Search Results for "computing"
Sort By:
Showing 1874 open source projects for "computing"
View related business solutions-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
NumPy
The fundamental package for scientific computing with Python
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. ... -
2
Retrobios
Complete BIOS and firmware packs for RetroArch, Batocera, Recalbox
Retrobios is a low-level systems programming project focused on recreating or emulating BIOS-like functionality for legacy or experimental computing environments. It is designed to provide a minimal firmware layer that initializes hardware and prepares systems to boot, often used for educational purposes or retrocomputing experiments. The project likely explores how early computing systems managed hardware abstraction, memory initialization, and device communication before modern operating systems take control. ... -
3
-
4
BOINC
Open-source software for volunteer computing and grid computing
BOINC (Berkeley Open Infrastructure for Network Computing) is an open-source platform that enables distributed computing using volunteered computer resources. It allows researchers to harness massive amounts of processing power from public participants for scientific projects such as climate research, disease modeling, and astrophysics. BOINC supports cross-platform deployment and is backed by a large, active community. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
5
Quantum++
Modern C++ quantum computing library
Quantum++ is a modern C++ general-purpose quantum computing library, composed solely of template header files. Quantum++ is written in standard C++17 and has very low external dependencies, using only the Eigen 3 linear algebra header-only template library and, if available, the OpenMP multiprocessing library. Quantum++ is not restricted to qubit systems or specific quantum information processing tasks, being capable of simulating arbitrary quantum processes. -
6
QPanda 2
QPanda 2 is an open source quantum computing framework
QPanda2 is an open source quantum computing framework developed by Origin Quantum, which can be used to build, run and optimize quantum algorithms. QPanda2 is the basic library of a series of software developed by Origin Quantum, which provides core components for QRunes, Qurator and quantum computing services. -
7
The Julia Programming Language
High-level, high-performance dynamic language for technical computing
Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose computing. ... -
8
CUDA-QX
Accelerated libraries for quantum-classical computing built on CUDA-Q
...By abstracting low-level details and providing ready-to-use components, it accelerates experimentation and development in quantum computing research. The project is part of NVIDIA’s broader effort to enable scalable quantum-classical computing systems through hardware-agnostic programming models. -
9
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. -
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.
-
10
Primes.jl
Prime numbers in Julia
Julia functions for computing prime numbers. -
11
ReservoirComputing.jl
Reservoir computing utilities for scientific machine learning (SciML)
ReservoirComputing.jl provides an efficient, modular and easy-to-use implementation of Reservoir Computing models such as Echo State Networks (ESNs). For information on using this package please refer to the stable documentation. Use the in-development documentation to take a look at not-yet-released features. -
12
Jupyter Notebook
Jupyter Interactive Notebook
The notebook extends the console-based approach to interactive computing in a qualitatively new direction, providing a web-based application suitable for capturing the whole computation process: developing, documenting, and executing code, as well as communicating the results. The Jupyter notebook combines two components. A web application, which is a browser-based tool for interactive authoring of documents which combine explanatory text, mathematics, computations and their rich media output. ... -
13
Zipkin
Distributed tracing system to gather timing data
Zipkin is a distributed tracing system. It helps gather timing data needed to troubleshoot latency problems in service architectures. Features include both the collection and lookup of this data. If you have a trace ID in a log file, you can jump directly to it. Otherwise, you can query based on attributes such as service, operation name, tags and duration. Some interesting data will be summarized for you, such as the percentage of time spent in a service, and whether or not operations... -
14
PCjs Machines
The original IBM PC and other machine emulations in JavaScript
...PCjs also provides curated software collections, including operating systems, applications, and games from the early days of personal computing. Its design emphasizes accessibility and education, enabling users to explore historical systems without requiring specialized hardware or installations. The project also includes tools for embedding emulated machines into web pages, making it useful for documentation and teaching purposes. -
15
CUDA-Q
C++ and Python support for the CUDA Quantum programming model
...It enables complex workflows where classical and quantum computations are tightly integrated, supporting advanced research and real-world applications in quantum computing. The repository includes components such as the nvq++ compiler and runtime systems that manage execution across heterogeneous environments. -
16
Qiskit
Qiskit is an open-source SDK for working with quantum computers
Qiskit [kiss-kit] is an open-source SDK for working with quantum computers at the level of pulses, circuits, and application modules. When you are looking to start Qiskit, you have two options. You can start Qiskit locally, which is much more secure and private, or you get started with Jupyter Notebooks hosted in IBM Quantum Lab. Qiskit includes a comprehensive set of quantum gates and a variety of pre-built circuits so users at all levels can use Qiskit for research and application... -
17
Puter
🌐 The Internet Computer! Free, Open-Source, and Self-Hostable
Puter is an open-source, self-hostable internet computer platform that combines cloud storage, applications, development tools, and computing resources into a single web-based environment. Designed as a complete digital workspace, it enables users to access files, apps, and services from anywhere through a browser-based desktop experience. Puter includes built-in productivity applications while also providing developers with cloud infrastructure such as AI services, databases, storage, and serverless computing capabilities. ... -
18
PolarDB-X
PolarDB-X is a cloud native distributed SQL Database
PolarDB-X is a cloud-native distributed SQL database designed to handle high concurrency, massive storage, and complex querying scenarios. It features a shared-nothing architecture that decouples computing from storage, providing scalability and flexibility for various applications. -
19
Sogou C++ Workflow
C++ parallel computing and asynchronous networking engine
As Sogou`s C++ server engine, Sogou C++ Workflow supports almost all back-end C++ online services of Sogou, including all search services, cloud input method, online advertisements, etc., handling more than 10 billion requests every day. This is an enterprise-level programming engine in light and elegant design which can satisfy most C++ back-end development requirements. -
20
Cirq
A python framework for creating, editing, and invoking NISQ
Cirq is a Python library for writing, manipulating, and optimizing quantum circuits and running them against quantum computers and simulators. -
21
Tequila
A High-Level Abstraction Framework for Quantum Algorithms
Tequila is an abstraction framework for (variational) quantum algorithms. It operates on abstract data structures allowing the formulation, combination, automatic differentiation and optimization of generalized objectives. Tequila can execute the underlying quantum expectation values on state-of-the-art simulators as well as on real quantum devices. -
22
Volcano
A Cloud Native Batch System (Project under CNCF)
Volcano is a batch system built on Kubernetes. It provides a suite of mechanisms that are commonly required by many classes of batch & elastic workload including machine learning/deep learning, bioinformatics/genomics, and other "big data" applications. These types of applications typically run on generalized domain frameworks like TensorFlow, Spark, Ray, PyTorch, MPI, etc, which Volcano integrates with. Volcano builds upon a decade and a half of experience running a wide variety of... -
23
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. -
24
FreqTables.jl
Frequency tables in Julia
This package allows computing one- or multi-way frequency tables (a.k.a. contingency or pivot tables) from any type of vector or array. It includes support for CategoricalArray and Tables.jl compliant objects, as well as for weighted counts. -
25
PowerJob
Enterprise job scheduling middleware with distributed computing
...Four execution modes are supported, including stand-alone, broadcast, Map and MapReduce. Distributed computing resources could be utilized in MapReduce mode, try the magic out here! Both job dependency management and data communications between jobs are supported. Developers can write their processors in Java, Shell, Python, and will subsequently support multilingual scheduling via HTTP.
