Search Results for "computing"
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Showing 120 open source projects for "computing"
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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. -
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Apache Sedona
Cluster computing framework for processing large-scale geospatial data
Apache Sedona™ is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark and Apache Flink, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines. According to our benchmark and third-party research papers, Sedona runs 2X - 10X faster than other Spark-based geospatial data systems on computation-intensive query workloads. ... -
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NVIDIA Warp
A Python framework for accelerated simulation, data generation
NVIDIA Warp is a high-performance Python framework developed by NVIDIA for building and accelerating simulation, graphics, and physics-based workloads using GPU computing. It enables developers to write kernel-level code in Python that is automatically compiled into efficient CUDA kernels, combining ease of use with near-native performance. The framework is designed for applications such as robotics, reinforcement learning, physical simulation, and differentiable computing, where performance and flexibility are critical. ... -
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ModelingToolkit.jl
Modeling framework for automatically parallelized scientific ML
ModelingToolkit.jl is a modeling language for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. It then mixes ideas from symbolic computational algebra systems with causal and acausal equation-based modeling frameworks to give an extendable and parallel modeling system. It allows for users to give a high-level description of a model for symbolic preprocessing to analyze and enhance the model. -
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Fury
A blazing fast multi-language serialization framework
...In serialization scenarios, Fury can achieve a 100x speed up compared to traditional serialization frameworks. If you use Java clusters on a large scale, this will save a lot of computing resources. -
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Micronaut
Micronaut Application Framework
...With Micronaut you can build message-driven applications, command-line applications, HTTP servers, and more whilst for Microservices in particular Micronaut also provides distributed configuration. This is achieved by pre-computing the framework infrastructure at compilation time which reduces the logic required at runtime for the application to work. Compatible with Java, Groovy, and Kotlin, with Scala on the roadmap. -
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RuleGo
Component orchestration rule engine framework for Go
...It’s lightweight, embeddable, orchestration-ready, and built for flexible composition of business logic into reusable components. No external middleware dependencies, efficient data processing and linkage on low-cost devices, suitable for IoT edge computing. Embedded and Standalone Deployment modes. Supports embedding RuleGo into existing applications. It can also be deployed independently as middleware, providing rule engine and orchestration services. -
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TensorZero
TensorZero is an open-source stack for industrial-grade LLM apps
tensorzero is a lightweight C++ library designed for tensor operations and numerical computing. It offers a minimal and readable implementation of core tensor functionality, making it ideal for educational purposes, lightweight applications, or those wanting to understand how tensor libraries work under the hood. With no external dependencies, tensorzero is easy to integrate into C++ projects needing basic multi-dimensional array support. -
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Flower
Flower: A Friendly Federated Learning Framework
...Flower can be used with any machine learning framework, for example, PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, Pandas for federated analytics, or even raw NumPy for users who enjoy computing gradients by hand. -
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Bacalhau
Community-driven, simple, yet powerful framework
...It allows developers to run containerized workloads close to where the data lives, reducing latency, cost, and privacy risks. Bacalhau supports various runtime environments and is designed to make decentralized data processing as accessible as traditional cloud computing. It’s especially useful for large-scale AI/ML jobs, scientific research, and content indexing in Web3 ecosystems. -
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Catlab.jl
A framework for applied category theory in the Julia language
Catlab.jl is a framework for applied and computational category theory, written in the Julia language. Catlab provides a programming library and interactive interface for applications of category theory to scientific and engineering fields. It emphasizes monoidal categories due to their wide applicability but can support any categorical structure that is formalizable as a generalized algebraic theory. First and foremost, Catlab provides data structures, algorithms, and serialization for... -
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tsuru
Open source and extensible Platform as a Service (PaaS)
...Let your developers code and understand the business instead of solving infrastructure problems or handling large configuration files. We built the platform as a service atop of battle-tested technologies from Cloud Native Computing Foundation stack. Manage your distributed apps in many kubernetes clusters/regions with a single point of control. Tsuru is an open source project and, as such, we welcome contributions. Feel free to report or fix bugs, contribute to the documentation, or just give your opinion about the software. -
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gRPC-Go
The Go language implementation of gRPC. HTTP/2 based RPC
...It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking and authentication. It is also applicable in last mile of distributed computing to connect devices, mobile applications and browsers to backend services. Define your service using Protocol Buffers, a powerful binary serialization toolset and language. Install runtime and dev environments with a single line and also scale to millions of RPCs per second with the framework. Automatically generate idiomatic client and server stubs for your service in a variety of languages and platforms. ... -
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KubeEdge
Kubernetes Native Edge Computing Framework (project under CNCF)
KubeEdge is built upon Kubernetes and extends native containerized application orchestration and device management to hosts at the Edge. It consists of a cloud part and an edge part, and provides core infrastructure support for networking, application deployment, and metadata synchronization between the cloud and edge. It also supports MQTT which enables edge devices to access through edge nodes. With KubeEdge it is easy to get and deploy existing complicated machine learning, image... -
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FATE
An industrial grade federated learning framework
...Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. FATE became open-source in February 2019. FATE TSC was established to lead FATE open-source community, with members from major domestic cloud computing and financial service enterprises. FedAI is a community that helps businesses and organizations build AI models effectively and collaboratively, by using data in accordance with user privacy protection, data security, data confidentiality and government regulations. -
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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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The MITK Workbench is a free, open-source application for medical image visualization, segmentation, registration, and much more. Beyond the Workbench application, MITK is a comprehensive C++ framework for medical image computing. It provides a modular foundation for extending the MITK Workbench with custom plugins or developing your own medical imaging applications and research prototypes.
