# Multi-Threading

`Base.Threads.@threads` — Macro

    Threads.@threads [schedule] for ... end

A macro to parallelize a `for` loop to run with multiple threads. Splits the iteration space among multiple tasks and runs those tasks on threads according to a scheduling policy. A barrier is placed at the end of the loop which waits for all tasks to finish execution.

The `schedule` argument can be used to request a particular scheduling policy. The only currently supported value is `:static`, which creates one task per thread and divides the iterations equally among them. Specifying `:static` is an error if used from inside another `@threads` loop or from a thread other than 1.

The default schedule (used when no `schedule` argument is present) is subject to change.

Julia 1.5

The `schedule` argument is available as of Julia 1.5.

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`Base.Threads.foreach` — Function

    Threads.foreach(f, channel::Channel;
                    schedule::Threads.AbstractSchedule=Threads.FairSchedule(),
                    ntasks=Threads.nthreads())

Similar to `foreach(f, channel)`, but iteration over `channel` and calls to `f` are split across `ntasks` tasks spawned by `Threads.@spawn`. This function will wait for all internally spawned tasks to complete before returning.

If `schedule isa FairSchedule`, `Threads.foreach` will attempt to spawn tasks in a manner that enables Julia's scheduler to more freely load-balance work items across threads. This approach generally has higher per-item overhead, but may perform better than `StaticSchedule` in concurrence with other multithreaded workloads.

If `schedule isa StaticSchedule`, `Threads.foreach` will spawn tasks in a manner that incurs lower per-item overhead than `FairSchedule`, but is less amenable to load-balancing. This approach thus may be more suitable for fine-grained, uniform workloads, but may perform worse than `FairSchedule` in concurrence with other multithreaded workloads.

Julia 1.6

This function requires Julia 1.6 or later.

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`Base.Threads.@spawn` — Macro

    Threads.@spawn expr

Create and run a `Task` on any available thread. To wait for the task to finish, call `wait` on the result of this macro, or call `fetch` to wait and then obtain its return value.

Values can be interpolated into `@spawn` via `$`, which copies the value directly into the constructed underlying closure. This allows you to insert the value of a variable, isolating the asynchronous code from changes to the variable's value in the current task.

Note

See the manual chapter on threading for important caveats.

Julia 1.3

This macro is available as of Julia 1.3.

Julia 1.4

Interpolating values via `$` is available as of Julia 1.4.

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`Base.Threads.threadid` — Function

    Threads.threadid()

Get the ID number of the current thread of execution. The master thread has ID `1`.

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`Base.Threads.nthreads` — Function

    Threads.nthreads()

Get the number of threads available to the Julia process. This is the inclusive upper bound on `threadid()`.

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## Synchronization

`Base.Threads.Condition` — Type

    Threads.Condition([lock])

A thread-safe version of `Base.Condition`.

To call `wait` or `notify` on a `Threads.Condition`, you must first call `lock` on it. When `wait` is called, the lock is atomically released during blocking, and will be reacquired before `wait` returns. Therefore idiomatic use of a `Threads.Condition` `c` looks like the following:

    lock(c)
    try
        while !thing_we_are_waiting_for
            wait(c)
        end
    finally
        unlock(c)
    end

Julia 1.2

This functionality requires at least Julia 1.2.

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`Base.Event` — Type

    Event()

Create a level-triggered event source. Tasks that call `wait` on an `Event` are suspended and queued until `notify` is called on the `Event`. After `notify` is called, the `Event` remains in a signaled state and tasks will no longer block when waiting for it.

Julia 1.1

This functionality requires at least Julia 1.1.

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See also Synchronization.

## Atomic operations

Warning

The API for atomic operations has not yet been finalized and is likely to change.

`Base.Threads.Atomic` — Type

    Threads.Atomic{T}

Holds a reference to an object of type `T`, ensuring that it is only accessed atomically, i.e. in a thread-safe manner.

Only certain "simple" types can be used atomically, namely the primitive boolean, integer, and float-point types. These are `Bool`, `Int8`...`Int128`, `UInt8`...`UInt128`, and `Float16`...`Float64`.

New atomic objects can be created from a non-atomic values; if none is specified, the atomic object is initialized with zero.

Atomic objects can be accessed using the `[]` notation:

Examples

    julia> x = Threads.Atomic{Int}(3)
    Base.Threads.Atomic{Int64}(3)

    julia> x[] = 1
    1

    julia> x[]
    1

Atomic operations use an `atomic_` prefix, such as `atomic_add!`, `atomic_xchg!`, etc.

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`Base.Threads.atomic_cas!` — Function

    Threads.atomic_cas!(x::Atomic{T}, cmp::T, newval::T) where T

Atomically compare-and-set `x`

Atomically compares the value in `x` with `cmp`. If equal, write `newval` to `x`. Otherwise, leaves `x` unmodified. Returns the old value in `x`. By comparing the returned value to `cmp` (via `===`) one knows whether `x` was modified and now holds the new value `newval`.

For further details, see LLVM's `cmpxchg` instruction.

