Instagram has open-sourced Cinder, a high-performance fork of CPython 3.8, now available as a Facebook Incubator project on GitHub. The primary goal behind releasing Cinder is to create a faster and more unified version of CPython, addressing some of the performance bottlenecks inherent in the reference implementation of Python. By sharing Cinder as an open-source project, Instagram hopes to foster discussions about incorporating some of its performance improvements into CPython itself, while reducing redundancy in ongoing performance efforts for Python.
Cinder incorporates several performance-enhancing features to boost Python’s execution speed. These include bytecode inline caching, which dynamically optimizes frequently used Python operations, and eager evaluation of coroutines, where async functions that are immediately awaited can be executed more efficiently. Additionally, Cinder includes a method-at-a-time Just-In-Time (JIT) compiler and an experimental bytecode compiler that leverages type annotations to produce type-specialized bytecode. These optimizations contribute to improved execution speed, particularly for workloads that require intensive async operations. However, Instagram has emphasized that Cinder is not yet polished or documented for general use.
While Instagram has released Cinder as an open-source project, they have been clear about its purpose. The company does not intend for Cinder to become a full-fledged alternative to CPython or to be supported as an independent open-source project. Instead, Instagram’s developers focus on ensuring that Cinder meets the performance needs of their own production environment, without guaranteeing its stability, correctness, or performance for other use cases. They have invited feedback from the community, particularly from those with expertise in making Cinder even faster.
One of Cinder’s notable optimizations is its inline caching mechanism, which improves the performance of common Python opcodes by replacing them with specialized versions that are more efficient to execute. Additionally, Cinder optimizes the handling of coroutines by evaluating async functions eagerly. If a function call is immediately awaited and does not require further asynchronous operations, Cinder skips creating a coroutine object and directly returns the result. This reduces overhead in environments that rely heavily on async processing, providing a significant CPU optimization in such workloads. Although Cinder’s optimizations are promising, its development is still in its early stages, and it remains to be seen how much of it will be integrated into the broader Python ecosystem.