Search Results for "parallel"
Sort By:
Showing 1501 open source projects for "parallel"
View related business solutions-
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
Earn up to 16% annual interest with Nexo.Generate interest, access liquidity without selling, and execute trades seamlessly. All in one platform. Geographic restrictions, eligibility, and terms apply.
-
1
parallel-ssh
Asynchronous parallel SSH client library.
parallel-ssh is an asynchronous parallel SSH library designed for large-scale automation. It differentiates itself from alternatives, other libraries and higher-level frameworks like Ansible or Chef in several ways. -
2
Orca
Orca is the ADE for working with a fleet of parallel agents
...Its focus is parallel agent work, development orchestration, and a more structured workspace for agent-driven coding. Overall, it is built for teams and individual developers who want to coordinate many AI coding sessions from one environment. -
3
Kreuzberg
Polyglot document intelligence framework
Kreuzberg is a flexible task orchestration and agent workflow platform designed to help developers build, coordinate, and scale intelligent agents or automation pipelines that interact with external services, runtime environments, and multi-step business workflows. It emphasizes modular design so that developers can define discrete tasks or “actions” and then compose them into complex flows where dependencies, parallel steps, and error handling are declaratively managed. This structure makes it simpler to build resilient automation pipelines that span multiple technologies or APIs without needing to glue everything together manually. The platform also supports observability and monitoring, helping teams trace the progression of workflows, catch errors early, and evaluate performance trends in orchestrated systems. -
4
The Futhark Programming Language
A data-parallel functional programming language
Futhark is a small programming language designed to be compiled into efficient parallel code. It is a statically typed, data-parallel, and purely functional array language in the ML family, and comes with a heavily optimizing ahead-of-time compiler that presently generates either GPU code via CUDA and OpenCL, or multi-threaded CPU code. Futhark is not designed for graphics programming, but can instead use the compute power of the GPU to accelerate data-parallel array computations. ... -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
5
Dagger.jl
A framework for out-of-core and parallel execution
Dagger.jl is a framework for out-of-core and parallel computing in Julia that allows users to construct and execute dynamic task graphs. It is designed for large-scale, distributed, and memory-efficient computations. Dagger supports lazy evaluation and scheduling across multiple threads or machines, enabling high-performance workflows for data processing, scientific computing, and machine learning. -
6
AudioBookConverter
Improved AudioBookConverter based on freeipodsoftware release
...The software supports a wide range of input formats including MP3, FLAC, and AAC, and provides flexible output options for different devices. It includes intelligent artwork handling, automatically sourcing and merging cover images from files or directories. The application uses parallel encoding to improve performance, making it efficient for large audiobook collections. It also supports advanced chapter management, including importing and editing chapter structures. Overall, it simplifies audiobook creation while maintaining high-quality output and metadata integrity. -
7
Emdash
Emdash is the Open-Source Agentic Development Environment
Emdash is an open-source agentic development environment that allows developers to orchestrate multiple AI coding agents in parallel within a unified desktop application. It introduces a new paradigm where coding tasks are delegated to independent AI agents, each operating in its own isolated Git worktree, enabling concurrent development workflows without conflicts. The platform is provider-agnostic, supporting a wide range of CLI-based AI coding tools such as Claude Code, Codex, and others, allowing developers to switch or combine models based on their needs. ... -
8
LAMMPS
Public development project of the LAMMPS MD software package
...LAMMPS is script-driven, so users can define simulation geometry, force fields, integration methods, output, and analysis behavior through input files. Its main value is combining broad scientific flexibility with scalable parallel performance for complex molecular and mesoscale simulations. -
9
golangci-lint
Fast linters Runner for Go
golangci-lint is a fast Go linters runner. It runs linters in parallel, uses caching, supports yaml config, has integrations with all major IDE and has dozens of linters included. ⚡ Very fast: runs linters in parallel, reuses Go build cache and caches analysis results. Yaml-based configuration. Integrations with VS Code, Sublime Text, GoLand, GNU Emacs, Vim, Atom, GitHub Actions. A lot of linters included, no need to install them. -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
10
Apache Beam
Unified programming model for Batch and Streaming
Apache Beam is an open source, unified programming model to define both batch and streaming data-parallel processing pipelines, as well as certain language-specific SDKs for constructing pipelines and Runners. These pipelines are executed on one of Beam’s supported distributed processing back-ends, which include Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Beam is especially useful for Embarrassingly Parallel data processing tasks, and caters to the different needs and backgrounds of end users, SDK writers and runner writers. -
11
compress
Optimized Go Compression Packages
...Optimized deflate packages which can be used as a dropin replacement for gzip, zip and zlib. snappy is a drop-in replacement for github.com/golang/snappy offering better compression and concurrent streams. huff0 and FSE implementations for raw entropy encoding. gzhttp Provides client and server wrappers for handling gzipped requests efficiently. pgzip is a separate package that provides a very fast parallel gzip implementation. Stream decompression is now faster on asynchronous since the goroutine allocation much more effectively splits the workload. On typical streams, this will typically use 2 cores fully for decompression. When a stream has finished decoding no goroutines will be left over, so decoders can now safely be pooled and still be garbage collected. -
12
mprocs
Run multiple commands in parallel
mprocs runs multiple commands in parallel and shows output of each command separately. When you work on a project you very often need the same list of commands to be running. For example: webpack serve, just --watch, node src/server.js. With mprocs you can list these commands in mprocs.yaml and run all of them by running mprocs. Then you can switch between the outputs of running commands and interact with them. -
