How Much Faster Is Python 3.11 Compared to Python 3.10?

Python 3.11 has brought significant performance improvements, making it a considerably faster version compared to its predecessor, Python 3.10. This boost in speed is largely attributed to PEP 659, also known as the “Specializing Adaptive Interpreter,” a core component of the Faster CPython project. Let’s delve into how Python 3.11 achieves this enhanced performance and what makes it stand out in terms of execution speed.

At its heart, Python is a dynamic language. However, real-world Python code often exhibits “type stability,” meaning that in many sections of code, the types of objects remain consistent throughout execution. Python 3.11’s interpreter is designed to leverage this type stability through a process called specialization. During runtime, Python actively monitors the executing code, looking for recurring patterns and type stability. When it identifies “hot” code regions – parts of the code executed frequently – it dynamically replaces generic operations with specialized ones. These specialized operations are finely tuned for specific use cases and data types, allowing them to operate much faster than their general-purpose counterparts. This mechanism is complemented by inline caching, where Python stores the results of computationally intensive operations directly within the bytecode itself, further accelerating execution.

One of the key innovations in Python 3.11 is the adaptive specializing interpreter itself. It does not specialize code prematurely. Specialization only kicks in when Python detects code that is executed repeatedly, ensuring that resources aren’t wasted on code that runs only once. Furthermore, Python 3.11 is capable of de-specialization. If the code’s behavior becomes too dynamic or the types change frequently, Python can revert to more generic operations. This adaptive nature ensures that specialization remains beneficial even in dynamic environments. The specialization process is performed periodically and is designed to be lightweight, allowing Python to adapt quickly to changing execution conditions.

The performance gains in Python 3.11 aren’t just theoretical; they translate into tangible speed improvements across various operations. Here’s a breakdown of some key optimizations and their impact, as outlined by PEP 659:

• Faster Binary Operations: Operations like addition, subtraction, and multiplication (+, -, *) for common data types such as integers (int), floating-point numbers (float), and strings (str) now utilize optimized “fast paths” tailored to their underlying types. This specialization can lead to speed improvements of up to 10%.

• Optimized Subscripting: Accessing elements within container types like lists (list), tuples (tuple), and dictionaries (dict) using subscripting (a[i]) has been significantly improved. Python 3.11 directly indexes the underlying data structures of these containers, bypassing overhead. Specialization also extends to custom __getitem__() methods, which are now inlined, similar to function calls. These subscripting optimizations can yield speedups in the range of 10-25%.

• Faster Function Calls: Calls to frequently used built-in functions and types, for example len() and str(), are now streamlined. Python 3.11 directly invokes their underlying C implementations, avoiding the overhead of the standard Python calling convention. This optimization can lead to speed improvements of up to 20%.

• Efficient Variable and Attribute Access: Loading global variables (print, len) and object attributes (o.attr) is now much faster. Python 3.11 caches the index of objects within the global/built-in namespace and the attribute index within class/object namespaces. In many cases, this caching eliminates namespace lookups entirely, resulting in quicker access. Similarly, loading methods for calls (o.meth()) benefits from caching the method’s actual address, removing namespace lookups even in complex inheritance hierarchies.

• Unpacking Sequence Optimization: The unpacking sequence operation (*seq) is specialized for common container types like lists and tuples. This avoids internal calling conventions and can result in speed improvements of around 8%.

In conclusion, Python 3.11 represents a significant leap forward in performance compared to Python 3.10. Through the implementation of PEP 659 and its adaptive specializing interpreter, Python 3.11 achieves substantial speed gains across a wide range of operations. By intelligently specializing and caching operations based on runtime behavior and type stability, Python 3.11 delivers a faster and more efficient execution environment for Python code, making it a compelling upgrade for developers seeking performance improvements. For a deeper dive into the specifics of these optimizations, refer to the detailed PEP 659.