Search Results for "parallel" - Page 21
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Showing 1824 open source projects for "parallel"
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dft_installer.sh
Automated Installer for VASP, SIESTA, their libraries and utilities
Automated Installer for VASP, SIESTA, and their libraries and utilities for serial and parallel modes in a Linux environment. (dft_installer.sh) Gustavo Domínguez Rodríguez, Gabriel Iván Canto Santana, Jorge Alejandro Tapia Gonzalez, Cesar Alberto Cab Cauich High-performance scientific software is commonly compiled on the computing system, for optimizing it according to its specific characteristics. However, this compilation is often a tedious and limiting process, which frequently becomes an obstacle for its use. ... -
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cwdaemon
morse daemon for the serial or parallel port
This project is no longer actively developed on SourceForge. Its development has been fully moved to https://github.com/acerion/cwdaemon on 2024.08.03. ------ cwdaemon is a small daemon which uses the PC parallel or serial port and a simple transistor switch to output morse code to a transmitter from a text message sent to it via the udp internet protocol. The program uses the soundcard or PC speaker to generate a sidetone. Current version of cwdaemon is 0.12.0, it has been released in May 2023. Source code for cwdaemon is available on github: https://github.com/acerion/cwdaemon -
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Nginx-Clojure
Nginx module for embedding Clojure or Java or Groovy programs
...The non-blocking coroutine-based socket is Compatible with Java Socket API and works well with largely existing java libraries such as apache HTTP client, mysql jdbc drivers. With this feature, one java main thread can handle thousands of connections. Handle multiple sockets parallel in sub coroutines. -
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WFEF .NET Bindings
.NET bindings for the WFEF project.
Home Browse Open Source WFIO .NET Bindings WFIO .NET Bindings .NET bindings for WFIO Status: Pre-Alpha Brought to you by: thylordroot Add a Review Downloads: 0 This Week Last Update: 2023-01-25 Browse Code Get Updates Share This Windows Mac Linux BSD ChromeOS Summary Reviews Support Code This subproject contains a parallel implementation effort for the .NET Virtual Machine. It allows for you to use the WFEF interface in your .NET applications and will include a translation layer so that you can talk to the native WFEF libraries. This subproject exists partially to overcome the 8.3 file naming convention that WFEF itself is limited to. -
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Sqlite Index Blaster
Create huge Sqlite indexes at breakneck speeds
SQLite Blaster is an advanced SQLite extension that enhances database performance by enabling multi-threading, data compression, and memory optimizations. It is designed for applications that require fast local storage with improved query efficiency. -
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ElegantRL
Massively Parallel Deep Reinforcement Learning
ElegantRL is an efficient and flexible deep reinforcement learning framework designed for researchers and practitioners. It focuses on simplicity, high performance, and supporting advanced RL algorithms. -
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GPT-NeoX
Implementation of model parallel autoregressive transformers on GPUs
This repository records EleutherAI's library for training large-scale language models on GPUs. Our current framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. We aim to make this repo a centralized and accessible place to gather techniques for training large-scale autoregressive language models, and accelerate research into large-scale training. For those looking for a TPU-centric codebase, we... -
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winLAME
winLAME is an easy to use encoder for many audio formats, e.g. MP3.
winLAME is an easy to use encoder for many audio formats, including MP3, Opus, Ogg Vorbis and more. winLAME lets you read in audio tracks from CDs or encode audio files from your hard drive. The intuitive wizard-style user interface makes it easy to set up encoding settings. Multicore CPUs are used to parallelize encoding tasks. Official GitHub project webpage: https://github.com/vividos/winLAME -
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o'scopepy
Oscilloscope using PC sound card and Python 3
Version 6 uses a programming paradigm based on workcells which communicate with each other using text based signals. This creates a parallel programming environment with autonomous virtual machines communicating over a peer to peer network. The workcells are instantiated anonymously, therefore all methods and variables are completely private. Tkinter is used for both GUIs and for creating a second event loop used for synchronizing workcell to workcell communication. -
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Mars Framework
Mars is a tensor-based unified framework for large-scale data
...The project provides a tensor-based execution model that extends the capabilities of tools such as NumPy, pandas, and scikit-learn so that large datasets can be processed in parallel without rewriting code for distributed environments. Its architecture automatically divides large computational tasks into smaller chunks that can be executed across multiple nodes in a cluster, allowing complex analytics, machine learning workflows, and data transformations to run efficiently at scale. Mars is particularly useful for workloads that exceed the memory capacity of a single machine or require high levels of parallel processing. -
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Scrapyd
A service daemon to run Scrapy spiders
...Scrapyd is an application (typically run as a daemon) that listens to requests for spiders to run and spawns a process for each one. Scrapyd also runs multiple processes in parallel, allocating them in a fixed number of slots given by the max_proc and max_proc_per_cpu options, starting as many processes as possible to handle the load. -
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FairScale
PyTorch extensions for high performance and large scale training
FairScale is a collection of PyTorch performance and scaling primitives that pioneered many of the ideas now used for large-model training. It introduced Fully Sharded Data Parallel (FSDP) style techniques that shard model parameters, gradients, and optimizer states across ranks to fit bigger models into the same memory budget. The library also provides pipeline parallelism, activation checkpointing, mixed precision, optimizer state sharding (OSS), and auto-wrapping policies that reduce boilerplate in complex distributed setups. ... -
