Search Results for "distributed shared memory"
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Showing 39 open source projects for "distributed shared memory"
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1
I hate money
A simple shared budget manager web application
I hate money is a web application made to ease shared budget management. It keeps track of who bought what, when, and for whom; and helps to settle the bills. I hate money is written in python, using the flask framework. It’s developed with ease of use in mind and is trying to keep things simple. Hope you (will) like it! The code is distributed under a BSD beerware derivative: if you meet the people in person and you want to pay them a craft beer, you are highly encouraged to do so. -
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JetCache
JetCache is a Java cache framework
...It provides more powerful annotations than those in Spring Cache. The annotations in JetCache support native TTL, two-level caching, and automatically refresh in distributed environments, also you can manipulate Cache instances by your code. Currently, there are four implementations: RedisCache, TairCache(not open source on github), CaffeineCache (in memory) and a simple LinkedHashMapCache (in memory). -
3
node-rate-limiter-flexible
Count and limit requests by key with atomic increments
rate-limiter-flexible counts and limits number of actions by key and protects from DDoS and brute force attacks at any scale. It works with Redis, process Memory, Cluster or PM2, Memcached, MongoDB, MySQL, PostgreSQL and allows to control of requests rate in single process or distributed environment. All operations in memory or distributed environments use atomic increments against race conditions. Combine limiters, block key for some duration, delay actions, manage failover with insurance options, configure smart key blocking in memory and many others. ... -
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Nebulex
In-memory and distributed caching toolkit for Elixir
Nebulex provides support for transparently adding caching into an existing Elixir application. Similar to Ecto, the caching abstraction allows consistent use of various caching solutions with minimal impact on the code. Nebulex cache abstraction shields developers from directly dealing with the underlying caching implementations, such as Redis, Memcached, or even other Elixir cache implementations like Cachex. Additionally, it provides totally out-of-box features such as cache usage... -
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5
Go Micro
A framework for distributed systems development
Go Micro is a framework for distributed systems development. Go Micro provides the core requirements for distributed systems development including RPC and Event driven communication. The Micro philosophy is sane defaults with a pluggable architecture. We provide defaults to get you started quickly but everything can be easily swapped out. Auth is built in as a first class citizen. -
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Holochain
The current, performant & industrial strength version of Holochain
Holochain is a post-blockchain framework for building agent-centric, distributed applications. Instead of using global consensus, Holochain enables each agent (user) to maintain their own local state while validating actions with a shared set of rules. This allows for scalable, secure, and resilient apps where data is owned and controlled by users. Ideal for social apps, cooperatives, and data sovereignty platforms, Holochain focuses on enabling collaboration without central servers or miners. ... -
7
CacheManager
CacheManager is an open source caching abstraction layer for .NET
...Two implementations are available, based on System.Runtime.Caching and based on the new Microsoft.Extensions.Caching.Memory package. Redis can be used as an extremely fast and reliable out-of-process (or distributed) solution. Another great distributed solution is memcached, which can be used as a layer. The Redis implementation is based on the popular StackExchange.Redis package. Couchbase is primarily a document database but can also be used for simple caching. ASP.NET comes with its own in-process memory cache which can be used as one layer. ... -
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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. According to our benchmark and third-party research papers, Sedona has 50% less peak memory consumption than other Spark-based geospatial data systems for large-scale in-memory query processing. ... -
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RIBs
Uber's cross-platform mobile architecture framework
...Plus, most RIB logic is decoupled from child RIB logic. This makes RIB classes easy to test and reason about independently. Tooling for developer productivity. RIBs come with IDE tooling around code generation, memory leak detection, static analysis and runtime integrations. -
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10
Smallpond
A lightweight data processing framework built on DuckDB and 3FS
smallpond is a lightweight distributed data processing framework built by DeepSeek, designed to scale DuckDB workloads over clusters using their 3FS (Fire-Flyer File System) backend. The idea is to preserve DuckDB’s fast analytics engine but lift it from single-node to multi-node settings, giving you the ability to operate on large datasets (e.g. petabyte scale) without moving to a heavyweight system like Spark. Users write Python-like code (via DataFrame APIs or SQL strings) to express... -
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Netty-socketio
Socket.IO server implemented on Java
...CentOS, 1 CPU, 4GB RAM runned on VM, CPU 10%, Memory 15%, 6000 xhr-long polling sessions or 15000 websockets sessions, 4000 messages per second. -
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Micronaut
Micronaut Application Framework
Micronaut is a modern, JVM-based, full-stack Java framework designed for building modular, easily testable JVM applications with support for Java, Kotlin, and the Groovy language. Micronaut is developed by the creators of the Grails framework and takes inspiration from lessons learned over the years building real-world applications from monoliths to microservices using Spring, Spring Boot, and Grails. Micronaut aims to provide all the tools necessary to build JVM applications. With Micronaut... -
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Apache Flink
Stream processing framework with powerful stream
Apache Flink is a distributed engine for stateful computations over data streams and batches, designed for low-latency processing at scale. Its core runtime executes dataflow graphs with fine-grained backpressure and checkpointing, allowing applications to recover consistently from failures. Flink’s event-time model and watermarks enable accurate out-of-order processing, windowing, and complex time semantics that typical real-time systems struggle with. Developers program against high-level... -
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AWS SigV4 Library
AWS library to sign AWS HTTP requests with Signature Version 4
...This library can optionally be used by applications sending direct HTTP requests to AWS services requiring SigV4 authentication. This library has no dependencies on any additional libraries other than the standard C 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. -
15
.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... -
16
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. ... -
18
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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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. ... -
20
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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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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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. ... -
23
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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Rocket Chip
Rocket Chip Generator
...A diplomacy framework (LazyModules) lets designers wire components with negotiated parameters, enabling reuse and rapid exploration of different cache sizes, port counts, and memory hierarchies. The generator supports custom accelerators through the RoCC interface, allowing domain-specific compute units to be plugged into the pipeline with shared cache and memory semantics. Tooling integrates with FIRRTL, Verilator, and commercial EDA flows, and the ecosystem around Rocket Chip (e.g., Chipyard) adds harnesses, peripherals, and verification infrastructure. -
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Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable.
