Python 3.12, the latest stable release, arrived on October 2, 2023, bringing with it a host of new features and improvements. For developers and performance enthusiasts alike, a key question arises: how fast is Python 3.12 compared to its predecessor, Python 3.11? This article delves into the performance enhancements introduced in Python 3.12, drawing insights from the official changelog and focusing on the core elements that contribute to speed and efficiency gains. We will explore the specific features and optimizations that make Python 3.12 a potentially faster and more efficient runtime environment for your code.
Key Performance Improvements in Python 3.12
Python 3.12 builds upon the solid foundation of 3.11, which itself brought significant performance boosts. The focus in this iteration remains on usability, correctness, and crucially, performance. Let’s examine the standout features contributing to the speed improvements in Python 3.12.
Comprehension Inlining: PEP 709
One of the most impactful changes for performance in Python 3.12 is the implementation of comprehension inlining, as detailed in PEP 709. In previous versions of Python, list, dictionary, and set comprehensions were executed by creating a new, single-use function object for each comprehension. This introduced overhead, impacting the execution speed.
Python 3.12 fundamentally changes this by inlining comprehensions. Instead of function calls, the comprehension code is directly integrated into the surrounding scope. This optimization dramatically reduces the overhead associated with comprehensions, leading to speed improvements of up to two times in some cases.
Alt text: Python 3.12 logo representing comprehension inlining performance improvement.
The benefits of comprehension inlining are multifaceted:
- Reduced Function Call Overhead: Eliminating the creation and calling of function objects directly cuts down on execution time.
- Simplified Execution Flow: Inlined comprehensions streamline the code execution path, making it more direct and efficient.
- Performance Boost for Data Processing: Comprehensions are widely used for data manipulation and transformation in Python. Inlining significantly speeds up these operations, benefiting a broad range of applications.
While the primary goal of PEP 709 was performance, it also brings subtle but noteworthy behavioral changes:
- Tracebacks and Profiling: Tracebacks will no longer show a separate frame for comprehensions. Similarly, profilers will not register comprehensions as function calls. This simplifies debugging and profiling output.
- Symbol Tables: The
symtablemodule’s output changes, as comprehensions no longer have child symbol tables. Their locals are now integrated into the parent function’s symbol table. locals()Behavior: Callinglocals()within a comprehension now includes variables from the outer scope and omits the synthetic.0variable that was previously present.
These changes, while altering some introspection aspects, are outweighed by the substantial performance gains achieved through comprehension inlining. For codebases that heavily utilize comprehensions, upgrading to Python 3.12 is likely to yield noticeable speed improvements without requiring code modifications.
Per-Interpreter GIL: PEP 684
PEP 684 introduces a per-interpreter GIL (Global Interpreter Lock). Historically, Python has used a single GIL for the entire process, limiting true parallelism in multi-threaded Python programs, especially for CPU-bound tasks. While this change doesn’t directly speed up single-threaded Python code, it opens up significant performance potential in scenarios involving sub-interpreters and concurrency.
Alt text: Diagram depicting Python 3.12 Per-Interpreter GIL for improved concurrency.
With a per-interpreter GIL, each sub-interpreter can have its own GIL. This allows Python programs to fully leverage multiple CPU cores by running different parts of the application in separate sub-interpreters, each with its own GIL. This is a major architectural shift, enabling true parallel execution for CPU-bound Python workloads that are structured using sub-interpreters.
Currently, the per-interpreter GIL is primarily accessible through the C-API. However, a Python API is anticipated in Python 3.13, which will make this feature more readily available to Python developers.
The implications of the per-interpreter GIL for performance are substantial in concurrent programming:
- True Parallelism for CPU-Bound Tasks: Sub-interpreters can now execute Python code in parallel on multiple cores, bypassing the traditional GIL limitation for CPU-intensive operations.
- Improved Scalability: Applications designed to utilize sub-interpreters can scale more effectively on multi-core systems, leading to significant performance gains for concurrent workloads.
- Enhanced Concurrency for I/O-Bound and CPU-Bound Mixes: While the traditional GIL is less of a bottleneck for I/O-bound tasks, the per-interpreter GIL allows for better concurrency when I/O-bound and CPU-bound operations are combined within a single application using sub-interpreters.
It’s important to note that the per-interpreter GIL is not a magic bullet for all performance issues. Existing multi-threaded Python code that relies on threads within a single interpreter will not automatically see performance improvements from this change. The benefits are realized when applications are redesigned to leverage sub-interpreters for concurrent execution.
CPython Implementation Improvements
Beyond comprehension inlining and the per-interpreter GIL, Python 3.12 includes a range of CPython implementation improvements that contribute to overall performance enhancements. While the changelog doesn’t provide extensive details on each specific micro-optimization, it highlights “CPython implementation improvements” as a category of changes. These often include:
- Code Optimizations in the Interpreter Core: Refinements to the CPython interpreter’s core logic, bytecode execution, and internal data structures can lead to subtle but cumulative performance gains across various Python operations.
