSum maps a string to a number to be used a verification check PGA_DATATYPE_USER is not supported.
PGAClearDebugLevel Turn off a debul level.
PGAClearDebugLevelByName Turn off debugging of the named function.
PGACopyIndividual copies string p1 in population pop1 to position p2 in population pop2
PGACreate creates an uninitialized context variable. The Fortran version of this function call contains only the last three arguments
PGACrossover performs crossover on two parent strings to create two child strings (via side-effect).
PGADebugPrint Write debugging information
PGADestroy deallocate memory for this instance of PGAPack, if this context initialized MPI, finalize MPI as well.
PGADone Returns PGA_TRUE if the stopping conditions have been met, otherwise returns false. Calls exactly one of the user defined C or fortran or system (PGACheckStoppingConditions) stopping condition functions.
PGADuplicate determines if a specified string is a duplicate of one already in an existing population
PGAEncodeIntegerAsBinary encodes an integer value as a binary string
PGAEncodeIntegerAsGrayCode encodes a real value as a binary reflected Gray code sequence
PGAEncodeRealAsBinary encodes a real value as a binary string
PGAEncodeRealAsGrayCode encodes a real value as a binary reflected Gray code sequence
PGAError reports error messages. Prints out the message supplied, and the value of a piece of data. Terminates if PGA_FATAL.
PGAEvaluate Calls a user-specified function to return an evaluation of each string in the population. The user-specified function is only called if the string has been changed (e.g., by crossover or mutation) or the user has explicitly signaled the string's evaluation is out-of-date by a call to PGASetEvaluationUpToDateFlag().
PGAFitness Maps the user's evaluation function value to a fitness value. First, the user's evaluation function value is translated to all positive values if any are negative. Next, this positive sequence is translated to a maximization problem if the user's optimization direction was minimization. This positive sequence is then mapped to a fitness value using linear ranking, linear normalization fitness, or the identity (i.e., the evaluation function value). This routine is usually used after PGAEvaluate is called.
PGAGetBestIndex returns the index of the string with the best evaluation function value in population pop
PGAGetBinaryAllele returns the value of a (binary) allele in a PGA_DATATYPE_BINARY string
PGAGetBinaryInitProb Returns the probability that an allele will be randomly initialized to "1" in a PGA_DATATYPE_BINARY string.
PGAGetCharacterAllele
PGAGetCommunicator Returns the default communicator used when PGARun is called.
PGAGetCrossoverProb Returns the crossover probability
PGAGetCrossoverType Returns the type of crossover selected
PGAGetDataType Returns the data type used by the given context.
PGAGetEvaluation returns the evaluation function value for string p in population pop
PGAGetEvaluationUpToDateFlag returns true/false to indicate whether the evaluate function value is up to date
PGAGetFitness returns the fitness value for a string
PGAGetFitnessCmaxValue returns the value of the multiplier used by PGAFitnessMinCmax.
PGAGetFitnessMinType Returns the type of fitness transformation used for minimization problems.
PGAGetFitnessType Returns the type of fitness transformation used.
PGAGetGAIterValue returns the number of the current genetic algorithm generation
PGAGetIntegerAllele Returns the value of allele i of member p in population pop. Assumes the data type is PGA_DATATYPE_INTEGER.
PGAGetIntegerFromBinary interpets a binary string as encoding an integer value and returns the integer value it represents.
PGAGetIntegerFromGrayCode interpets a binary reflected Gray code sequence as encoding an integer value and returns the integer value it represents.
PGAGetIntegerInitType returns the type of scheme used to randomly initialize strings of data type PGA_DATATYPE_INTEGER.
PGAGetMaxFitnessRank returns the maximum value used in rank-based fitness.
PGAGetMaxGAIterValue Returns the maximum number of iterations to run
PGAGetMaxIntegerInitValue returns the maximum of the range of integers used to randomly initialize integer strings.
PGAGetMaxMachineDoubleValue returns the largest double of the current machine
PGAGetMaxMachineIntValue returns the largest integer of the current machine
PGAGetMaxRealInitValue returns the maximum value used to randomly initialize allele i in a real string
PGAGetMinIntegerInitValue returns the minimum of the range of integers used to randomly initialize integer strings.
