Search Results for "distributed shared memory" - Page 5
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Showing 326 open source projects for "distributed shared memory"
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LingBot-World
Advancing Open-source World Models
LingBot-World is an open-source, high-fidelity world simulator designed to advance the state of world models through video generation. Built on top of Wan2.2, it enables realistic, dynamic environment simulation across diverse styles, including real-world, scientific, and stylized domains. LingBot-World supports long-term temporal consistency, maintaining coherent scenes and interactions over minute-level horizons. With real-time interactivity and sub-second latency at 16 FPS, it is... -
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DynaMix
A new take on polymorphism
DynaMix (Dynamic Mixins) is an alternative take on object-oriented programming and dynamic polymorphism. It lets users compose and modify polymorphic objects at run time. The main target language is C++, but C is also supported. The library is a means to create a project's architecture rather than achieve its purpose. It helps with extensibility, readability, scalability, and interoperability. It focuses on maximal performance and minimal memory overhead. The library uses the type... -
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Consul Template
Template rendering, notifier, and supervisor for @hashicorp Consul
This project provides a convenient way to populate values from Consul into the file system using the consul-template daemon. The daemon consul-template queries a Consul or Vault cluster and updates any number of specified templates on the file system. As an added bonus, it can optionally run arbitrary commands when the update process completes. Please see the examples folder for some scenarios where this functionality might prove useful. While there are multiple ways to run Consul Template,... -
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tigerbeetle
The financial transactions database designed for mission critical safe
TigerBeetle is production-ready on Linux and seamlessly integrated with major programming languages. TigerBeetle is a financial transactions database designed for mission-critical safety and performance to power the next 30 years of OLTP. TigerBeetle redesigns the distributed database storage engine and consensus protocol for the OLTP workload. This solves the problem of OLTP write contention to unlock three orders of magnitude more performance than a general purpose (OLGP) database. The... -
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Omnilingual ASR
Omnilingual ASR Open-Source Multilingual SpeechRecognition
Omnilingual-ASR is a research codebase exploring automatic speech recognition that generalizes across a very large number of languages using shared modeling and training recipes. It focuses on leveraging self-supervised audio pretraining and scalable fine-tuning so low-resource languages can benefit from high-resource data. The project provides data preparation pipelines, training scripts, decoding utilities, and evaluation tools so researchers can reproduce results and extend to new... -
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NeuralForecast
Scalable and user friendly neural forecasting algorithms.
NeuralForecast offers a large collection of neural forecasting models focusing on their performance, usability, and robustness. The models range from classic networks like RNNs to the latest transformers: MLP, LSTM, GRU, RNN, TCN, TimesNet, BiTCN, DeepAR, NBEATS, NBEATSx, NHITS, TiDE, DeepNPTS, TSMixer, TSMixerx, MLPMultivariate, DLinear, NLinear, TFT, Informer, AutoFormer, FedFormer, PatchTST, iTransformer, StemGNN, and TimeLLM. There is a shared belief in Neural forecasting methods'... -
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.NET for Apache Spark
A free, open-source, and cross-platform big data analytics framework
.NET for Apache Spark provides high-performance APIs for using Apache Spark from C# and F#. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write... -
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Apache Spark
A unified analytics engine for large-scale data processing
Apache Spark is a unified engine for large-scale data processing, offering APIs for batch jobs, streaming, machine learning, and graph computation. It builds on resilient distributed datasets (RDDs) and the newer DataFrame/Dataset abstractions to provide fault-tolerant, in-memory computation across clusters. Spark’s execution engine handles scheduling, shuffles, caching, and data locality so users can focus on transformations rather than infrastructure plumbing. With Spark Streaming (microbatches) and Structured Streaming, it delivers low-latency event processing suitable for real-time analytics. ... -
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Armbian Linux Build Framework
Armbian Linux Build Framework
