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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.
Python Integrated ParallelProgramming 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
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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.
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