GarlicSim is a platform for writing, running, and analyzing simulations. It is general enough to handle any kind of simulation: physics, game theory, epidemic spread, electronics, etc. GarlicSim aims to eliminate the need to write any boilerplate code that isn't directly related to the phenomenon you're simulating. GarlicSim defines a new format for simulations, called a simulation package and often abbreviated as simpack. The simpack contains all the code that define the simulated system, and is simply a Python package which defines a few special functions according to the GarlicSim simpack API. Simpack code may also be written in C. All of the tools that GarlicSim provides can be used to run simulations of all kinds of different domains.
The underling library provides simple, scalable means to manipulate MPI-parallel, three dimensional pencil decompositions using FFTW. Pencil decompositions are a natural way to distribute O(n^3) data across O(n^2) processors and are well-suited for memory-intensive, structured spectral turbulence simulations and postprocessing codes. It may be useful in other domains as well. The library is written in C99 and may be used by C89 or C++ applications.
Sally is a tool for mapping a set of strings to a set of vectors. This mapping is referred to as embedding and allows techniques of machine learning and data mining to be applied for the analysis of string data. It can be used with data such as text documents, DNA sequences, or log files. The vector space model or bag-of-words model is used. Strings are characterized by a set of features, where each feature is associated with one dimension of the vector space. Occurrences of the features in each string are counted. Alternatively, binary or TF-IDF values can be computed. Vectors can be output in plain text, LibSVM, or Matlab format.
Meta.Numerics is a Mono-compatible .NET library for scientific and numerical programming. It includes functionality for matrix algebra (including SVD, non-symmetric eigensystems, and sparse matrices), special functions of real and complex numbers (including Bessel functions and the complex error function), statistics and data analysis (including PCA, logistic and nonlinear regression, statistical tests, and nonuniform random deviates), and signal processing (including arbitrary-length FFTs).
GriF is a collaborative grid framework to support computational chemistry applications. It is meant to be used as a tool to facilitate massive grid calculations and also to improve scientific collaboration. Accordingly, GriF facilitates profiling the users of grid communities in order to systematically evaluate the work carried out in a grid and to foster its sustainability.