Search Results for "parallel computing datamaning"
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Showing 24 open source projects for "parallel computing datamaning"
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Dask
Parallel computing with task scheduling
Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines. -
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PyOpenCL
OpenCL integration for Python, plus shiny features
PyOpenCL is a Python wrapper for the OpenCL framework, providing seamless access to parallel computing on CPUs, GPUs, and other accelerators. It enables developers to harness the full power of heterogeneous computing directly from Python, combining Python’s ease of use with the performance benefits of OpenCL. PyOpenCL also includes convenient features for managing memory, compiling kernels, and interfacing with NumPy, making it a preferred choice in scientific computing, data analysis, and machine learning workflows that demand acceleration. -
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Lithops
A multi-cloud framework for big data analytics
Lithops is an open-source serverless computing framework that enables transparent execution of Python functions across multiple cloud providers and on-prem infrastructure. It abstracts cloud providers like IBM Cloud, AWS, Azure, and Google Cloud into a unified interface and turns your Python functions into scalable, event-driven workloads. Lithops is ideal for data processing, ML inference, and embarrassingly parallel workloads, giving you the power of FaaS (Function-as-a-Service) without vendor lock-in. ... -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best... -
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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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PyCNN
Image Processing with Cellular Neural Networks in Python
Image Processing with Cellular Neural Networks in Python. Cellular Neural Networks (CNN) are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors. -
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SAT-Assembler
Scalable and accurate targeted gene assembly for large-scale NGS data
...It recovers genes from gene families of particular interest to biologists with high coverage, low chimera rate, and extremely low memory usage compared with exiting gene assembly tools. Moreover, it is naturally compatible with parallel computing platforms. -
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aCompute
Aims to enable researcher to tap in to mobile computing capability
This is a software agent based computing program that will enable researchers and other users to tap in computing power of machine available by sharing work load on the fly with zero configuration on network & resources A self organizing agent program that will understand network and its resource. where as the only job left to researcher is to split up jobs in several chunks of programs either parallel or sequential jobs and go issue the job (A visual Modeler or Scripting support need to be yet designed) Software agents will automatically manage the rest or resource management, sharing , cloning of tasks etc. ... -
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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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corunner
A utility to transport files and execute at remote in prarallel
Are you maitaining many machines and bored with the tedious operation of logging to those machines one by one just for checking the machine's status? Or are you losing patient for your simple script that have to run command in those machines in sequence? The corunner is just for those senarios. It's designed to speed up execution in your machines. It can executes command in handreds of or thousands of machines concurrently. Provide it with your files and command and the machines' location,... -
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BriCS
SaaS for running simulation models in the cloud
The Bristol Cloud Service simulation runner is a cloud computing Software as a Service designed to enable users to quickly launch simulation code on Amazon AWS' Elastic Compute Cloud (EC2). BRiCS is written in Python using the Django framework and interacts with EC2 using the boto API. BriCS enables multiple simulation runs to be launched in parallel from a web browser. Model configuration (parameters) files are uploaded via the browser and results files are downloadable on completion. ... -
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AWK~plus is the next generation script practice environment. The AWK Language specifications and a main extension of GNU GAWK. Combination of Dynamic and Static typing. Parallel computing that a lock is free, and is thread safe at a language level.
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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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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.
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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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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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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.
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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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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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Simple Distributed Job Management Tools facilitates parallel, distributed execution of simple commands, on a network of UNIX-like machines. Components include a job dependency/exclusivity language and load-balanced remote execution facility.
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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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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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pySPACE
Signal Processing and Classification Environment in Python using YAML
pySPACE is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due...
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