...BASH shell and lightweight XFCE-based desktop. Standard boot, config, and update methods with minimal user-space footprint. Special config utilities are optional. A distributed image is compressed to its real data size which starts below 1G. Login is possible via serial, HDMI/VGA or SSH. Boot loader and kernel optimizations, memory caching, ZRAM swap, and video acceleration where applicable. Images are made fully automatized from sources. Releases are PGP signed and code is regularly inspected by the community. ... -
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TensorRT LLM
TensorRT LLM provides users with an easy-to-use Python API
...It provides a Python-based API built on top of PyTorch that allows developers to define, customize, and deploy LLMs efficiently across a variety of hardware configurations, from single GPUs to large multi-node clusters. The library focuses on maximizing throughput and minimizing latency through advanced techniques such as quantization, custom attention kernels, and optimized memory management strategies. It includes support for cutting-edge inference methods like speculative decoding and inflight batching, enabling real-time and large-scale AI applications. TensorRT-LLM integrates seamlessly with NVIDIA’s broader inference ecosystem, including Triton Inference Server and distributed deployment frameworks, making it suitable for production environments. -
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Swarms
Enterprise multi-agent orchestration framework for scalable AI apps
...Swarms also includes mechanisms for agent lifecycle management, memory handling, and dynamic composition, making it adaptable to evolving workloads. Additionally, it focuses on developer productivity through APIs, CLI tools, and templates that simplify building and deploying agent-based applications. -
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Ludwig AI
Low-code framework for building custom LLMs, neural networks
...Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. Experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. ... -
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dqlite
Embeddable, replicated and fault tolerant SQL engine
Dqlite is a fast, embedded, persistent SQL database with Raft consensus that is perfect for fault-tolerant IoT and Edge devices. Dqlite (distributed SQLite) extends SQLite across a cluster of machines, with automatic failover and high-availability to keep your application running. It uses C-Raft, an optimised Raft implementation in C, to gain high-performance transactional consensus and fault tolerance while preserving SQlite’s outstanding efficiency and tiny footprint. Stable Golang client... -
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Light-4J
A fast, lightweight and more productive microservices framework
Light means lightweight, lightning-fast and shedding light on how to program with modern Java SE for cloud-native deployment. I had been working on the Java EE platforms since early 2000 and suffered performance and productivity issues. In 2014, I realized that the IT industry was moving from Monolithic to Microservices and from on-premise data centers to the public clouds. To reduce the production cost for my applications, I need to find a lightweight platform that has a small memory... -
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Resque
Ruby library for creating background jobs and processing them
...Resque is heavily inspired by DelayedJob (which rocks) and comprises three parts, a Ruby library for creating, querying, and processing jobs, a Rake task for starting a worker which processes jobs, a Sinatra app for monitoring queues, jobs, and workers. Resque workers can be distributed between multiple machines, support priorities, are resilient to memory bloat / "leaks," are optimized for REE (but work on MRI and JRuby), tell you what they're doing, and expect failure. -
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Tiny CUDA Neural Networks
Lightning fast C++/CUDA neural network framework
...We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared memory in its default configuration. It will likely only work on an RTX 3090, an RTX 2080 Ti, or high-end enterprise GPUs. Lower-end cards must reduce the n_neurons parameter or use the CutlassMLP (better compatibility but slower) instead. tiny-cuda-nn comes with a PyTorch extension that allows using the fast MLPs and input encodings from within a Python context. ... -
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Chitu
High-performance inference framework for large language models
...It supports heterogeneous computing environments, including CPUs, GPUs, and various specialized AI accelerators, allowing models to run across a wide range of infrastructure configurations. Chitu is designed to scale from small single-machine deployments to large distributed clusters that handle high volumes of concurrent inference requests. The system also includes performance optimizations for large models, including support for quantized formats and efficient computation operators that reduce memory usage and latency. Its architecture aims to support enterprise adoption by ensuring stable long-term operation under production workloads. -
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Laravel Haystack
Supercharged job chains for Laravel