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Empact Foundation Class Library
Cross-platform C++ library for use as a default application framework.
...Features include: * Threading & synchronization * Socket programming: SSL, NanoMsg & ZMQ * File I/O utilities: zlib, ini, yaml * Native Database access: MySQL, SQLite, BerkleyDB, Postgre, REDIS and ODBC * Built-in mini XML parser; optional EXPAT, LIBXML and MSXML support * Network protocol stack: HTTP, FTP, SMTP, POP3, SOAP, XMLRPC * Scripting languages: Perl, Python, JavaScript, VBScript, Java, Lua, TCL, Squirrel * Cloud Computing: AWS * Encryption: OpenSSL * Platforms: Linux/Posix, Windows, Arduino * Over 500+ highly reusable classes. 4000+ fully documented functions. Follow the 'Wiki' link above to explore everything about the framework. -
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TorchQuantum
A PyTorch-based framework for Quantum Classical Simulation
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers. Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, and quantum neural networks. Dynamic computation graph, automatic gradient computation, fast GPU support, batch model terrorized processing. -
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Erda
An enterprise-grade Cloud-Native application platform for Kubernetes
Erda is an open-source platform created by Terminus to ensure the development of microservice applications. It provides DevOps, microservice governance, and multi-cloud management capabilities. The multi-cloud architecture based on Kubernetes and application-centric DevOps and microservice governance can make the development, operation, monitoring, and problem diagnosis of complex business applications simpler and more efficient. erda is the core repository that implements all the RESTful... -
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CloudTest-Cloud java unit test framework
A redefined framework with new approach and methodology for unit test
CloudTest is a redefined unit testing approach and methodology, which can make your testing jobs become much more easy and efficient. It is a pure java lightweight framework integrated test cases management, test data management, assert management, automation regression, performance monitor and test report in one. -
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Clay Golem
Golem is creating a global market for computing power
The Golem Network fosters a global group of creators building ambitious software solutions that will shape the technological landscape of future generations by accessing computing resources across the platform. Golem Network is an accessible, reliable, open access and censorship-resistant protocol, democratizing access to digital resources and connecting users through a flexible, open-source platform. With Golem Network, users can connect with ease and pay each other for sharing their unused resources. Golem’s democratized access combined with a unique peer-to-peer exchange creates an unstoppable ecosystem for a myriad of use-cases to be born, allowing software developers to leverage their creativity more than ever before. ... -
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X-DeepLearning
An industrial deep learning framework for high-dimension sparse data
...Storage and communication optimization, parameters are automatically allocated globally without manual intervention, and requests are merged to completely eliminate computing/storage/communication hotspots of ps. Complete streaming training features including feature admission, feature elimination, model incremental export, feature counting statistics, etc. Background: XDL1.0 focuses on throughput optimization and adopts the one request per thread processing model, which can significantly improve the limit throughput under ultra-high concurrency. -
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Wally
Distributed Stream Processing
...By eliminating infrastructure complexity, going from prototype to production has never been simpler. When we set out to build Wally, we had several high-level goals in mind. Create a dependable and resilient distributed computing framework. Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. Provide high-performance & low-latency data processing. Be portable and deploy easily (i.e., run on-prem or any cloud). Manage in-memory state for the application. Allow applications to scale as needed, even when they are live and up-and-running. ... -
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JPPF
The open source grid computing solution
JPPF makes it easy to parallelize computationally intensive tasks and execute them on a Grid.
