Search Results for "distributed shared memory"
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Showing 295 open source projects for "distributed shared memory"
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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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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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Olric
Distributed, in-memory key/value store and cache
A lightweight, distributed in-memory data store designed for key-value caching and ephemeral storage. -
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Cipher
Cipher is an opensource memory layer designed for coding agents
Cipher is an open‑source memory layer designed for AI coding agents. It records both high‑level conceptual memory (design decisions, business logic) and agent reasoning steps so AI assistants can maintain context seamlessly across IDEs or sessions. Built to integrate with MCP-compatible environments (e.g. Cursor, Windsurf, Gemini CLI, Claude Desktop, AWS Kiro), it enables team-aware workspace memory sharing and real-time collaboration. -
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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. -
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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PolarDB for PostgreSQL
A cloud-native database based on PostgreSQL developed by Alibaba Cloud
PolarDB for PostgreSQL is Alibaba Cloud's cloud-native, distributed version of PostgreSQL designed for high availability, scalability, and performance. It enhances standard PostgreSQL with features like shared storage, compute-storage separation, and parallel processing. PolarDB supports cloud-native workloads, offering enterprise-grade capabilities while maintaining PostgreSQL compatibility. -
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Infinispan
Infinispan is an open source data grid platform
Infinispan is a distributed in-memory data grid and caching system designed for high-performance computing. It allows applications to scale dynamically by distributing data across multiple nodes, reducing latency and improving resilience. -
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OpenWork
An open-source alternative to Claude Cowork, powered by opencode
OpenWork is a framework for building decentralized collaborative work environments powered by AI and human contributions. At its core, the project enables contributors to define tasks, workflows, and goals that can be split, shared, and recombined across distributed nodes while agents and humans cooperate to advance progress. It offers structured templates for work items, decision logic for task allocation, and consensus mechanisms that let groups verify and validate results toward shared objectives. This project also includes moderation and reputation layers so that contributor trust and quality can be assessed and integrated into future task assignments. ... -
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Jaeger
Monitor and troubleshoot transactions in complex distributed systems
...It is used for monitoring and troubleshooting microservices-based distributed systems. OpenTracing compatible data model and instrumentation libraries include Go, Java, Node, Python, C++ and C#. Jaeger uses consistent upfront sampling with individual per service/endpoint probabilities and it has multiple storage backends: Cassandra, Elasticsearch, memory. -
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Redisson
Valkey & Redis Java client. Real-Time Data Platform
Redisson is a Java client library for Redis that offers distributed data structures, services, and frameworks to build scalable and reliable applications. It simplifies Redis usage by providing in-memory Java objects like maps, sets, locks, queues, and semaphores that are backed by Redis. Redisson supports advanced features like distributed locking, asynchronous APIs, and integrates with frameworks like Spring and Quarkus for reactive and cloud-native development. -
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Gocache
Go cache library that brings you multiple ways to manage caches
Guess what is Gocache? a Go cache library. This is an extendable cache library that brings you a lot of features for caching data. Multiple cache stores: actually in memory, redis, or your own custom store. A chain cache: use multiple cache with a priority order (memory then fallback to a redis shared cache for instance) A loadable cache: allow you to call a callback function to put your data back in cache. A metric cache to let you store metrics about your caches usage (hits, miss, set success, set error, ...) ... -
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PartitionedArrays.jl
Vectors and sparse matrices partitioned into pieces
This package provides distributed (a.k.a. partitioned) vectors and sparse matrices in Julia. See the documentation for further details. -
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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). -
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etcd
Distributed reliable key-value store for the most critical data
...Mainly used as a separate coordination service, in distributed systems. And designed to hold small amounts of data that can fit entirely in memory. Read and write values using standard HTTP tools, such as curl. Store data in hierarchically organized directories, as in a standard filesystem. Watch specific keys or directories for changes and react to changes in values. -
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Dagger.jl
A framework for out-of-core and parallel execution
Dagger.jl is a framework for out-of-core and parallel computing in Julia that allows users to construct and execute dynamic task graphs. It is designed for large-scale, distributed, and memory-efficient computations. Dagger supports lazy evaluation and scheduling across multiple threads or machines, enabling high-performance workflows for data processing, scientific computing, and machine learning. -
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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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Memcached
memcached development tree
