Search Results for "parallel"
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Showing 996 open source projects for "parallel"
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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. -
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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. -
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EnvPool
C++-based high-performance parallel environment execution engine
EnvPool is a fast, asynchronous, and parallel RL environment library designed for scaling reinforcement learning experiments. Developed by SAIL at Singapore, it leverages C++ backend and Python frontend for extremely high-speed environment interaction, supporting thousands of environments running in parallel on a single machine. It's compatible with Gymnasium API and RLlib, making it suitable for scalable training pipelines. -
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CUDA Core Compute Libraries (CCCL)
CUDA Core Compute Libraries
...It brings together Thrust, CUB, and libcudacxx, which collectively provide high-level abstractions, low-level performance primitives, and a CUDA-compatible standard library for GPU programming. The goal of CCCL is to simplify CUDA development by offering reusable building blocks that enable developers to write efficient and scalable parallel code without starting from scratch. Thrust provides a high-level interface for parallel algorithms, while CUB delivers highly optimized primitives for device-level operations, and libcudacxx ensures compatibility with modern C++ standards. By unifying these components, CCCL reduces duplication and improves developer productivity while maintaining performance across different GPU architectures. -
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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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FileTrees.jl
Parallel file processing made easy
...When computing lazy trees, these values are held in distributed memory and operated on in parallel. -
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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. -
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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. -
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MiroFish
A Simple and Universal Swarm Intelligence Engine
MiroFish is a next-generation artificial intelligence prediction engine that leverages multi-agent technology and swarm-intelligence simulation to model, simulate, and forecast complex real-world scenarios. The system extracts “seed” information from sources such as breaking news, policy documents, and market signals to construct a high-fidelity digital parallel world populated by thousands of virtual agents with independent memory and behavior rules. Users can inject variables or conditions into this simulated environment from a “god’s eye view,” enabling iterative prediction of future trends under different assumptions, which can be useful for decision support, scenario planning, or creative exploration. ... -
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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. -
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PolarDB for PostgreSQL
A cloud-native database based on PostgreSQL developed by Alibaba Cloud
PolarDB for PostgreSQL is Alibaba Cloud's cloud-native, distributed version of PostgreSQL designed for high availability, scalability, and performance. It enhances standard PostgreSQL with features like shared storage, compute-storage separation, and parallel processing. PolarDB supports cloud-native workloads, offering enterprise-grade capabilities while maintaining PostgreSQL compatibility. -
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LLMCompiler
An LLM Compiler for Parallel Function Calling
...LLMCompiler addresses this limitation by applying principles from classical compilers to analyze a task and construct an execution plan that allows multiple functions to run in parallel whenever possible. The framework builds a dependency graph of required operations, identifying which tasks must run sequentially and which can be executed simultaneously. Its architecture includes components such as a planning module that constructs the task graph, a task dispatcher that manages dependencies, and an executor that performs parallel calls. -
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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. -
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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. ... -
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Async PHP
Easily run code asynchronously
Spatie Async is a PHP library that allows developers to run parallel processes using asynchronous tasks. It helps optimize performance by executing long-running or resource-intensive tasks concurrently, instead of sequentially. The library is easy to use and integrates well with existing PHP applications, making it suitable for batch processing, data scraping, or any scenario where concurrency can boost efficiency. -
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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. -
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multi-agent-shogun
Samurai-inspired multi-agent system for Claude Code
multi-agent-shogun is a multi-agent orchestration system designed to coordinate multiple AI coding agents working in parallel. Inspired by the hierarchy of a feudal Japanese military structure, the system organizes agents into roles such as Shogun, Karo, and Ashigaru, which correspond to strategist, coordinator, and worker agents. A user interacts primarily with the Shogun agent by issuing natural language instructions that describe the desired tasks. -
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fd
A simple, fast and user-friendly alternative to 'find'
...Instead of just showing the search results, you often want to do something with them. fd provides two ways to execute external commands for each of your search results: the -x/--exec option runs an external command for each of the search results (in parallel), or the -X/--exec-batch option launches the external command once, with all search results as arguments. -
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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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pgsync
Postgres to Elasticsearch/OpenSearch sync
pgsync is a lightweight tool for syncing Postgres databases across environments, such as from production to staging. It allows selective table syncing, data masking, and parallel copying for fast and safe data migration. pgsync is ideal for developers who need realistic test data without exposing sensitive information. -
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Piscina
A fast, efficient Node.js Worker Thread Pool implementation
Piscina is a fast, efficient Node.js worker thread pool implementation, designed to perform heavy CPU-bound tasks in parallel, thereby improving application performance. -
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Bevy
A refreshingly simple data-driven game engine built in Rust
A refreshingly simple data-driven game engine built in Rust. All engine and game logic uses Bevy ECS, a custom Entity Component System. Massively Parallel and Cache-Friendly. The fastest ECS according to some benchmarks. Components are Rust structs, Systems are Rust functions. Queries, Global Resources, Local Resources, Change Detection, Lock-Free Parallel Scheduler. Bevy is still in the very early stages of development. APIs can and will change (now is the time to make suggestions!). ... -
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Bend
A massively parallel, high-level programming language
Bend is an interactive programming environment (REPL) built on top of the Kotlin language, designed to allow users to explore, experiment, and learn Kotlin in a live, feedback-driven manner. The tool lets you define variables, functions, or values at the prompt and iteratively refine them—immediately seeing output and types—while preserving state across commands. It emphasizes discoverability and experimentation: users can inspect functions, call them on sample inputs, and evolve logic... -
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HunyuanVideo-I2V
A Customizable Image-to-Video Model based on HunyuanVideo
...It extends video generation so that given a static reference image plus an optional prompt, it generates a video sequence that preserves the reference image’s identity (especially in the first frame) and allows stylized effects via LoRA adapters. The repository includes pretrained weights, inference and sampling scripts, training code for LoRA effects, and support for parallel inference via xDiT. Resolution, video length, stability mode, flow shift, seed, CPU offload etc. Parallel inference support using xDiT for multi-GPU speedups. LoRA training / fine-tuning support to add special effects or customize generation. -
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LightGBM
Gradient boosting framework based on decision tree algorithms
LightGBM or Light Gradient Boosting Machine is a high-performance, open source gradient boosting framework based on decision tree algorithms. Compared to other boosting frameworks, LightGBM offers several advantages in terms of speed, efficiency and accuracy. Parallel experiments have shown that LightGBM can attain linear speed-up through multiple machines for training in specific settings, all while consuming less memory. LightGBM supports parallel and GPU learning, and can handle large-scale data. It’s become widely-used for ranking, classification and many other machine learning tasks.
