Search Results for "parallel computing"
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Showing 33 open source projects for "parallel computing"
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PelicanHPC is an iso-hybrid (CD or USB) image that let's you set up a high performance computing cluster in a few minutes. A Pelican cluster allows you to do parallel computing using MPI. You can run Pelican on a single multiple core machine to use all cores to solve a problem, or you can network multiple computers together to make a cluster. The frontend node (either a real computer or a virtual machine) boots from the image.
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dispy
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. -
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PuSSH is Pythonic, Ubiquitous SSH, a Python wrapper/script that runs commands in parallel on clusters/ranges of linux/unix machines via SSH, ideally where SSH is configured to use Kerberos, RSA/DSA keys, or ssh-agent as to avoid password authentication.
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GXP is a parallel/distributed shell, plus a parallel task execution engine that runs your Makefile in parallel on distributed machines. Very easy to install (no need to compile. install it on YOUR machine and use it on ALL machines).
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PyDSH is a remote administration tool, consisting of pydsh and pydcp. Pydsh allows you to run a command on multiple hosts in parallel over RSH, SSH or Telnet, OR manage your SSH public keys. The pydcp command allows copying files to/from multiple hosts.
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Simdist lets you harness the power of cluster computing without any knowledge of parallel libraries such as MPI, and with no restrictions on programming language. Primarily targeted at evolutionary computing and similar master-slave configurations.
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GridSim allows modeling and simulation of entities in parallel and distributed computing systems such as users, applications, resources, and resource brokers/schedulers for design and evaluation of scheduling algorithms. http://www.gridbus.org/gridsim
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RT-BOINC stands for a Real-Time BOINC. It was designed for managing highly-interactive, short-term, and massively-parallel real-time applications. We implemented RT-BOINC on top of the recent BOINC server source codes.
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BSPonMPI is an implementation of the BSPlib standard on top of MPI. Both MPI and BSPlib are API's of communication routines meant for parallel computing, but BSPlib is easier to learn and its performance easier to predict.
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NetWorkSpaces, developed by Scientific Computing Associates Inc., provides a framework to coordinate programs written in R. It allows users to write parallel programs in R easily.
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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
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PPIV is a parallel software application used in Particle Image Velocimetry (PIV) analysis. The software will compute the vector field for one image pair or multiple image pairs. PPIV can run on a single desktop machine or in a cluster environment.
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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.
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ClusterIt is a collection of clustering tools to allow simple administration of groups of UNIX machines. It can also be used to facilitate parallel shell scripting, and compiling.
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The underlayer is a symmetric decentralized parallel computing and load balancing framework, providing c, perl and java APIs for distributed job management.
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SimParEx executes a program(command) on many computers (farm) in parallel and collects the results (task farming). Major features: minimal requirements (TCP, SSH, Perl), flexible task definition, web interface.
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This tool lets you login with SSH to a group of machines and interact with them as if they were one machine. Each command that you enter is run in parallel on all machines in the group. The output from each machine is printed separately.
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This project aims to modify Parallel Virtual Machine (PVM) to use benefits of RDMA-capable network interconnects, commonly used in HPC clusters.
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Personal supercomputer on a disk! The Science by FlashMob project creates specialized live booting CDs that create a single user supercomputer from a set of handy laptop or desktop machines. Suitable for MPI, CHARM++, and pyMPI parallel programs.
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Maui Scheduler is an advanced reservation HPC parallel batch scheduler for use with Linux and BSD clusters. Maui provides a complete scientific scheduling solution, supporting running custom parallel and MPI jobs over Myrinet and ethernet.
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The software provides a means to form a simple Java cluster. It can be also viewed as a parallel computing library in Java. The project is based on the following <a href='http://www-106.ibm.com/developerworks/java/library/j-super.html'>article</a>
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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 parallel programming. It does so by extending the
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Web Textual eXtraction Tools C++ Parallel web crawler, noun phrase idenification, Multi-lingual Part of Speech Tagging, Tarjan's Algorithm, Co-RelationShip Mappings...
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Netcluster is a framework for writing parallel programs. Its main objective is to utilize the idle time of computers on a LAN by distributing tasks amongst them. The package consists of a daemon running on all volunteering machines, and a library to provi
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TOP-C is a library for easily writing parallel applications for both distributed and shared memory architectures. It hides the details of parallel programming, and presents the application programmer with a simple task-oriented interface.
