php_mt_seed finds possible seeds given the very first PHP mt_rand() output after possible seeding with mt_srand(). With advanced invocation modes, php_mt_seed is also able to match multiple, non-first, and/or inexact mt_rand() outputs to possible seed values. php_mt_seed is written in C with optional SIMD intrinsics (SSE4.1/AVX, XOP, AVX2, MIC) and OpenMP. On a modern quad-core CPU, it is able to search the full 32-bit seed space in one minute (or in just seven seconds on Xeon Phi).

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.

SHTns is a high-performance Spherical Harmonic Transform library. It was designed for numerical simulation (fluid flows, mhd, etc.) in spherical geometries, but can be used for any kind of problem involving scalar or vector spherical harmonics. It is very fast, thanks to careful vectorization and runtime tuning. It supports multi-threaded transforms via OpenMP. It features scalar and vector transforms, synthesis and analysis, and flexible truncation and normalization. A Python interface is included.

Elemental is a C++ framework for distributed-memory dense linear algebra that strives to be fast, portable, and programmable. It can be thought of as a generalization of PLAPACK to element-by-element distributions that also makes use of recent algorithmic advances from the FLAME project. Elemental usually outperforms both PLAPACK and ScaLAPACK, however, it heavily relies on MPI collectives so a good MPI implementation is crucial. Both pure MPI and hybrid OpenMP-MPI configurations are supported.

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.