- Memory Management Enhancements: Improvements in memory allocation, deallocation, and garbage collection can reduce overhead and improve the efficiency of Python programs, especially those that are memory-intensive.
- Optimized Built-in Functions and Modules: Specific built-in functions and standard library modules may receive targeted optimizations for improved speed and efficiency.
While these individual CPython implementation improvements may be less prominent than features like comprehension inlining, their collective impact contributes to making Python 3.12 a more performant runtime overall.
Low Impact Monitoring for CPython: PEP 669
PEP 669 introduces a new low-impact monitoring API for CPython. While primarily aimed at profilers, debuggers, and monitoring tools, this feature has an indirect positive impact on performance by enabling the creation of more efficient performance analysis tools.
Alt text: Python 3.12 Low Impact Monitoring API diagram for performance analysis tools.
The key aspect of PEP 669 is its low overhead. Traditional profiling and debugging techniques can introduce significant performance penalties due to the instrumentation required to collect data. The new monitoring API is designed to minimize this overhead, providing near-zero impact when monitoring is not actively in use and very low impact when it is.
This allows for the development of more efficient and less intrusive profiling and debugging tools. By enabling developers to better understand the performance characteristics of their Python code with minimal overhead, PEP 669 indirectly contributes to performance optimization efforts. Developers can use these improved tools to identify bottlenecks and optimize their code more effectively.
Benchmarking Python 3.12 vs Python 3.11: Is It Faster?
While the changelog highlights several performance-enhancing features, the ultimate question for many is: how much faster is Python 3.12 in real-world scenarios? Direct benchmarks comparing Python 3.12 and 3.11 are crucial to quantify the performance gains.
Unfortunately, the provided changelog does not include specific benchmark numbers. To get a definitive answer, we would typically look to:
- Official Python Benchmark Suites: The Python project often maintains benchmark suites (like pyperformance) that are used to track performance across versions. Results from these suites would provide a standardized comparison.
- Community Benchmarks: Performance-focused members of the Python community often conduct their own benchmarks and share the results. Searching for community benchmarks comparing Python 3.12 and 3.11 can provide valuable real-world data.
- Application-Specific Benchmarks: The actual performance improvement you see will depend heavily on your specific application’s workload. Running benchmarks on your own code, comparing execution times under Python 3.11 and 3.12, is the most relevant way to assess the performance impact for your use case.
General Expectations based on Features:
Based on the features discussed, we can anticipate performance improvements in Python 3.12 compared to 3.11, particularly in the following areas:
- Code with Heavy Comprehension Usage: Applications that make extensive use of list, dictionary, or set comprehensions are highly likely to see noticeable speedups due to comprehension inlining.
- Concurrent Applications Using Sub-interpreters (in the future): While currently requiring C-API access, applications redesigned to use sub-interpreters for concurrency will be able to leverage the per-interpreter GIL for true parallelism and significant performance gains on multi-core systems.
- General Python Code Execution: The cumulative effect of CPython implementation improvements should contribute to a general, albeit possibly smaller, performance boost across a wide range of Python code.
Need for Empirical Data:
Without concrete benchmark numbers from official or community sources at this time, it’s challenging to provide precise figures on the overall speed increase of Python 3.12. However, the features implemented strongly suggest that Python 3.12 is indeed faster than Python 3.11 in many common scenarios.
Recommendation:
For developers concerned with performance, testing your applications with Python 3.12 is highly recommended. Run your own benchmarks to quantify the specific performance gains for your workloads. The combination of comprehension inlining, per-interpreter GIL (for future concurrent applications), and general CPython optimizations makes Python 3.12 a compelling upgrade for performance-sensitive Python projects.
Conclusion: Python 3.12 – A Step Forward in Performance
Python 3.12 continues the trend of performance improvement in the Python language. Key features like comprehension inlining offer immediate and significant speedups for codebases that utilize comprehensions. The introduction of the per-interpreter GIL sets the stage for true parallel execution in Python, especially as Python APIs for sub-interpreters mature in future versions. Coupled with general CPython implementation optimizations and low-impact monitoring capabilities, Python 3.12 represents a meaningful step forward in terms of performance and efficiency.
While the exact magnitude of the performance gain will vary depending on the application, the architectural and implementation changes in Python 3.12 strongly indicate that it is indeed faster than Python 3.11 in many common use cases. For developers seeking to optimize their Python applications, upgrading to Python 3.12 is a worthwhile consideration to leverage these performance enhancements.
Further Reading and Resources:
- What’s New In Python 3.12 (Original Article)
- PEP 709: Inlined comprehensions
- PEP 684: A per-interpreter GIL
- PEP 669: Low impact monitoring for CPython