PGAGetMinMachineDoubleValue returns the smallest double of the current machine
PGAGetMinMachineIntValue returns the smallest integer of the current machine
PGAGetMinRealInitValue returns the minimum value used to randomly initialize allele i in a real string
PGAGetMutationAndCrossoverFlag Returns true if mutation occurs only when crossover does.
PGAGetMutationBoundedFlag returns PGA_TRUE or PGA_FALSE to indicate whether mutated integer strings remain in the range specified when initialized with PGASetIntegerInitRange.
PGAGetMutationIntegerValue Returns the value of the multiplier used to mutate PGA_DATATYPE_INTEGER strings with.
PGAGetMutationOrCrossoverFlag Returns true if mutation only occurs when crossover does not.
PGAGetMutationProb Returns the probability of mutation.
PGAGetMutationRealValue Returns the value of the multiplier used to mutate PGA_DATATYPE_REAL strings with.
PGAGetMutationType Returns the type of mutation used
PGAGetNoDuplicatesFlag Returns PGA_TRUE if duplicates are not allowed, else returns PGA_FALSE.
PGAGetNumProcs Returns the size of communicator comm in processes. If comm is NULL or a sequential version of PGAPack is used, PGAGetNumProcs() returns 1.
PGAGetNumReplaceValue Returns the maximum number of strings to replace each generation.
PGAGetOptDirFlag Returns a symbolic constant that represents the direction of optimization
PGAGetPopReplaceType returns the symbolic constant used to determine which strings to copy from the old population to the new population.
PGAGetPopSize Returns the population size
PGAGetPrintFrequencyValue returns how often to print statistics reports
PGAGetPTournamentProb returns the probability of selecting the best string in a probabilistic binary tournament
PGAGetRandomInitFlag returns true/false to indicate whether or not alleles are randomly initialized.
PGAGetRandomSeed returns the integer to seed random numbers with
PGAGetRank Returns the rank of the processor in communicator comm. If comm is NULL or a sequential version of PGAPack is used, PGAGetRank() returns 0.
PGAGetRealAllele returns the value of real-valued allele i in string p in population pop
PGAGetRealFromBinary Interpets a binary string as encoding a real value and returns the real value it represents.
PGAGetRealFromGrayCode interpets a binary reflected Gray code sequence in a binary string as encoding a real value and returns the real value it represents.
PGAGetRealInitType returns the type of scheme used to randomly initialize strings of data type PGA_DATATYPE_REAL.
PGAGetRestartAlleleChangeProb returns the probability with which an allele will be mutated during a restart
PGAGetRestartFlag returns whether the algorithm should employ the restart operator
PGAGetRestartFrequencyValue returns the number of iterations of no change in the best string after which the algorithm should restart
PGAGetSelectType Returns the type of selection selected
PGAGetSortedPopIndex returns a population string index from the array created by PGASortPop().
PGAGetStoppingRuleType Returns a symbolic constant that defines the termination criteria.
PGAGetStringLength Returns the string length
PGAGetUniformCrossoverProb returns the probability of a bit being selected from the first child string in uniform crossover
PGAGetWorstIndex returns the index of the string with the worst evaluation function value in population pop
PGAHammingDistance Calculates the mean Hamming distance for a population of binary strings. For all other data types returns a value of 0.0 and prints a warning message.
PGAMean calculates the mean value of an array of elements
PGAMutate This routine performs mutation on a string.
PGAPrintContextVariable prints the value of all the fields in the context variable.
PGAPrintIndividual prints the allele values of a string and associated fields (evaluation, fitness, etc.) of a string
PGAPrintPopulation Calls PGAPrintIndividual to print each member of a population
PGAPrintReport prints genetic algorithm statistics. The statistics that are printed are determined by PGASetPrintOptions().
