facebookincubator/cinder

Features

• CPython-Based Runtimes - Implements a high-performance Python runtime based on a modified CPython interpreter optimized for cloud environments.
• Language Runtime Implementations - Provides a high-performance Python runtime implementation optimized for large-scale distributed systems and cloud environments.
• Distributed Caching - Ships a scalable memory layer to store and retrieve data across multiple network nodes.
• Distributed Data Caching Layers - Provides a high-performance data layer via a scalable network of memory nodes.
• High-Performance Data Infrastructures - Implements a scalable memory layer providing high-throughput data access for asynchronous services.
• In-Memory Caches - Implements a scalable in-memory caching layer across multiple network nodes to reduce database load.
• Distributed Memory Caches - Provides a scalable in-memory storage layer that distributes data across multiple network nodes.
• In-Memory Data Stores - Implements a networked in-memory data store for high-concurrency memory management in large clusters.
• Python Extension Development - Provides the ability to write high-performance native modules in compiled languages for use in Python.
• Native Module Development - Provides the framework to build high-performance native extensions in compiled languages for import into the runtime.
• Bytecode Virtual Machines - Provides a virtual machine that executes intermediate bytecode for portable and consistent runtime performance.
• Native C Extension APIs - Provides a stable C-language interface for linking high-performance compiled modules directly into the interpreter memory space.
• High-Performance Runtimes - Implements a high-performance execution engine optimized for throughput and latency in large-scale cloud environments.
• Large-Scale Runtime Execution - Optimizes Python execution for the demands of massive cloud clusters and distributed environments.
• Asynchronous Data Fetching - Manages non-blocking network communication and data access for high-concurrency distributed systems.
• Embedded Interpreters - Integrates a Python interpreter into native applications to execute dynamic code from within compiled binaries.
• Embedded Script Execution - Enables the execution of dynamic Python scripts by embedding the interpreter within a native compiled application.
• Native Extension Frameworks - Ships a framework for developing high-performance compiled modules that link directly into the interpreter memory space.
• Pluggable Module Systems - Allows loading external compiled binaries as importable modules to extend core capabilities without modifying the runtime.

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