Laravel Haystack provides supercharged job chains for Laravel. It comes with powerful features like delaying jobs for as long as you like, applying middleware to every job, sharing data and models between jobs and even chunking jobs. Laravel Haystack supports every queue connection/worker out of the box. (Database, Redis/Horizon, SQS). It's great if you need to queue thousands of jobs in a chain or if you are looking for features that the original Bus chain doesn't provide. If you need to... -
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EasyZSwoole
swoole, easyswoole, swoole framework
EasySwoole is a distributed swoole framework with a permanent memory. It is born specifically for API and supports the simultaneous monitoring of HTTP, WebSocket, self-defined TCP, UDP protocol, and has rich components, such as collaboration Connect Pool, TP style co-process ORM, co-process microcredit SDK, co-process Kafka client, co-process ElasticSearch client, co-process Consul client, co-process Redis client, co-process Apollo client, co-process NSQ client, co-process self-definition queue、 Many components such as the Memcached client, the co-process view engine, JWT, the co-process RPC, the co-process SMTP client, the co-process HTTP client, the co-process Actor, and the Crontab timer. ... -
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AWS IoT Fleet Provisioning Library
Client library for using AWS IoT Fleet Provisioning service
...This library has no dependencies on any additional libraries other than the standard C library, and therefore, can be used with any MQTT library. This library is distributed under the MIT Open Source License. This library has gone through code quality checks including verification that no function has a GNU Complexity score over 8, and checks against deviations from mandatory rules in the MISRA coding standard. Deviations from the MISRA C:2012 guidelines are documented under MISRA Deviations. This library has also undergone static code analysis using Coverity static analysis, and validation of memory safety through the CBMC automated reasoning tool. -
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DeepSpeed
Deep learning optimization library: makes distributed training easy
DeepSpeed is an easy-to-use deep learning optimization software suite that enables unprecedented scale and speed for Deep Learning Training and Inference. With DeepSpeed you can: 1. Train/Inference dense or sparse models with billions or trillions of parameters 2. Achieve excellent system throughput and efficiently scale to thousands of GPUs 3. Train/Inference on resource constrained GPU systems 4. Achieve unprecedented low latency and high throughput for inference 5. Achieve extreme... -
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AWS IoT Device Defender Library
Client library for using AWS IoT Defender service on embedded devices
...This library also supports custom metrics, a feature that helps you monitor operational health metrics that are unique to your fleet or use case. For example, you can define a new metric to monitor the memory usage or CPU usage on your devices. This library has no dependencies on any additional libraries other than the standard C library, and therefore, can be used with any MQTT client library. This library is distributed under the MIT Open Source License. This library has gone through code quality checks including verification that no function has a GNU Complexity score over 8, and checks against deviations from mandatory rules in the MISRA coding standard. ... -
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FFTW++ is a C++ header class for the FFTW Fast Fourier Transform library that automates memory allocation, alignment, planning, wisdom, and communication on both serial and parallel (OpenMP/MPI) architectures. In 2D and 3D, hybrid dealiasing of convolutions substantially reduces memory usage and computation time. Wrappers for C, Python, and Fortran are included.
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OneFlow
OneFlow is a deep learning framework designed to be user-friendly
...An extension for OneFlow to target third-party compiler, such as XLA, TensorRT and OpenVINO etc.CUDA runtime is statically linked into OneFlow. OneFlow will work on a minimum supported driver, and any driver beyond. For more information. Distributed performance (efficiency) is the core technical difficulty of the deep learning framework. OneFlow focuses on performance improvement and heterogeneous distributed expansion. It adheres to the core concept and architecture of static compilation and streaming parallelism and solves the memory wall challenge at the cluster level. world-leading level. ... -
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Curve
Curve is a sandbox project hosted by the CNCF Foundation
A cloud-native distributed storage system. A sandbox project hosted by the CNCF Foundation. Curve is a modern storage system developed by netease, currently supporting file storage(CurveFS) and block storage(CurveBS). Now it's hosted at CNCF as a sandbox project. The performance, mixed, capacity cloud disk or persistent volume of virtual machine/container, and remote disks of physical machines. High-performance separation of storage and computation architecture: high-performance and low...