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TextBox
A text generation library with pre-trained language models github.com
...From a model perspective, we incorporate 47 pre-trained language models/modules covering the categories of general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight models (modules). From a training perspective, we support 4 pre-training objectives and 4 efficient and robust training strategies, such as distributed data parallel and efficient generation. Compared with the previous version of TextBox, this extension mainly focuses on building a unified, flexible, and standardized framework for better supporting PLM-based text generation models. -
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OpenXR-Hpp project
Open-Source OpenXR C++ language projection
...If you just want to generate the headers, run ./generate-openxr-hpp.sh or ./generate-openxr-hpp.ps1. If your OpenXR-SDK-Source (or internal gitlab) repo isn't in a directory named that parallel to this one, you can set OPENXR_REPO environment variable before running. Requires clang-format, preferably 6.0. To build this project, you must have OpenXR-SDK-Source cloned in a peer directory of this one. -
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OK
Welcome to the future of programming languages
...The language emphasises readability and pushing logic out into functions so cases remain simple. It includes concurrency via a map function that executes callbacks in parallel. The project is illustrative of Duffield’s vision: code should feel “magical to write” by removing what is unnecessary. -
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WFIO .NET Bindings
.NET bindings for WFIO
This subproject contains a parallel implementation effort for the .NET Virtual Machine. It allows for you to use the WFIO interface in your .NET applications and will include a translation layer so that you can talk to the native WFIO libraries. This subproject exists partially to overcome the 8.3 file naming convention that WFIO itself is limited to. -
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FLoops.jl
Fast sequential, threaded, and distributed for-loops for Julia
Fast sequential, threaded, and distributed for-loops for Julia, fold for humans.FLoops.jl provides a macro @floop. It can be used to generate a fast generic sequential and parallel iteration over complex collections. Furthermore, the loop written in @floop can be executed with any compatible executors. See FoldsThreads.jl for various thread-based executors that are optimized for different kinds of loops. FoldsCUDA.jl provides an executor for GPU. FLoops.jl also provides a simple distributed executor. -
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webotron
Using industrial automation techniques for creating web scraping tools
Industry uses machines that can easily maim or kill their operators and is also used in very adverse environments. In spite of this, production quality must be close to perfect without reliance on operator skill or attentiveness. Control programs must be robust, yet simple enough to be understood and maintained by non programmer skilled trades like electricians . The main programming model is the PLC which implements double buffering and an event loop. The most advanced production model... -
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ImageJ2x
Java Image Processing Program
...The program supports simultaneous that number of windows (images), limited only by available memory. It is multithreaded, so that time-consuming operations such as reading lists in parallel with other operations are performed. It can calculate area and pixel value statistics of user-defined selection. It can measure distances and angles. It can record density histograms and line profiles. It supports standard image processing functions such as contrast manipulation, sharpening, smoothing, edge detection and filtering it through all kinds of geometric transformations such as Zoom in / out and rotation. ... -
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ThreadsX.jl
Parallelized Base functions
...The public API functions of ThreadsX expect that the data structure and function(s) passed as argument are "thread-friendly" in the sense that operating on distinct elements in the given container from multiple tasks in parallel is safe. For example, ThreadsX.sum(f, array) assumes that executing f(::eltype(array)) and accessing elements as in array[i] from multiple threads is safe. -
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OpenProject Community Ed Virtual Machine
Free & Open Source - Project / Task / Time & Team Management Software
Please click the Files Tab above to get all the related files of this system. ( Right Click Each File, to Open Them in New Tab, one by one, to download them all ) This is a Complete Virtual Machine, with Web Based OpenProject Software, for all sorts of Project Management / Task Management / Time Management & Team Management Activities. This Web Based Software can be operated by all Teams, in parallel, on your local and trusted work network. Have a look at the Video below for details. Refer Wiki for more instructions. -
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SkePi
Data parallel and stream parallel skeletons implemented in erlang.
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Vue Query
Hooks for fetching, caching and updating asynchronous data in Vue
...Transport/protocol/backend agnostic data fetching (REST, GraphQL, promises, whatever!) Auto Caching + Refetching (stale-while-revalidate, Window Refocus, Polling/Realtime) Parallel + Dependent Queries. Mutations + Reactive Query Refetching. Multi-layer Cache + Automatic Garbage Collection. Paginated + Cursor-based Queries. Load-More + Infinite Scroll Queries w/ Scroll Recovery. Request Cancellation. (experimental) Suspense + Fetch-As-You-Render Query Prefetching (experimental) SSR support. If you need to update options on your query dynamically, make sure to pass them as reactive variables. -
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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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Elephas
Distributed Deep learning with Keras & Spark
...Elephas intends to keep the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models, which can be run on massive data sets. Elephas implements a class of data-parallel algorithms on top of Keras, using Spark's RDDs and data frames. Keras Models are initialized on the driver, then serialized and shipped to workers, alongside with data and broadcasted model parameters. Spark workers deserialize the model, train their chunk of data and send their gradients back to the driver. The "master" model on the driver is updated by an optimizer, which takes gradients either synchronously or asynchronously. ...
