Search Results for "parallel computing datamaning"

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.

Useful to split up jobs over multiple CPU cores or even multiple computers. Examples: tar -cf - /bigdirectory | splitjob -j 4 gzip > big.tar.gz splitjob "ssh h1 bzip2" "ssh h2 bzip2" < f > f.bz2

A framework to run MATLAB programs as batch jobs. Features a structured input description, integrity constraints and GUI.Independent parts of a job can execute in parallel on a cluster computer. Developed at Freiburg Brain Imaging (FBI) - http://fbi.uniklinik-freiburg.de/

...With a focus on the MILP methods we implement a load balancing and speed up the solving process in a multiplicative way. Sometimes we have super-linear speedup with a small set of hardware. With a splitting of problems, parallel computing and distributing the actual best solution to all running processes we solve CBP much faster than a sequential processing can do.

Ichnaea is a set of tools that aid in collecting and tracking parameters and timings for parallel applications. The Performance Modelling Timing Module, PMTM, is a library that wraps system timing calls to abstract these from code developers and aid portability. It also has functionality to store parameters and print those, along with the timing information to a comma separated variable file. The Performance Modelling Analysis Tool, PMAT, is coming soon. This is able to read in and...

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. NeuraNEP emulates a distributed processing environment capable of handling millions of microthreads in parallel, for example running neural networks with millions of spiking cells. Microthreads are general processing elements that can also represent non-neural elements, such as cell populations, extracellular space, emulating...

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.

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).

MapPSO is a tool for Ontology Alignment, which uses Discrete Particle Swarm Optimisation. A particle swarm is used to search for the optimal alignment. The algorithm is massively parallel and adapts naturally on parallel architectures.

Platform for parallel computation in the Amazon cloud, including machine learning ensembles written in R for computational biology and other areas of scientific research. Home to MR-Tandem, a hadoop-enabled fork of X!Tandem peptide search engine.

Equalizer is the standard middleware to create parallel OpenGL-based applications. Please visit https://github.com/Eyescale for current development information.

This project to be release for parallel computing

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

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.

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.

Distributed Parallel Programming for Python! This package builds on traditional Python by enabling users to write distributed, parallel programs based on MPI message passing primitives. General python objects can be messaged between processors. Ru

Roomy is a programming language extension for writing parallel disk-based applications. All details of parallelism and disk I/O are hidden within the Roomy library.

PyMW is a Python module for parallel master-worker computing in a variety of environments. With the PyMW module, users can write a single program that scales from multicore machines to global computing platforms.

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.

Numerical package Parallel EXECution. Using a numerical package like Octave, 'npexec' distribute computation of a math formula among various network elements, collecting result automatically. ->y=npexec('fft(x.^m)', 'm=-8:8','x=ARG');

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.

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.

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.

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 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.