Python 上下文中的运行时是什么？它由什么组成？
What is runtime in context of Python? What does it consist of?
关于这个问题
我想了解 python 运行时是由什么组成的。我的猜测是：
- 包含所有运行时变量的 python 进程。
- 吉尔
- 底层解释器代码（CPython等）。
现在如果这是正确的，我们可以说 python 中的多处理创建了多个运行时，而 python 进程是我们可以直接与运行时关联的东西吗？ （我认为这是正确的选择）
或者，每个 python 线程都有自己的堆栈，它在与父进程相同的 GIL 和内存 space 上工作，可以称为具有单独的运行时吗？
或者，不管有多少个线程或进程运行，它们都会在一个运行时下？
简单来说，在Python的语境下runtime的定义是什么？
PS：明白线程和进程的区别。 GIL：我理解影响，但我不理解它。
你在谈论计算机科学中两个不同（但相似）的概念；多进程和多线程。以下是一些可能有用的 questions/answers 汇编：
Multiprocessing is the use of two or more central processing units (CPUs) within a single computer system.The term also refers to the ability of a system to support more than one processor or the ability to allocate tasks between them.
In computer architecture, multithreading is the ability of a central processing unit (CPU) (or a single core in a multi-core processor) to provide multiple threads of execution concurrently, supported by the operating system. This approach differs from multiprocessing. In a multithreaded application, the threads share the resources of a single or multiple cores, which include the computing units, the CPU caches, and the translation lookaside buffer (TLB).
- What is the difference between a process and a thread? -- Whosebug
Process
Each process provides the resources needed to execute a program. A process has a virtual address space, executable code, open handles to system objects, a security context, a unique process identifier, environment variables, a priority class, minimum and maximum working set sizes, and at least one thread of execution. Each process is started with a single thread, often called the primary thread, but can create additional threads from any of its threads.
Thread
A thread is an entity within a process that can be scheduled for execution. All threads of a process share its virtual address space and system resources. In addition, each thread maintains exception handlers, a scheduling priority, thread local storage, a unique thread identifier, and a set of structures the system will use to save the thread context until it is scheduled. The thread context includes the thread's set of machine registers, the kernel stack, a thread environment block, and a user stack in the address space of the thread's process. Threads can also have their own security context, which can be used for impersonating clients.
- Meaning of “Runtime Environment” and of “Software framework”? -- Whosebug
A runtime environment basically is a virtual machine that runs on top of a machine - provides machine abstraction. It is generally lower level than a library. A framework can contain a runtime environment, but is generally tied to a library.
In computer programming, a runtime system, also called runtime environment, primarily implements portions of an execution model. Most languages have some form of runtime system that provides an environment in which programs run. This environment may address a number of issues including the layout of application memory, how the program accesses variables, mechanisms for passing parameters between procedures, interfacing with the operating system, and otherwise. Typically the runtime system will have some responsibility for setting up and managing the stack and heap, and may include features such as garbage collection, threads or other dynamic features built into the language.
The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines.
However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.
Past efforts to create a “free-threaded” interpreter (one which locks shared data at a much finer granularity) have not been successful because performance suffered in the common single-processor case. It is believed that overcoming this performance issue would make the implementation much more complicated and therefore costlier to maintain.
关于 GIL 的更多信息的有用来源。
Whenever you fork, the entire Python process is duplicated in memory (including the Python interpreter, your code and any libraries, current stack etc.) to create a second process - one reason why forking a process is much more expensive than creating a thread.
This creates a new copy of the python interpreter.
One advantage of having two python interpreters running is that you now have two GIL's (Global Interpreter Locks), and therefore can have true multi-processing on a multi-core system.
Threads in one process share the same GIL, meaning only one runs at a given moment, giving only the illusion of parallelism.
Memory management in Python involves a private heap containing all Python objects and data structures. The management of this private heap is ensured internally by the Python memory manager. The Python memory manager has different components which deal with various dynamic storage management aspects, like sharing, segmentation, preallocation or caching.
当您通过 threading 库生成线程时，实际上是在单个 Python 运行时内生成作业。此运行时确保线程具有共享内存并通过 global interpreter lock:
管理这些线程的 运行 序列
当您通过 multiprocessing 库生成一个进程时，您正在生成一个包含运行指定代码的新 Python 解释器（新的运行时）的新进程。如果你想共享内存，你必须使用 multiprocessing.shared_memory:
This module provides a class, SharedMemory, for the allocation and management of shared memory to be accessed by one or more processes on a multicore or symmetric multiprocessor (SMP) machine. To assist with the life-cycle management of shared memory especially across distinct processes, a BaseManager subclass, SharedMemoryManager, is also provided in the multiprocessing.managers module.
我们可以说 python 中的多处理创建了多个运行时并且 python 进程是我们可以直接与运行时相关的东西吗？
是的。不同的 GIL，不同的内存 space，不同的运行时。
每个 python 线程都有自己的堆栈，与父进程在相同的 GIL 和内存上工作 space 可以称为具有单独的运行时吗？
取决于你所说的“堆栈”是什么意思。相同的 GIL，共享内存 space，相同的运行时。
不管有多少个线程和进程运行，它都会在一个运行时下吗？
取决于 multithreading/multiprocess.
简单来说，runtime在Python上下文中的定义是什么？
运行时环境字面意思是python.exe或/usr/bin/python。 Python 可执行文件将通过将您的 Python 代码转换为 CPU 可读字节码来解释它。当你多线程时，你只有一个 python 运行。当你 multiprocess 你有多个 pythons 运行.
