Search Results for "parallel computing"
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Showing 42 open source projects for "parallel computing"
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HDF5
Official HDF5® Library Repository
HDF5 (Hierarchical Data Format v5) is a widely-used data management library and file format for storing large and complex scientific data sets efficiently. -
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Octave Forge
A collection of packages providing extra functionality for GNU Octave
Octave Forge is a central location for collaborative development of packages for GNU Octave. The Octave Forge packages expand Octave's core functionality by providing field specific features via Octave's package system. See https://octave.sourceforge.io/packages.php for a list of all available packages. GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and... -
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splitjob
Reads from stdin, splits and sends to parallel invocations of commands
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 -
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PMCGPU
Parallel simulators for Membrane Computing on the GPU
Membrane Computing is a new research area (within Natural Computing) that aims to provide computing devices abstracted from the functioning and structure of living cells. These devices are called P systems. The objective of this project (PMCGPU) is to bring together all the researchers working on the development of parallel simulators for P systems, specially those using the GPU (e.g. -
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Chapel
a Productive Parallel Programming Language
Chapel is an emerging parallel programming language whose design and development are being led by HPE in collaboration with academia, computing labs, and industry. Chapel's goal is to improve the productivity of parallel programmers, from laptops to supercomputers. **Please note that Chapel development has moved to GitHub** -
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Parallel MILC
Parallel decompression for the MILC algorithm
The MILC compression has been developed specifically for medical images and proven to be effective. This algorithm is a parallel version for the decompression phase, meant to exploit the parallel computing potential of the modern hardware. Developed with OpenCL framework (version 1.2), runs on CPU and GPU OpenCL devices. -
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CUDA-Quicksort
CUDA-Quicksort: A GPU-based implementation of the quicksort algorithm
CUDA-quicksort is a GPU-based implementation of the quicksort algorithm. CUDA-quicksort is designed to exploit the power computing of modern NVIDIA GPUs. "Two GPU-based implementations of the quicksort were presented in literature: the GPU-quicksort, a compute-unified device architecture (CUDA) iterative implementation, and the CUDA dynamic parallel (CDP) quicksort, a recursive implementation provided by NVIDIA Corporation."[*]. CUDA-quicksort is an iterative GPU-based implementation of the quicksort algorithm. ... -
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Ichnaea
Performance Timing Tools
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... -
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neuranep
Neural Network Engineering Platform
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... -
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mapgraph
Massively Parallel Graph processing on GPUs -- now part of Blazegraph
Mapgraph is SYSTAP’s disruptive new technology to exploit the main memory bandwidth advantages of GPUs. The early work was co-developed with the University of Utah SCI Institute and has its pedigree in the UINTAH software running on over 750M cores on the TITAN Super Computer. Today, SYSTAP has commercialized this technology into it’s Blazegraph Accelerator and Blazegraph HPC products. Checkout our options for GPU acceleration of graphs or contact us to learn more: ... -
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GENIE (GEne-geNe IntEraction)
GPU based Parallel Gene-Gene Interaction Analysis
Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic... -
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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
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Cca-forum unifies the Common Component Architecture tools and tutorial. It includes the CCA specifications, the Ccaffeine framework for HPC, and related tools. These support multilanguage scientific and parallel computing.
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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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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
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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.
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Tsunami is an open-source high performance computing language. With it you can write streaming data-parallel algorithms that utilize GPGPUs for orders-of-magnitude speed-up with the ease of writing sequential algorithms.
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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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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');
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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.
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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.
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This project involves implementing simulations of Enigma machines and the Turing bombe on various parallel-computing systems including multi-processor PCs, Linux clusters, and modern enhanced graphic cards.
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Pydusa is a package for parallel programming 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.
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Open FMM ( http://www.openfmm.net ) is and open source EM software project. It consist of a fast parallel two-dimensional TM/TE solver called Nero2d.
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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.
