Search Results for "distributed computing" - Page 5
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Showing 855 open source projects for "distributed computing"
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Blazegraph (powered by bigdata)
Fast, scalable, robust graph database platform
Blazegraph has moved to Github. Please see https://github.com/blazegraph/database/. -
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Nebula docs
Documentation repo of nebula orchestration system
Nebula is a open source distributed Docker orchestrator designed for massive scales (tens of thousands of servers/worker devices), unlike Mesos/Swarm/Kubernetes it has the ability to have workers distributed on high latency connections (such as the internet) yet have the pods(containers) be managed centrally with changes taking affect (almost) immediately, this makes Nebula ideal for managing a vast cluster of servers\devices across the globe, some example use cases are appliances\virtual appliances located at clients data centers, edge computing, and POS systems. ... -
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Nebula reporter
The optional reporter container which reads nebula reports from Kafka
Nebula is an open source-distributed Docker orchestrator designed for massive scales (tens of thousands of servers/worker devices), unlike Mesos/Swarm/Kubernetes it has the ability to have workers distributed on high latency connections (such as the internet) yet have the pods(containers) be managed centrally with changes taking effect (almost) immediately, this makes Nebula ideal for managing a vast cluster of servers\devices across the globe. Ever wandered how your going to push an update... -
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Nebula worker
The worker node manager container which manages nebula nodes
Nebula is a open source distributed Docker orchestrator designed for massive scales (tens of thousands of servers/worker devices), unlike Mesos/Swarm/Kubernetes it has the ability to have workers distributed on high latency connections (such as the internet) yet have the pods(containers) be managed centrally with changes taking affect (almost) immediately, this makes Nebula ideal for managing a vast cluster of servers\devices across the globe, some example use cases are IoT devices, appliances\virtual appliances located at clients data centers, and edge computing. -
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dupost
Dual Porosity Stokes model implemented with FEniCS
...Douglas, X. Hu, B. Bai, X. He, M. Wei and J. Hou, "A Data Assimilation Enabled Model for Coupling Dual Porosity Flow with Free Flow," 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), Wuxi, 2018, pp. 304-307. doi: 10.1109/DCABES.2018.00085 2. Hu X., Douglas C.C. (2019) An Implementation of a Coupled Dual-Porosity-Stokes Model with FEniCS. In: Rodrigues J. et al. (eds) Computational Science – ICCS 2019. ICCS 2019. Lecture Notes in Computer Science, vol 11539. ... -
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spark-ml-source-analysis
Spark ml algorithm principle analysis and specific source code
...Each section discusses both the mathematical principles behind the algorithms and how Spark implements them in a distributed computing environment. By studying these implementations, readers gain insight into how large-scale machine learning pipelines operate across distributed data systems. -
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Prometheus Spec
Censorship-resistant trustless state channels for machine learning
Censorship resistant decentralised computing is a new topic in distributed systems theory. With the recent rise in popularity of Bitcoin as a censorship-resistant financial and transactional system a new technological field of blockchain and its applications appeared. This work addresses the problem of computational integrity proofs (correctness of the actual computations) made by some non-trusted (anonymous) third party without repeating the whole volume of the actual computing. ... -
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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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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
...Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and distributed computing topics, helping you bridge from basics to production-adjacent workflows. The collection is suitable for self-paced study, quick reference, or as teaching materials in workshops. By combining narrative explanations with executable code, it shortens the path from theory to working prototypes. -
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Jadex is a Belief Desire Intention (BDI) reasoning engine that allows for programming intelligent software agents in XML and Java. The resoning engine is very flexible.
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ATTENTION: For more info and latest version, refer to https://github.com/estigma88/jdhtuq. jDHTUQ is a peer-to-peer DHT system based in Chord algorithm, but built like API to generalize the implementation of peer-to-peer DHT systems. jDHTUQ have two fundamental services , put and get of resource.
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SAFMQ: Store and Forward Message Queue, message oriented middleware. Uses include Assured Async Messaging, SOA, Delayed/Batch, and Cluster/Grid Computing. The SAFMQ server provides cross platform communication among C++, PHP, Java, and .NET clients.
