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developer is officially maintaining the package.
The rationale for changing this from "tech-pkg" to "pkgsrc-users" is
that it implies that any user can try to maintain the package (by
submitting patches to the mailing list). Since the folks most likely
to care about the package are the folks that want to use it or are
already using it, this would leverage the energy of users who aren't
developers.
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framework. The list of changes include:
* Modify compiler.mk so that "c" is always prepended to USE_LANGUAGES,
so we no longer need to say it in package Makefiles. Packages
should now append to USE_LANGUAGES instead of setting it.
* Create mk/compiler/f2c.mk which implements another pseudo-compiler
"f2c" that may be used with any C compiler backend, e.g.
PKGSRC_COMPILER= f2c ccache gcc
* Teach the various "real" compiler files, e.g., sunpro.mk, mipspro.mk,
etc., to use f2c if the native Fortran compiler isn't present.
Packages that use Fortran should now simply include the line:
USE_LANGUAGES+= fortran
in the package Makefile.
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in the process. (More information on tech-pkg.)
Bump PKGREVISION and BUILDLINK_DEPENDS of all packages using libtool and
installing .la files.
Bump PKGREVISION (only) of all packages depending directly on the above
via a buildlink3 include.
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Should anybody feel like they could be the maintainer for any of thewe packages,
please adjust.
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LINPACK is a collection of Fortran subroutines that analyze and solve
linear equations and linear least-squares probles. The package solves
linear systems whose matrices are general, banded, symmetric
indefinite, symmetric positive definite, triangular, and tridiagonal
square. In addition, the package computes the QR and singular value
decompositions of rectangular matrices and applies them to
least-squares problems. LINPACK uses column-oriented algorithms to
increase efficiency by preserving locality of reference.
LINPACK was designed for supercomputers in use in the 1970s and early
1980s. LINPACK has been largely superceded by LAPACK which has been
designed to run efficiently on shared-memory, vector supercomputers.
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