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librsb is a library for sparse matrix computations featuring the Recursive Sparse Blocks (RSB) matrix format. This format allows cache-efficient and multithreaded (that is, shared memory parallel) operations on large sparse matrices. The most common operations necessary to iterative solvers are available (matrix-vector multiplication, triangular solution, rows/columns scaling, diagonal extraction/setting, blocks extraction, norm computation, formats conversion). The RSB format is especially well-suited for symmetric and transposed multiplication variants. On these variants, librsb has been found to be faster than Intel MKL's implementation for CSR. Most numerical kernels code is auto-generated, and the supported numerical types can be chosen by the user at buildtime. librsb implements the Sparse BLAS standard, as specified in the BLAS Forum documents.

SUNDIALS (SUite of Nonlinear and DIfferential/ALgebraic
equation Solvers) provides robust time integrators and
nonlinear solvers that can easily be incorporated into
existing simulation codes. It requires minimal information
from the user, allow users to easily supply their own data
structures underneath the solvers, and allows for easy
incorporation of user-supplied linear solvers and
preconditioners.

PLplot is a library of C functions that are useful
for making scientific plots from programs written
in a wide variety of languages. It can be used to
create standard x-y plots, semi-log plots, log-log
plots, contour plots, 3D plots, shade (gray-scale
and color) plots, mesh plots, bar charts, and pie
charts. Multiple graphs may be placed on a single
page with multiple lines in each graph. Different
line styles, widths, and colors are supported. A
virtually infinite number of distinct area fill
patterns may be used. A variety of output devices
and file formats are supported.

PCP (Pattern Classification Program) is a machine learning
program for supervised classification of
patterns. It runs in interactive and batch modes, and
implements the following machine learning algorithms and
methods: k-means clustering, Fisher's linear discriminant,
dimension reduction using Singular Value Decomposition,
Principal Component Analysis, feature subset selection,
Bayes error estimation, parametric classifiers (linear and
quadratic), pseudo-inverse linear discriminant, k-Nearest
Neighbor method, neural networks, Support Vector
Machine algorithm (SVM), model selection for SVM, cross-validation, and bagging
(committee)
classification.

GetDP is a general finite element solver using
mixed elements to discretize de Rham-type
complexes in one, two, and three dimensions. The
main feature of GetDP is the closeness between the
input data defining discrete problems (written by
the user in ASCII data files) and the symbolic
mathematical expressions of these problems.