PGAPrintString write the allele values in a string to a file
PGARandom01 generates a uniform random number on the interval [0,1) If the second argument is 0 it returns the next random number in the sequence. Otherwise, the second argument is used as a new seed for the population
PGARandomFlip flip a biased coin and return PGA_TRUE if the coin is a "winner." Otherwise, return PGA_FALSE.
PGARandomGaussian returns an approximation to a Gaussian random number
PGARandomInterval returns a uniform random number on the specified interval
PGARandomUniform returns a uniform random number on the interval [start,end]
PGARank returns the rank of a string in a population. This is a value between 1,...,N (the population size). The most fit string has rank 1, the least fit string has rank N.
PGAReceiveIndividual receive an individual from another process
PGARestart reseeds a population from the best string
PGARound Mathematically round a double to an integer, using 0.5 as the cutoff value.
PGARun Highest level routine to execute the genetic algorithm. It is called after PGACreate and PGASetup have been called.
PGARunGM High-level routine to execute the genetic algorithm using the global model. It is called after PGACreate and PGASetup have been called. If a NULL communicator is given, a sequential execution method is used, otherwise, work is divided among the processors in the communicator.
PGARunMutationAndCrossover Performs crossover and mutation from one population to create the next. Assumes PGASelect has been called.
PGARunMutationOrCrossover Performs crossover or mutation (but not both) from one populationto create the next. Assumes PGASelect has been called.
PGASelect performs genetic algorithm selection using either the default selection scheme or that specified with PGASetSelectType().
PGASelectNextIndex returns the index of next individual in internal array that contains the indices determined by PGASelect
PGASendIndividual transmit an individual to another process
PGASendReceiveIndividual Send an individual to a process, while receiving a different individual from a different process.
PGASetBinaryAllele sets a binary allele to the specified value.
PGASetBinaryInitProb specify the probability of initializing an allele to "1" when creating a PGA_DATATYPE_BINARY string. The default value is 0.5.
PGASetCharacterAllele sets the value of an allele in a PGA_DATATYPE_CHARACTER string.
PGASetCharacterInitType sets a flag to specify whether the character strings will be exclusively lowercase, exclusively uppercase, or a mixure of both cases. Legal flags are PGA_CINIT_UPPER, PGA_CINIT_LOWER, and PGA_CINIT_MIXED. Default is PGA_CINIT_LOWER.
PGASetCommunicator Set the default communicator to use when PGARun is called. Does not necessarily need to be the same as the number of processes in MPI_COMM_WORLD (which is the default).
PGASetCrossoverProb Probability that a selected string will undergo crossover.
PGASetCrossoverType specify the type of crossover to use.
PGASetDebugLevel Turn on a debug level.
PGASetDebugLevelByName Turn on debugging of the named function.
PGASetEvaluation Set the evaluation function value for a string to a specified value. Also sets the evaulation up to date flag to PGA_TRUE.
PGASetEvaluationUpToDateFlag sets the flag associated with a string to PGA_TRUE or PGA_FLASE to indicate whether the evaluate function value is out-of-date or not. Note that this flag is always set to PGA_TRUE when PGASetEvaluation is called.
PGASetFitnessCmaxValue The value of the multiplier used by PGAFitnessMinCmax so that the worst string has a nonzero fitness. The default value is 1.01.
PGASetFitnessMinType sets the type of algorithm used if a minimization problem is specified to determine how values are remapped for maximization. Valid choices are PGA_FITNESSMIN_RECIPROCAL and PGA_FITNESSMIN_CMAX to do the mapping using the reciprocal of the evaluation function, or by subtracting the worst evaluation function value from each evaluation function value, respectively. The default is PGA_FITNESSMIN_CMAX
PGASetFitnessType Set the type of fitness algorithm to use. Valid choices are PGA_FITNESS_RAW, PGA_FITNESS_NORMAL, or PGA_FITNESS_RANKING for raw fitness (the evaluation function value), linear normalization, or linear ranking, respectively. The default is PGA_FITNESS_RAW.
PGASetIntegerAllele sets the value of a (integer) allele.
