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.
jMathLab is a platform for mathematical and numerical computations. It uses the Matlab/Octave programming language. It runs on any platform where Java is installed, and can also run on the Web browser. The following packages are included: symbolic calculations (simplification, differentials, integration), numeric calculations, evaluations of mathematical functions, special functions, linear algebra with vectors and matrices, plotting data and functions, saving data (vectors and matrices) in files, random numbers, statistics, and solving linear and non-linear equations
OpenMPF is a library for solving large, dense, multi-RHS linear systems. It is based on MPI/openMP parallelism, and relies on BLAS/LAPACK/MUMPS for the single node computations. It implements direct and iterative solvers, out-of-core matrices and vectors, and is easily accessible through a Python interface.