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  2. Thread pool - Wikipedia

    en.wikipedia.org/wiki/Thread_pool

    The size of a thread pool is the number of threads kept in reserve for executing tasks. It is usually a tunable parameter of the application, adjusted to optimize program performance. [3] Deciding the optimal thread pool size is crucial to optimize performance.

  3. Thread-local storage - Wikipedia

    en.wikipedia.org/wiki/Thread-local_storage

    In computer programming, thread-local storage (TLS) is a memory management method that uses static or global memory local to a thread. The concept allows storage of data that appears to be global in a system with separate threads. Many systems impose restrictions on the size of the thread-local memory block, in fact often rather tight limits.

  4. Dask (software) - Wikipedia

    en.wikipedia.org/wiki/Dask_(software)

    A single-machine scheduler is the default scheduler which provides basic features on local processes or thread pool and is meant to be used on a single machine. It is simple and cheap to use but does not scale. Local threads A threaded scheduler leverages Python’s concurrent.futures.ThreadPoolExecuter to execute computations.

  5. Thread (computing) - Wikipedia

    en.wikipedia.org/wiki/Thread_(computing)

    A process with two threads of execution, running on one processor Program vs. Process vs. Thread Scheduling, Preemption, Context Switching. In computer science, a thread of execution is the smallest sequence of programmed instructions that can be managed independently by a scheduler, which is typically a part of the operating system. [1]

  6. RPyC - Wikipedia

    en.wikipedia.org/wiki/RPyC

    Version 2.X, the first release of which was 2.2, added thread synchronization and the Async concept, which can be used as a superset of events. Version 2.40 adds the execute method, that can be used to execute code on the other side of the connection directly.

  7. Global interpreter lock - Wikipedia

    en.wikipedia.org/wiki/Global_Interpreter_Lock

    Schematic representation of how threads work under GIL. Green - thread holding GIL, red - blocked threads. A global interpreter lock (GIL) is a mechanism used in computer-language interpreters to synchronize the execution of threads so that only one native thread (per process) can execute basic operations (such as memory allocation and reference counting) at a time. [1]

  8. Reference counting - Wikipedia

    en.wikipedia.org/wiki/Reference_counting

    There are three reasons for the atomicity requirements. First, a reference count field may be updated by multiple threads, and so an adequate atomic instruction, such as a (costly) compare-and-swap, must be used to update the counts. Second, it must be clear which object loses a reference so that its reference count can be adequately decremented.

  9. Beginthread - Wikipedia

    en.wikipedia.org/wiki/Beginthread

    The operating system allocates a stack for the thread containing the number of bytes specified by stack_size. If the value of stack_size is zero, the operating system creates a stack the same size as that of the main thread. [1]