我希望核心开发者可以进来更详细地讨论这个问题。目前，以上只是供您开始 understanding/seeing 大局的资源汇编。
关于这个问题
我想了解 python 运行时是由什么组成的。我的猜测是：
- 包含所有运行时变量的 python 进程。
- 吉尔
- 底层解释器代码（CPython等）。
现在如果这是正确的，我们可以说 python 中的多处理创建了多个运行时，而 python 进程是我们可以直接与运行时关联的东西吗？ （我认为这是正确的选择）
或者，每个 python 线程都有自己的堆栈，它在与父进程相同的 GIL 和内存 space 上工作，可以称为具有单独的运行时吗？
或者，不管有多少个线程或进程运行，它们都会在一个运行时下？
简单来说，在Python的语境下runtime的定义是什么？
PS：明白线程和进程的区别。 GIL：我理解影响，但我不理解它。
你在谈论计算机科学中两个不同（但相似）的概念；多进程和多线程。以下是一些可能有用的 questions/answers 汇编：
Multiprocessing is the use of two or more central processing units (CPUs) within a single computer system.The term also refers to the ability of a system to support more than one processor or the ability to allocate tasks between them.
In computer architecture, multithreading is the ability of a central processing unit (CPU) (or a single core in a multi-core processor) to provide multiple threads of execution concurrently, supported by the operating system. This approach differs from multiprocessing. In a multithreaded application, the threads share the resources of a single or multiple cores, which include the computing units, the CPU caches, and the translation lookaside buffer (TLB).
- What is the difference between a process and a thread? -- Whosebug
Process
Each process provides the resources needed to execute a program. A process has a virtual address space, executable code, open handles to system objects, a security context, a unique process identifier, environment variables, a priority class, minimum and maximum working set sizes, and at least one thread of execution. Each process is started with a single thread, often called the primary thread, but can create additional threads from any of its threads.
Thread
A thread is an entity within a process that can be scheduled for execution. All threads of a process share its virtual address space and system resources. In addition, each thread maintains exception handlers, a scheduling priority, thread local storage, a unique thread identifier, and a set of structures the system will use to save the thread context until it is scheduled. The thread context includes the thread's set of machine registers, the kernel stack, a thread environment block, and a user stack in the address space of the thread's process. Threads can also have their own security context, which can be used for impersonating clients.
- Meaning of “Runtime Environment” and of “Software framework”? -- Whosebug
A runtime environment basically is a virtual machine that runs on top of a machine - provides machine abstraction. It is generally lower level than a library. A framework can contain a runtime environment, but is generally tied to a library.
In computer programming, a runtime system, also called runtime environment, primarily implements portions of an execution model. Most languages have some form of runtime system that provides an environment in which programs run. This environment may address a number of issues including the layout of application memory, how the program accesses variables, mechanisms for passing parameters between procedures, interfacing with the operating system, and otherwise. Typically the runtime system will have some responsibility for setting up and managing the stack and heap, and may include features such as garbage collection, threads or other dynamic features built into the language.
The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines.
However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.
Past efforts to create a “free-threaded” interpreter (one which locks shared data at a much finer granularity) have not been successful because performance suffered in the common single-processor case. It is believed that overcoming this performance issue would make the implementation much more complicated and therefore costlier to maintain.
关于 GIL 的更多信息的有用来源。
Whenever you fork, the entire Python process is duplicated in memory (including the Python interpreter, your code and any libraries, current stack etc.) to create a second process - one reason why forking a process is much more expensive than creating a thread.
This creates a new copy of the python interpreter.
One advantage of having two python interpreters running is that you now have two GIL's (Global Interpreter Locks), and therefore can have true multi-processing on a multi-core system.
Threads in one process share the same GIL, meaning only one runs at a given moment, giving only the illusion of parallelism.
Memory management in Python involves a private heap containing all Python objects and data structures. The management of this private heap is ensured internally by the Python memory manager. The Python memory manager has different components which deal with various dynamic storage management aspects, like sharing, segmentation, preallocation or caching.
当您通过 threading 库生成线程时，实际上是在单个 Python 运行时内生成作业。此运行时确保线程具有共享内存并通过 global interpreter lock:
当您通过 multiprocessing 库生成一个进程时，您正在生成一个包含运行指定代码的新 Python 解释器（新的运行时）的新进程。如果你想共享内存，你必须使用 multiprocessing.shared_memory:
This module provides a class,
SharedMemory, for the allocation and management of shared memory to be accessed by one or more processes on a multicore or symmetric multiprocessor (SMP) machine. To assist with the life-cycle management of shared memory especially across distinct processes, a BaseManager subclass,SharedMemoryManager, is also provided in the multiprocessing.managers module.
我们可以说 python 中的多处理创建了多个运行时并且 python 进程是我们可以直接与运行时相关的东西吗？
是的。不同的 GIL，不同的内存 space，不同的运行时。
每个 python 线程都有自己的堆栈，与父进程在相同的 GIL 和内存上工作 space 可以称为具有单独的运行时吗？
取决于你所说的“堆栈”是什么意思。相同的 GIL，共享内存 space，相同的运行时。
不管有多少个线程和进程运行，它都会在一个运行时下吗？
取决于 multithreading/multiprocess.
简单来说，runtime在Python上下文中的定义是什么？
运行时环境字面意思是python.exe或/usr/bin/python。 Python 可执行文件将通过将您的 Python 代码转换为 CPU 可读字节码来解释它。当你多线程时，你只有一个 python 运行。当你 multiprocess 你有多个 pythons 运行.
我希望核心开发者可以进来更详细地讨论这个问题。目前，以上只是供您开始 understanding/seeing 大局的资源汇编。