CPython Internals

CPython Internals - explore CPython source co… | Awesome Repos

Features

Interpreter Implementation Notes - Provides detailed technical notes and source code analysis of the CPython internal engine.
C API Development - Guides the creation of native modules using the C API to improve performance.
Python Extension Development - Provides guidance on creating high-performance native modules for the Python runtime.
Reference Counting - Analyzes the reference counting mechanism used for immediate memory reclamation of Python objects.
Memory Management Analyses - Analyzes the internal implementation of reference counting, generational garbage collection, and the global interpreter lock.
Abstract Syntax Tree Parsing - Analyzes the conversion of Python source code into abstract syntax trees as part of the compilation pipeline.
Generational Garbage Collection - Details the generational garbage collection algorithm used to reclaim unreachable object cycles.
Bytecode Virtual Machines - Explains the virtual machine loop that executes Python's low-level bytecode instructions.
Native C Extension APIs - Documents the native C API used to create high-performance Python extensions.
Global Interpreter Locks - Explains the role of the Global Interpreter Lock in ensuring thread safety for the interpreter state.
Internal Object Models - Examines how core data types and internal objects are structured within the CPython engine.
Runtime Implementation Analysis - Provides a detailed analysis of the internal C source code and architecture of CPython.
Runtime Memory Management Studies - Explains the CPython engine's approach to object allocation, reference counting, and garbage collection.
Implementation Guides - Acts as a detailed technical implementation guide for the CPython interpreter architecture.
Object Model Analysis - Describes how core Python data types and the object model are implemented at a low level.
Object Model Implementations - Examines the low-level C implementation of the Python object model and internal data structures.
Bytecode Pipeline Analysis - Traces the pipeline from raw source code through abstract syntax trees to executable instructions.
Compilation Pipeline Tracing - Maps the compilation process from raw grammar through syntax trees to final bytecode.
Logic Analysis - Provides a technical analysis of memory handling and exception processing within the interpreter.
Logic Documentation - Provides comprehensive explanations of memory management, garbage collection, and the global interpreter lock.
Multi-Version Bytecode Analyzers - Analyzes the compilation pipeline and bytecode instructions across the CPython architecture.

Open-source alternatives to CPython Internals

Similar open-source projects, ranked by how many features they share with CPython Internals.

facebookincubator/cinder
facebookincubator/cinder
3,764View on GitHub
Cinder is a high-performance Python runtime implementation based on CPython. It is designed as an execution environment optimized for large-scale distributed systems and cloud environments. The project integrates a distributed memory cache and an asynchronous memory layer to manage data across multiple network nodes. It also provides a native C extension framework for developing high-performance compiled modules that link directly into the interpreter memory space. The system covers capabilities for asynchronous data retrieval, large-scale execution, and the integration of embedded scripting
Pythoncompilerinterpreterjit
View on GitHub3,764
python/cpython
python/cpython
73,265View on GitHub
CPython is the primary, community-maintained reference implementation of the Python programming language. It functions as a high-level, interpreted execution environment that compiles source code into platform-independent bytecode for processing by a stack-based virtual machine. The runtime manages memory through a combination of reference counting and generational cyclic garbage collection, while dynamic type dispatching determines object behavior at runtime based on metadata stored within object headers. The project is distinguished by its C-based architecture, which provides a stable forei
Python
View on GitHub73,265
crazyguitar/pysheeet
crazyguitar/pysheeet
8,150View on GitHub
pysheeet is a technical reference library providing a curated collection of code snippets and implementation patterns for advanced Python development, system integration, and high-performance computing. It serves as a comprehensive guide for implementing low-level network programming, native C extensions, and asynchronous and concurrent programming. The project provides specialized frameworks for the development and deployment of large language models, including tools for distributed GPU inference and high-performance serving. It also includes detailed patterns for high-performance computing
Python
View on GitHub8,150
luau-lang/luau
luau-lang/luau
5,635View on GitHub
Luau is a high-performance programming language evolved from Lua 5.1. It consists of a bytecode virtual machine, an embeddable scripting engine, and a static code analyzer designed to provide a secure, sandboxed execution environment for host applications. The project is distinguished by its gradual typing system, which combines automatic type inference with optional manual annotations to detect bugs without requiring full static typing. It also features native vector mathematics for high-performance operations and a specialized debugging system that uses bytecode patching to minimize executi
C++luaprogramming-languagescripting-language
View on GitHub5,635

See all 30 alternatives to CPython Internals

zpointCPython-Internals

Features

Open-source alternatives to CPython Internals

facebookincubator/cinder

python/cpython

crazyguitar/pysheeet

luau-lang/luau

Star history

Open-source alternatives to CPython Internals

facebookincubator/cinder

python/cpython

crazyguitar/pysheeet

luau-lang/luau