PGASetIntegerInitPermute sets a flag to tell the initialization routines to set each integer-valued gene to a random permutation of the values given by an upper and lower bound. The length of the interval must be the same as the string length. This is the default strategy for initializing integer-valued strings. The default interval is [0,L-1] where L is the string length. No string initialization is done by this call.
PGASetIntegerInitRange sets a flag to tell the initialization routines to set each integer-valued gene to a value chosen randomly from the interval given by an upper and lower bound. No string initialization is done by this call.
PGASetMaxFitnessRank The value of the parameter Max when using linear ranking for fitness determination. The default value is 1.2. The value must be from the interval [1.0, 2.0]. The fitness type must have been set to PGA_FITNESS_RANKING with PGASetFitnessType for this function call to have any effect.
PGASetMaxGAIterValue specify the maximum number of iterations for the stopping rule PGA_STOP_MAXITER (which, by itself, is the default stopping rule and is always in effect). The default value is 1000 iterations.
PGASetMaxNoChangeValue specifiy maximum number of iterations of no change in the evaluation function value of the best string before stopping. The default value is 50. The stopping rule PGA_STOP_NOCHANGE must have been set by PGASetStoppingRuleType for this function call to have any effect.
PGASetMaxSimilarityValue Specifiy the maximum percent of homogeneity of the population before stopping. The similarity measure is the same evaluation function value. The default value is 95 percent. The stopping rule PGA_STOP_TOOSIMILAR must have been set by PGASetStoppingRuleType for this function call to have any effect.
PGASetMutationAndCrossoverFlag A boolean flag to indicate if recombination uses both crossover and mutation on selected strings
PGASetMutationBoundedFlag If this flag is set to PGA_TRUE, then for Integer and Real strings whenever a gene is mutated, if it underflows (overflows) the lower (upper)bound it is reset to the lower (upper) bound.
PGASetMutationIntegerValue Set multiplier to mutate PGA_DATATYPE_INTEGER strings with.
PGASetMutationOrCrossoverFlag A boolean flag to indicate if recombination uses exactly one of crossover or mutation on selected strings.
PGASetMutationProb Specifies the probability that a given allele will be mutated.
PGASetMutationRealValue Set multiplier to mutate PGA_DATATYPE_REAL strings with.
PGASetNoDuplicatesFlag A boolean flag to indicate if duplicate strings are allowed in the population. Valid choices are PGA_TRUE and PGA_FALSE. The default is PGA_FALSE -- allow duplicates.
PGASetNumReplaceValue specifies the number of new strings to create each generation. The default is ten percent of the population size
PGASetPopReplaceType Choose method of sorting strings to copy from old population to new population. Valid choices are PGA_POPREPL_BEST, PGA_POPREPL_RANDOM_NOREP, or PGA_POPREPL_RANDOM_REP for copying the best strings, or random string, with or without replacement, respectively, from the old population into the new population. The default is PGA_POPREPL_BEST.
PGASetPopSize Specifies the size of the genetic algorithm population. The default population size is 100.
PGASetPrintFrequencyValue Specifies the frequency with which genetic algorithm statistics are reported. The default is every 10 GA iterations. Used only if PGARun() is used to run the GA.
PGASetPrintOptions set flags to indicate what GA statistics should be printed whenever output is printed. May be called more than once to specify different report options. Valid choices are PGA_REPORT_AVERAGE, PGA_REPORT_OFFLINE, PGA_REPORT_ONLINE, PGA_REPORT_WORST, PGA_REPORT_HAMMING, and PGA_REPORT_STRING to specify offline analysis, online analysis, the worst string in the population, the Hamming distance of the population, and the actual allele values of the best string. The best string is always printed.
PGASetPTournamentProb Specifies the probability that the string that wins a binary tournament will be selected.
PGASetRandomInitFlag A boolean flag to indicate whether to randomly initialize alleles. Legal values are PGA_TRUE and PGA_FALSE. Default is PGA_TRUE -- randomly initialize alleles.
PGASetRandomSeed set a seed for the random number generator. The default is to use a random seed. Specifying a seed exlicitly allows for reproducibility of runs.
