GIL в Python для senior interview
GIL — самая известная «фича» Python, о которой большинство разработчиков знают ровно одну фразу: «мешает многопоточности». Этого хватает для джуна, но не для сеньора. Статья построена так, чтобы после её прочтения вы могли не только ответить на вопросы по GIL на senior-интервью, но и объяснить коллеге-стажеру, что происходит внутри интерпретатора, когда два потока пытаются одновременно изменить один список.
A GIL finalmente saiu do caminho.
O Python 3.14 torna a build free-threaded oficialmente suportada — não mais experimental. Threads CPU-bound que antes brigavam pelo mesmo core agora escalam de verdade.
Escrevi sobre o que muda na prática, como instalar em cada distro e o que você não pode mais assumir sobre thread safety:
https://www.riverfount.dev.br/posts/python_314_free_threading/
Você tem quatro núcleos disponíveis. Seu script Python usa um. Isso nunca foi um bug — era uma decisão de design que durava desde 1992. O Python 3.14 não remove essa limitação de vez, mas dá o passo mais concreto até agora para deixá-la para trás. O que é a GIL e por que ela importa A Global Interpreter Lock é um mutex que garante que apenas uma thread execute bytecode Python por vez dentro de um processo CPython. Ela existe por uma razão pragmática: simplifica imensamente o gerenciamento de memória e a integração com extensões C, que historicamente assumem que esse lock existe.
Python's Global Interpreter Lock prevents execution on more than one CPU core at the same time, even when multiple threads are used. However, starting with Python 3.13 an experimental build allows disabling the GIL. While prior work has examined speedup implications of this disabling, the effects on energy consumption and hardware utilization have received less attention. This study measures execution time, CPU utilization, memory usage, and energy consumption using four workload categories: NumPy-based, sequential kernels, threaded numerical workloads, and threaded object workloads, comparing GIL and free-threaded builds of Python 3.14.2. The results highlight a trade-off. For parallelizable workloads operating on independent data, the free-threaded build reduces execution time by up to 4 times, with a proportional reduction in energy consumption, and effective multi-core utilization, at the cost of an increase in memory usage. In contrast, sequential workloads do not benefit from removing the GIL and instead show a 13-43% increase in energy consumption. Similarly, workloads where threads frequently access and modify the same objects show reduced improvements or even degradation due to lock contention. Across all workloads, energy consumption is proportional to execution time, indicating that disabling the GIL does not significantly affect power consumption, even when CPU utilization increases. When it comes to memory, the no-GIL build shows a general increase, more visible in virtual memory than in physical memory. This increase is primarily attributed to per-object locking, additional thread-safety mechanisms in the runtime, and the adoption of a new memory allocator. These findings suggest that Python's no-GIL build is not a universal improvement. Developers should evaluate whether their workload can effectively benefit from parallel execution before adoption.
Unlocking Python's Cores:Energy Implications of Removing the GIL
https://arxiv.org/abs/2603.04782
#HackerNews #UnlockingPythonCores #EnergyImplications #GIL #PythonProgramming #TechNews #HackerNews
Python's Global Interpreter Lock prevents execution on more than one CPU core at the same time, even when multiple threads are used. However, starting with Python 3.13 an experimental build allows disabling the GIL. While prior work has examined speedup implications of this disabling, the effects on energy consumption and hardware utilization have received less attention. This study measures execution time, CPU utilization, memory usage, and energy consumption using four workload categories: NumPy-based, sequential kernels, threaded numerical workloads, and threaded object workloads, comparing GIL and free-threaded builds of Python 3.14.2. The results highlight a trade-off. For parallelizable workloads operating on independent data, the free-threaded build reduces execution time by up to 4 times, with a proportional reduction in energy consumption, and effective multi-core utilization, at the cost of an increase in memory usage. In contrast, sequential workloads do not benefit from removing the GIL and instead show a 13-43% increase in energy consumption. Similarly, workloads where threads frequently access and modify the same objects show reduced improvements or even degradation due to lock contention. Across all workloads, energy consumption is proportional to execution time, indicating that disabling the GIL does not significantly affect power consumption, even when CPU utilization increases. When it comes to memory, the no-GIL build shows a general increase, more visible in virtual memory than in physical memory. This increase is primarily attributed to per-object locking, additional thread-safety mechanisms in the runtime, and the adoption of a new memory allocator. These findings suggest that Python's no-GIL build is not a universal improvement. Developers should evaluate whether their workload can effectively benefit from parallel execution before adoption.
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