13
Maestro AI Orchestration
Agent Orchestration Command Center
Maestro is a cross-platform desktop application designed for power users to orchestrate and manage fleets of AI agents and project workflows from a keyboard-centric interface. It provides a high-performance experience for running multiple agent sessions in parallel, integrating with tools such as Claude Code, OpenAI Codex, and other agent tooling to automate tasks, perform unattended execution, and organize long-running work flows. Users can collaborate with AI to draft specifications, break tasks into playbooks, and run them sequentially or concurrently in isolated contexts, each with clean session history. ... -
14
CUDA Python
Performance meets Productivity
...The project is designed to simplify GPU programming by offering Pythonic abstractions while still exposing the full power of CUDA for advanced users. It integrates tightly with the broader Python GPU ecosystem, including Numba for kernel compilation and CCCL for parallel primitives, allowing developers to write performant code without leaving Python. The toolkit also includes utilities for profiling, memory management, distributed computing, and numerical operations, making it suitable for scientific computing, AI, and data processing workloads. -
15
LobeHub
Workspace to find, build, and collaborate with AI agents
...LobeHub brings multiple models, tools, and modalities into a single unified environment under the user’s control. With built-in collaboration features, agents can work in parallel, share context, and support complex projects seamlessly. The platform is built around the idea of co-evolution, where both humans and agents continuously learn and improve together. -
16
StreamEx
Enhancing Java Stream API
This library defines four classes, StreamEx, IntStreamEx, LongStreamEx, DoubleStreamEx which are fully compatible with Java 8 stream classes and provide many additional useful methods. Also EntryStream class is provided which represents the stream of map entries and provides additional functionality for this case. Finally, there are some new useful collectors defined in MoreCollectors class as well as primitive collectors concept. -
17
future
R package: future: Unified Parallel and Distributed Processing in R
The future package in R provides a unified abstraction for asynchronous and/or parallel computation. It allows R expressions to be scheduled for future evaluation, with the result retrieved later, in a way decoupled from the specific backend used. This lets code be written in a way that works with sequential execution, multicore, multisession, cluster, or remote compute backends, without changing the high-level code. -
18
Prime Agent
A self-improving RLM agent for coding workflows
...Its Recursive Language Model architecture treats context as programmable variables and subagents as callable functions inside a persistent Python environment. Files, shell commands, tools, context operations, and agent coordination are handled programmatically through the same runtime. Built-in subagents can work in parallel or continue tasks independently while returning results to a parent agent. A continual harness stores memories, skills, prompts, and reusable agent specifications that can improve through evidence-backed refinement. Daemon-backed sessions survive terminal disconnections and can be resumed later. Automatic compaction, persistent goals, schedules, heartbeats, and autonomous execution help maintain progress across extended tasks. -
19
FileTrees.jl
Parallel file processing made easy
...When computing lazy trees, these values are held in distributed memory and operated on in parallel. -
20
HYPRE
Parallel solvers for sparse linear systems featuring multigrid methods
Livermore’s HYPRE library of linear solvers makes possible larger, more detailed simulations by solving problems faster than traditional methods at large scales. It offers a comprehensive suite of scalable solvers for large-scale scientific simulation, featuring parallel multigrid methods for both structured and unstructured grid problems. The HYPRE library is highly portable and supports a number of languages. Work on HYPRE began in the late 1990s. It has since been used by research institutions and private companies to simulate groundwater flow, magnetic fusion energy plasmas in tokamaks and stellarators, blood flow through the heart, fluid flow in steam generators for nuclear power plants, and pumping activity in oil reservoirs, to name just a few areas. ... -
21
101-0250-00
ETH course - Solving PDEs in parallel on GPUs
This course aims to cover state-of-the-art methods in modern parallel Graphical Processing Unit (GPU) computing, supercomputing and code development with applications to natural sciences and engineering. -
22
CubeSandbox
Instant, Concurrent, Secure & Lightweight Sandbox for AI Agents
...Built in Rust, it focuses on enabling concurrent execution while maintaining strong isolation and low overhead. The system is optimized for scenarios where multiple AI agents need to execute tasks in parallel without compromising system integrity. It provides fast startup times and efficient resource management, making it suitable for large-scale agent orchestration. CubeSandbox integrates well with cloud-native workflows and modern infrastructure pipelines. Its design prioritizes security, concurrency, and performance in AI-driven environments. ... -
23
Crawl4AI
Open-source LLM Friendly Web Crawler & Scraper
Crawl4AI is a high-performance, AI‑ready web crawler tailored for LLM data ingestion and RAG pipelines. It supports adaptive crawling heuristics (stopping when enough info is gathered), structured markdown output, and high-speed parallel execution. Designed to operate at scale with optional Docker deployment and framework integrations. -
24
prek
Better `pre-commit`, re-engineered in Rust
...By rewriting everything in Rust, prek delivers substantial performance gains and reduces disk usage while maintaining compatibility with existing .pre-commit-config.yaml setups, letting teams switch over without rewriting configurations. It’s optimized for modern workflows, supporting monorepos with workspace modes, parallel hook installation and execution, and native handling of common hook tasks without external runtimes. Because it ships as a single binary with no runtime dependencies and is already being adopted in large projects (like CPython, Apache Airflow, and FastAPI), prek is increasingly seen as a next-generation tool for developers who value performance, simplicity, and robust git hook automation. -
25
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.