PGASetRealAllele sets the value of real-valued allele i in string p in population pop
PGASetRealInitPercent sets the upper and lower bounds for randomly initializing real-valued genes. For each gene these bounds define an interval from which the initial allele value is selected uniformly randomly. With this routine the user specifies a median value and a percent offset for each allele.
PGASetRealInitRange sets the upper and lower bounds for randomly initializing real-valued genes. For each gene these bounds define an interval from which the initial allele value is selected uniformly randomly. The user specifies two arrays containing lower and bound for each gene to define the interval. This is the default strategy for initializing real-valued strings. The default interval is $[0,1.0]$ for each gene.
PGASetRestartAlleleChangeProb specifies the probability with which an allele will be mutated during a restart
PGASetRestartFlag specifies whether the algorithm should employ the restart operator
PGASetRestartFrequencyValue specifies the number of iterations of no change in the best string after which the algorithm should restart
PGASetSelectType specify the type of selection to use.
PGASetStoppingRuleType specify a stopping criterion. If called more than once the different stopping criterion are ORed together. Valid choices are PGA_STOP_MAXITER, PGA_STOP_TOOSIMILAR, or PGA_STOP_NOCHANGE to specify iteration limit reached, population too similar, or no change in the best solution found in a given number of iterations, respectively. The default is to stop when a maximum iteration limit is reached (by default, 1000 iterations).
PGASetUniformCrossoverProb Probability used in uniform crossover to specify that an allele value value be selected from a particular parent.
PGASetUp set all uninitialized variables to default values and initialize some internal arrays. Must be called after PGACreate() and before the GA is started.
PGASetUserFunction specifies the name of a user-written function call to provide a specific GA capability (e.g., crossover, mutation, etc.). This function MUST be used when using a non-native datatype and must be called once for each of: PGA_USERFUNCTION_CREATESTRING -- String creation PGA_USERFUNCTION_MUTATION -- Mutation PGA_USERFUNCTION_CROSSOVER -- Crossover PGA_USERFUNCTION_PRINTSTRING -- String Output PGA_USERFUNCTION_COPYSTRING -- Duplication PGA_USERFUNCTION_DUPLICATE -- Duplicate Checking PGA_USERFUNCTION_INITSTRING -- Initialization PGA_USERFUNCTION_BUILDDATATYPE -- MPI Datatype creation PGA_USERFUNCTION_STOPCOND -- Stopping conditions PGA_USERFUNCTION_ENDOFGEN -- Auxiliary functions at the end of each generation It MAY be called when using a native datatype to replace the built-in functions PGAPack has for that datatype (For example, if the Integer data type is used for a traveling salesperson problem, the user may want to provide their own custom crossover operator). See the user guide and the examples in the examples directory for more details.
PGASortPop Creates an (internal) array of indices according to one of three criteria. If PGA_POPREPL_BEST is used (the default) the array is sorted from most fit to least fit. If PGA_POPREPL_RANDOM_REP is used the indices in the array are selected randomly with replacement. If PGA_POPREPL_RANDOM_NOREP is used the indices in the array are selected randomly without replacement. The function PGASetPopReplaceType() is used to specify which strategy is used. The indices of the sorted population members may then be accessed from the internal array via PGAGetSortedPopIndex(). This routine is typically used during population replacement.
PGAStddev calculates the standard deviation of an array of elements
PGAUpdateGeneration updates internal data structures for the next genetic algorithm iteration, and checks if the termination conditions, both user and PGAPack, have been met. This routine must be called by both master and slave processes at the end of each GA generation.
pgbuiltin
pginterface
pgperl allow subroutines in the PGPLOT graphics library to be called from Perl.
pgpool_adm_pcp_attach_node a function to attach given node ID
pgpool_adm_pcp_detach_node a function to detach given node ID
pgpool_adm_pcp_health_check_stats a function to display health check statistics data on given node ID
pgpool_adm_pcp_node_count a function to retrieves number of backend nodes.
pgpool_adm_pcp_node_info a function to display the information on the given node ID
pgpool_adm_pcp_pool_status a function to retrieves parameters in pgpool.conf.

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