Memcached is a high-performance, open-source distributed memory caching system, typically used to accelerate dynamic, database-driven websites by storing frequently accessed data in RAM. To build memcached on your machine from the local repo, you will have to install autotools, automake, and libevent. If you want TLS support, install OpenSSL's development packages and change the configure line. libseccomp (optional, experimental, Linux), enables process restrictions for better security. ... -
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Perfetto
Production-grade client-side tracing, profiling, and analysis
Perfetto is a production-grade tracing platform for Android, Linux, and Chrome that captures extremely detailed information about what a system is doing over time. It’s designed around a low-overhead producer/consumer model: instrumented components (“producers”) write binary events into shared memory buffers and a collector (“service”) reliably streams them to storage. The data model spans kernel and userspace, so you can stitch together CPU scheduling, app lifecycles, binder/IPC hops, GPU work, power and thermal signals, file I/O, heap samples, and more into a single coherent timeline. Perfetto’s ecosystem includes a web-based UI that can load multi-GB traces directly in the browser and an offline “trace processor” that exposes the trace as a queryable SQL-like table schema for deep analysis and automation. ... -
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rustix
Safe Rust bindings to POSIX-ish APIs
rustix provides efficient memory-safe and I/O-safe wrappers to POSIX-like, Unix-like, Linux, and Winsock syscall-like APIs, with configurable backends. It uses Rust references, slices, and return values instead of raw pointers, and I/O safety types instead of raw file descriptors, providing memory safety, I/O safety, and provenance. It uses Results for reporting errors, bitflags instead of bare integer flags, an Arg trait with optimizations to efficiently accept any Rust string type, and... -
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Colossal-AI
Making large AI models cheaper, faster and more accessible
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing. Together with better performance come larger model sizes. This imposes challenges to the memory wall of the current accelerator hardware such as GPU. It is never ideal to train large models such as Vision Transformer, BERT, and GPT on a single GPU or a single machine. There is an urgent demand to train models in a distributed environment. However, distributed training, especially model parallelism, often requires domain expertise in computer systems and architecture. ... -
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Apache Polaris
Apache Polaris, the interoperable, open source catalog
...It provides a centralized catalog that allows multiple compute engines and analytics systems to interact with the same datasets through a standardized interface. By implementing the Iceberg REST catalog API, Polaris enables distributed data platforms to access shared table metadata without tightly coupling storage systems and query engines. This design allows organizations to run queries on the same Iceberg tables using tools such as Apache Spark, Flink, Trino, and other analytics engines while maintaining consistency across platforms. Polaris also focuses on data governance, security, and interoperability within large-scale cloud data architectures. ... -
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Foundatio
Pluggable foundation blocks for building distributed apps
Pluggable foundation blocks for building loosely coupled distributed apps. Includes implementations in Redis, Azure, AWS, RabbitMQ and in memory (for development). When building several big cloud applications we found a lack of great solutions (that's not to say there aren't solutions out there) for many key pieces to building scalable distributed applications while keeping the development experience simple. -
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Akka
Build concurrent, distributed, and resilient message-driven apps
...Up to 50 million msg/sec on a single machine. Small memory footprint; ~2.5 million actors per GB of heap. Distributed systems without single points of failure. Load balancing and adaptive routing across nodes. Event Sourcing and CQRS with Cluster Sharding. Distributed Data for eventual consistency using CRDTs. Asynchronous non-blocking stream processing with backpressure. -
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Beads
A memory upgrade for your coding agent
Beads is an open-source project providing a distributed, structured memory system for AI coding agents, replacing ad-hoc text plans with a git-backed graph that represents tasks, dependencies, and progress in a persistent, queryable format. Instead of storing plans as unstructured Markdown or ephemeral notes, Beads organizes agent state, task artifacts, and relationships as nodes and edges in a version-controlled graph so that long-horizon projects don’t lose context or coherence as the agent proceeds. ... -
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Qwen
The official repo of Qwen chat & pretrained large language model
Qwen is a series of large language models developed by Alibaba Cloud, consisting of various pretrained versions like Qwen-1.8B, Qwen-7B, Qwen-14B, and Qwen-72B. These models, which range from smaller to larger configurations, are designed for a wide range of natural language processing tasks. They are openly available for research and commercial use, with Qwen's code and model weights shared on GitHub. Qwen's capabilities include text generation, comprehension, and conversation, making it a... -
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Citus
Distributed PostgreSQL as an extension
Citus is a PostgreSQL extension that transforms Postgres into a distributed database, so you can achieve high performance at any scale. With Citus, you extend your PostgreSQL database with new superpowers. Distributed tables are sharded across a cluster of PostgreSQL nodes to combine their CPU, memory, storage and I/O capacity. References tables are replicated to all nodes for joins and foreign keys from distributed tables and maximum read performance. ...
