Search Results for "parallel" - Page 3

Do you have dozens of tools with dozens of parameters? KISSmenu helps you to organize them, to integrate them and to run them interactively in a terminal based menu or automatically, e.g. starting it up as a cron job. Development or tooling environment: shortcut based menu of commands, which can be used both interactively and non-interactively. Middleware: simply integrates various software through environment variables. Really simple! A 500 lines long bash script easy to maintain,...

MapReduce++ is a project for implementation of parallel algorithms. It has currently two C++ implementations of the MapReduce abstraction: the MapMP library (multiprocessors) and the MaPI framework (multicomputers).

A parallel-programming framework for concurrently running large numbers of small autonomous jobs, or microthreads, across multiple cores in a CPU or CPUs in a cluster. Each microthread is conceptually similar to a task in Ada and it is much lighter weight than an operating system thread. SpikeOS was designed to handle millions of microthreads, for example in a neural network hosting millions of spiking model neurons.

ETL Framework is a standalone Extract Transform Load engine written in Java. It includes executables for all major platforms and can be easily integrated into other applications. Key Features: * embeddable, open source and free * fast and scalable * uses target database features to do transformations and loads * manual and automatic data mapping * data streaming * bulk data loads * data quality features using SQL, JavaScript? and regex * data transformations Requirements *...

Java implementation of a Tuplespace. Moved to https://github.com/mike-k-houghton/tuplespace A Tuple is an ordered list of items. A Tuple Space is a form of associative memory where entries, tuples, are stored in the space and are retrieved using search criteria that are based on the intrinsic properties of the tuples. The two key operations are put get And that is it! There are refinements on these operations including, for example, timeouts where the tuple will only exist...

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 not force code that uses it to be.

Project provides a set of concurrent building blocks (Java & C/C++) that can be used to develop parallel/multi-threaded applications. Components are grouped into 4 categories: 1.Data Structures 2. Parallel Patterns 3.Parallel functions 4.Atomics and STM

A framework for creating automatically-assembling trees of objects. Intended to parallel another interface as strictly a data I/O structure (e.g. file load/save in parallel with a GUI displaying it.) Adaptable to any hierachical data format.

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

Python bindings for OpenCL, the open standard for parallel programming of heterogeneous systems

NimpSmartCard is a framework to interact with smart cards through PC/SC readers as well as test/production readers such as Micropross and Smartware. The framework comes with sample tasks and a sample terminal manager which supports scripts and logs

Parallelizable is a tiny framework in complement for java.util.conccurent api. Parallelizable is an abstract class to extend for making asynchronous parallel execution on a synchronous task. Very usefull if task often calls a very long process.

Meerkat is a distributed programming environment. It consists of a virtual machine which is suited to parallel processing. The data model is based on the concept of actors, although it is much more permissive than the traditional description.

Python Integrated Parallel Programming EnviRonment (PIPPER), Python pre-parser that is designed to manage a pipeline, written in Python. It enables automated parallelization of loops. Think of it like OpenMP for Python, but it works in a computer cluster

The intention of this project is to develop a library and framework which enables Java applications to use multicore processors. Several Ideas to help developers are as follows: Parallel For Work-Stealing Profiler for Parallel Applications ...

The CodeTime platform covers every aspect of parallel software from authoring, through distribution, to run-time. Its goals are: high programmer productivity; write once, run high performance anywhere; and wide acceptance.

PyGWA is a GPGPU library for Python. It contains Python bindings for AMD CAL and PyGWA.DP - a toy data-parallel programming API.

QuickMP (Quick Multi-Processing) is a simple cross-platform C++ API for generating parallel for loops in shared-memory programs, similar to OpenMP. It provides automatic scalable performance based on the number of available processors.

Dynamic Probe Class Library (DPCL) is an object based C++ class library that provides the necessary infrastructure to allow tool developers and sophisticated tool users to build parallel and serial tools through technology called dynamic instrumentation.

FastPara is a C++ programming framework for writing data-parallel applications in cluster environments. It builds the code framework for the data-parallel applications based on their data distribution declaration.

JSpasm is a pure Java package for developing event-driven, state-based software applications and packages. This small-footprint package will handle multiple models and large numbers of entities in parallel, and includes features to support SMP operation.

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).

The LisBON Framework is an adaptable framework for developing new parallel Memetic Algorithms (hybrid search algorithms for efficiently solving optimisation problems).