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Distributed and Parallel Computing with/for Python.
dispy is a generic and comprehensive, yet easy to use framework for creating and using compute clusters to execute computations in parallel across multiple processors in a single machine (SMP), among many machines in a cluster, grid or cloud. dispy is well suited for data parallel (SIMD) paradigm where a computation (Python function or standalone program) is evaluated with different (large) datasets independently.
dispy supports public / private / hybrid cloud computing, fog / edge computing.
Pydusa is a package for parallelprogramming using Python. It contains a module for doing MPI programming in Python. We have added parallel solver packages such as Parallel SuperLU for solving sparse linear systems.
LIME (Less-is-More) is parallel/concurrent programming environment based on C. Internally, it uses XML technology to describe tasks and their dependencies. Externally, it offers the ANSI C99 programming as well as a set of visually-oriented interfaces.
A data parallel scientific programming model. Compiles efficiently to different platforms like distributed memory (MPI), shared memory multi-processor (pthreads), Cell BE processor, Nvidia Cuda, SIMD vectorization (SSE, Altivec), and sequential C++ code.
The project is a data-parallel C++ library to teach parallelprogramming concepts. We intend to make this library portable, small, and easy to extend to work with real parallel message passing systems such as MPI and PVM.
Darrell Raymond Ulm
This project is setup to maintain a set of Javaparty codes.
This includes Javaparty benchmarks and applications.
Javaparty (http://www.ipd.uka.de/JavaParty/) is a Java extension for easy and efficient parallelprogramming.
It does so by extending the
MassiveJava is a Java-based environment for parallelprogramming. Lithium is able to execute the parallel application using a cluster or a network of workstations. A user is able to program a parallel application without take in account problem like sche