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In concurrent programming, there are two basic units of execution: processes and threads. In the Java programming language, concurrent programming is mostly concerned with threads. However, processes are also important.

A computer system normally has many active processes and threads. This is true even in systems that only have a single execution core, and thus only have one thread actually executing at any given moment. Processing time for a single core is shared among processes and threads through an OS feature called time slicing.

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It's becoming more and more common for computer systems to have multiple processors or processors with multiple execution cores. This greatly enhances a system's capacity for concurrent execution of processes and threads  but concurrency is possible even on simple systems, without multiple processors or execution cores.

Multithreaded execution is an essential feature of the Java platform. Every application has at least one thread  or several, if you count "system" threads that do things like memory management and signal handling. But from the application programmer's point of view, you start with just one thread, called the main thread. This thread has the ability to create additional threads, as we'll demonstrate in the next section.

The number of execution threads is controlled either by using the -t/--threads command line argument or by using the JULIA_NUM_THREADS environment variable. When both are specified, then -t/--threads takes precedence.

The number of threads can either be specified as an integer (--threads=4) or as auto (--threads=auto), where auto tries to infer a useful default number of threads to use (see Command-line Options for more details).

The number of threads specified with -t/--threads is propagated to worker processes that are spawned using the -p/--procs or --machine-file command line options. For example, julia -p2 -t2 spawns 1 main process with 2 worker processes, and all three processes have 2 threads enabled. For more fine grained control over worker threads use addprocs and pass -t/--threads as exeflags.

The Garbage Collector (GC) can use multiple threads. The amount used is either half the number of compute worker threads or configured by either the --gcthreads command line argument or by using the JULIA_NUM_GC_THREADS environment variable.

When a program's threads are busy with many tasks to run, tasks may experience delays which may negatively affect the responsiveness and interactivity of the program. To address this, you can specify that a task is interactive when you Threads.@spawn it:

Although Julia's threads can communicate through shared memory, it is notoriously difficult to write correct and data-race free multi-threaded code. Julia's Channels are thread-safe and may be used to communicate safely.

Additionally, Julia is not memory safe in the presence of a data race. Be very careful about reading any data if another thread might write to it! Instead, always use the lock pattern above when changing data (such as assigning to a global or closure variable) accessed by other threads.

To fix this, buffers that are specific to the task may be used to segment the sum into chunks that are race-free. Here sum_single is reused, with its own internal buffer s, and vector a is split into nthreads() chunks for parallel work via nthreads() @spawn-ed tasks.

Buffers should not be managed based on threadid() i.e. buffers = zeros(Threads.nthreads()) because concurrent tasks can yield, meaning multiple concurrent tasks may use the same buffer on a given thread, introducing risk of data races. Further, when more than one thread is available tasks may change thread at yield points, which is known as task migration.

At this time, most operations in the Julia runtime and standard libraries can be used in a thread-safe manner, if the user code is data-race free. However, in some areas work on stabilizing thread support is ongoing. Multi-threaded programming has many inherent difficulties, and if a program using threads exhibits unusual or undesirable behavior (e.g. crashes or mysterious results), thread interactions should typically be suspected first.

Our purpose is to help all quilting and sewing enthusiasts learn more about threads, needles, notions, sewing, and quilting. We not only teach the principles of how to use tools in a more efficient and exciting manner, we also underscore how simple improvements, such as adjusting tension and matching the right needle size to the top thread, can have a sizable impact in stitch quality and overall sewing success.

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Post threads lend themselves really well to creating intrigue and build-up to the climax of a story, or the nitty-gritty of a conversation.

When publishing one Post at a time, we recommend waiting about an hour after publishing your first Post to publish your second, and waiting another 15 minutes or so to publish your third.

An execution mode, which can either be supervisor or user mode.By default, threads run in supervisor mode and allow access toprivileged CPU instructions, the entire memory address space, andperipherals. User mode threads have a reduced set of privileges.This depends on the CONFIG_USERSPACE option. See User Mode.

Although the diagram above may appear to suggest that both Ready andRunning are distinct thread states, that is not the correctinterpretation. Ready is a thread state, and Running is aschedule state that only applies to Ready threads.

These stacks save memory because an MPU region will never need to beprogrammed to cover the stack buffer itself, and the kernel will not needto reserve additional room for the privilege elevation stack, or memorymanagement data structures which only pertain to user mode threads.

If it is known that a stack will need to host user threads, or if thiscannot be determined, define the stack with K_THREAD_STACK macros.This may use more memory but the stack object is suitable for hostinguser threads.

When enabled (see CONFIG_NUM_METAIRQ_PRIORITIES), there is a specialsubclass of cooperative priorities at the highest (numerically lowest)end of the priority space: meta-IRQ threads. These are scheduledaccording to their normal priority, but also have the special abilityto preempt all other threads (and other meta-IRQ threads) at lowerpriorities, even if those threads are cooperative and/or have taken ascheduler lock. Meta-IRQ threads are still threads, however,and can still be interrupted by any hardware interrupt.

Unlike similar features in other OSes, meta-IRQ threads are truethreads and run on their own stack (which must be allocated normally),not the per-CPU interrupt stack. Design work to enable the use of theIRQ stack on supported architectures is pending.

Static threads with zero delay should not normally have MetaIRQ priority levels. This can preempt the system initialization handling (depending on the priority of the main thread) and cause surprising ordering side effects. It will not affect anything in the OS per se, but consider it bad practice. Use a SYS_INIT() callback if you need to run code before entrance to the application main().

This API uses k_spin_lock only when accessing the _kernel.threads queue elements. It unlocks it during user callback function processing. If a new task is created when this foreach function is in progress, the added new task would not be included in the enumeration. If a task is aborted during this enumeration, there would be a race here and there is a possibility that this aborted task would be included in the enumeration.

This routine causes the current thread to yield execution to another thread of the same or higher priority. If there are no other ready threads of the same or higher priority, the routine returns immediately.

After k_thread_abort() returns, the thread is guaranteed not to be running or to become runnable anywhere on the system. Normally this is done via blocking the caller (in the same manner as k_thread_join()), but in interrupt context on SMP systems the implementation is required to spin for threads that are running on other CPUs.

To enable time slicing, slice must be non-zero. The scheduler ensures that no thread runs for more than the specified time limit before other threads of that priority are given a chance to execute. Any thread whose priority is higher than prio is exempted, and may execute as long as desired without being preempted due to time slicing.

Time slicing only limits the maximum amount of time a thread may continuously execute. Once the scheduler selects a thread for execution, there is no minimum guaranteed time the thread will execute before threads of greater or equal priority are scheduled.

This works by elevating the thread priority temporarily to a cooperative priority, allowing cheap synchronization vs. other preemptible or cooperative threads running on the current CPU. It does not prevent preemption or asynchrony of other types. It does not prevent threads from running on other CPUs when CONFIG_SMP=y. It does not prevent interrupts from happening, nor does it prevent threads with MetaIRQ priorities from preempting the current thread. In general this is a historical API not well-suited to modern applications, use with care.

Used for stacks embedded within other data structures. Use is highly discouraged but in some cases necessary. For memory protection scenarios, it is very important that any RAM preceding this member not be writable by threads else a stack overflow will lead to silent corruption. In other words, the containing data structure should live in RAM owned by the kernel. 006ab0faaa