Search Results for "parallel desktop"

SoundHelix is a free versatile Java framework for algorithmic random music composition. SoundHelix can generate random songs, play them (for example, on any number of MIDI devices or MIDI-capable software synthesizers) and is highly customizable using XML configuration. SoundHelix also allows writing the generated music as MIDI files. SoundHelix produces music that is actually enjoyable to listen to.

pFUnit is now hosted and developed on GitHub. And will be completely removed from sourceforge on January 01, 2020 Please use: https://github.com/Goddard-Fortran-Ecosystem/pFUnit GitHub has been the primary host for some time now, but with the release of pFUnit 4.0 earlier in 2019, the sourceforge site is very out of date.

With the help of the ProgramNet the user can develop plug-ins(called units) which can be connected with other units forming a net. The net has a form of a flow diagram. The resulted net can be executed in the ProgramNet for testing purposes or independently in the target platform as a final release. In order to execute a net the ProgramNet uses the kernel. The kernel executes automatically each unit in a separate thread and manages the data exchange between the units.

AshTL aims to provide a complete framework for dealing with spatial data in an efficient, parallel manner. As such, we primarily provide container classes and data types relating to 2D and 3D coordinate grids. AshTL is currently in alpha development (some features may not work correctly, and interfaces are likely to change). While in alpha and beta development, AshTL is licensed under AGPL v3+. Stable releases will include an exception so that AshTL itself will always be AGPL, but will...

SNAP (Small-world Network Analysis and Partitioning) is an extensible parallel framework for exploratory analysis and partitioning of large-scale networks.

A small but extensible Java based "embedded lisp"- derives from the 1960 Mc Carthy Lisp a new consequently functional, dynamically scoped dialect enriched by modern features (pattern matching, parallel processing, implicit lex. closures etc).