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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. -
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Madara
Middleware for distributed applications
The purpose of the project is to develop a portable programming framework that facilitates distributed and multi-threaded programming for C++, Java, and Python. MADARA was originally developed as an agent-based middleware specifically for real-time, distributed artificial intelligence, but is now more general purpose for distributed timing, control, knowledge and reasoning, and quality-of-service. MADARA is composed of several tools and middleware, and the main entry point into the system... -
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Slimgrid is a Java library for grid computations which is lighter than other ones (JPPF, Hadoop, ...). The main design goals are: minimalism, simplicity, pervasiveness. If you need to grab something which does not require you to comprehend massive and complex API's, do exhaustive configurations and installations, is robust and reliable, uses just one port for all management and communication, then SlimGrid is the right choice. The SlimGrid is built on top of the Apache's ZooKeeper library...
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A framework to run MATLAB programs as batch jobs. Features a structured input description, integrity constraints and GUI.Independent parts of a job can execute in parallel on a cluster computer. Developed at Freiburg Brain Imaging (FBI) - http://fbi.uniklinik-freiburg.de/
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ChunJun
A data integration framework
ChunJun is a distributed integration framework, and currently is based on Apache Flink. It was initially known as FlinkX and renamed ChunJun on February 22, 2022. It can realize data synchronization and calculation between various heterogeneous data sources. ChunJun has been deployed and running stably in thousands of companies so far. Based on the real-time computing engine--Flink, and supports JSON template and SQL script configuration tasks. -
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Prolog+CG is a Java implementation of Prolog with extensions implementing a subset of the Conceptual Graph (CG) theory of John Sowa. CGs are first-class datatypes on a par with terms. Object oriented extensions are also included.
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PREESM provides a complete framework for fast prototyping. It generates a simulation and an optimized multiprocessor code from graph descriptions of algorithm and architecture. Please visit the project homepage at http://preesm.sourceforge.net.
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DMTK
Microsoft Distributed Machine Learning Toolkit
The Microsoft Distributed Machine Learning Toolkit (DMTK) is an open-source framework created to support scalable machine learning across distributed computing environments. Developed by Microsoft Research, the toolkit provides infrastructure and algorithms designed to train large models efficiently on clusters of machines rather than a single system. -
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asyncoro
Python framework for asynchronous, concurrent, distributed programming
asyncoro is a Python framework for developing concurrent, distributed, network programs with asynchronous completions and coroutines. Asynchronous completions implemented in asyncoro are sockets (non-blocking sockets), database cursors, sleep timers and locking primitives. Programs developed with asyncoro have same logic and structure as Python programs with threads, except for a few syntactic changes. asyncoro supports socket I/O notification mechanisms epoll, kqueue, /dev/poll (and poll... -
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The Node Director is a tool for managing distributed, hetergeneous UNIX Systems. Its functionality includes System Configuration, Application Distribution, NIS & NIS+ Management, User Creation and Dynamic System Documentation.
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JavaCá&Lá
A java middleware for HPC and IoT
The Java Cá&Lá (or just JCL - www.javacaela.org) is a middleware that integrates high performance computing (HPC) and Internet of things (IoT) in a unique API. It enables asynchronous remote method invocation and distributed shared memory services over multicore computer architectures or over multicomputer clusters. Sensing services are also feasible in JCL IoT version. An API for internal control enables collecting several times and memory usage of tasks submitted to the JCL cluster. ... -
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SmartFrog (Smart Framework for Object Groups) is a framework for configuring and automatically activating distributed applications. The SmartFrog framework is released under LGPL license. More info at: www.smartfrog.org
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H2O-3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning
H2O-3 is an open-source machine learning platform designed to build scalable and distributed machine learning models across large datasets. The system operates as an in-memory computing platform that allows data scientists to train models quickly using distributed resources. It supports many machine learning algorithms including generalized linear models, gradient boosting machines, deep learning networks, and ensemble techniques.