This function can be used to implement transactional semantics. Before the transaction, one records the value in `x`. After the transaction, the new value is stored only if `x` has not been modified in the mean time.

Examples

    julia> x = Threads.Atomic{Int}(3)
    Base.Threads.Atomic{Int64}(3)

    julia> Threads.atomic_cas!(x, 4, 2);

    julia> x
    Base.Threads.Atomic{Int64}(3)

    julia> Threads.atomic_cas!(x, 3, 2);

    julia> x
    Base.Threads.Atomic{Int64}(2)

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`Base.Threads.atomic_xchg!` — Function

    Threads.atomic_xchg!(x::Atomic{T}, newval::T) where T

Atomically exchange the value in `x`

Atomically exchanges the value in `x` with `newval`. Returns the old value.

For further details, see LLVM's `atomicrmw xchg` instruction.

Examples

    julia> x = Threads.Atomic{Int}(3)
    Base.Threads.Atomic{Int64}(3)

    julia> Threads.atomic_xchg!(x, 2)
    3

    julia> x[]
    2

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`Base.Threads.atomic_add!` — Function

    Threads.atomic_add!(x::Atomic{T}, val::T) where T <: ArithmeticTypes

Atomically add `val` to `x`

Performs `x[] += val` atomically. Returns the old value. Not defined for `Atomic{Bool}`.

For further details, see LLVM's `atomicrmw add` instruction.

Examples

    julia> x = Threads.Atomic{Int}(3)
    Base.Threads.Atomic{Int64}(3)

    julia> Threads.atomic_add!(x, 2)
    3

    julia> x[]
    5

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`Base.Threads.atomic_sub!` — Function

    Threads.atomic_sub!(x::Atomic{T}, val::T) where T <: ArithmeticTypes

Atomically subtract `val` from `x`

Performs `x[] -= val` atomically. Returns the old value. Not defined for `Atomic{Bool}`.

For further details, see LLVM's `atomicrmw sub` instruction.

Examples

    julia> x = Threads.Atomic{Int}(3)
    Base.Threads.Atomic{Int64}(3)

    julia> Threads.atomic_sub!(x, 2)
    3

    julia> x[]
    1

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`Base.Threads.atomic_and!` — Function

    Threads.atomic_and!(x::Atomic{T}, val::T) where T

Atomically bitwise-and `x` with `val`

Performs `x[] &= val` atomically. Returns the old value.

For further details, see LLVM's `atomicrmw and` instruction.

Examples

    julia> x = Threads.Atomic{Int}(3)
    Base.Threads.Atomic{Int64}(3)

    julia> Threads.atomic_and!(x, 2)
    3

    julia> x[]
    2

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`Base.Threads.atomic_nand!` — Function

    Threads.atomic_nand!(x::Atomic{T}, val::T) where T

Atomically bitwise-nand (not-and) `x` with `val`

Performs `x[] = ~(x[] & val)` atomically. Returns the old value.

For further details, see LLVM's `atomicrmw nand` instruction.

Examples

    julia> x = Threads.Atomic{Int}(3)
    Base.Threads.Atomic{Int64}(3)

    julia> Threads.atomic_nand!(x, 2)
    3

    julia> x[]
    -3

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`Base.Threads.atomic_or!` — Function

    Threads.atomic_or!(x::Atomic{T}, val::T) where T

Atomically bitwise-or `x` with `val`

Performs `x[] |= val` atomically. Returns the old value.

For further details, see LLVM's `atomicrmw or` instruction.

Examples

    julia> x = Threads.Atomic{Int}(5)
    Base.Threads.Atomic{Int64}(5)

    julia> Threads.atomic_or!(x, 7)
    5

    julia> x[]
    7

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`Base.Threads.atomic_xor!` — Function

    Threads.atomic_xor!(x::Atomic{T}, val::T) where T

Atomically bitwise-xor (exclusive-or) `x` with `val`

Performs `x[] $= val` atomically. Returns the old value.

For further details, see LLVM's `atomicrmw xor` instruction.

Examples

    julia> x = Threads.Atomic{Int}(5)
    Base.Threads.Atomic{Int64}(5)

    julia> Threads.atomic_xor!(x, 7)
    5

    julia> x[]
    2

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`Base.Threads.atomic_max!` — Function

    Threads.atomic_max!(x::Atomic{T}, val::T) where T

Atomically store the maximum of `x` and `val` in `x`

Performs `x[] = max(x[], val)` atomically. Returns the old value.

For further details, see LLVM's `atomicrmw max` instruction.

Examples

    julia> x = Threads.Atomic{Int}(5)
    Base.Threads.Atomic{Int64}(5)

    julia> Threads.atomic_max!(x, 7)
    5

    julia> x[]
    7

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`Base.Threads.atomic_min!` — Function

    Threads.atomic_min!(x::Atomic{T}, val::T) where T

Atomically store the minimum of `x` and `val` in `x`

Performs `x[] = min(x[], val)` atomically. Returns the old value.

For further details, see LLVM's `atomicrmw min` instruction.

Examples

    julia> x = Threads.Atomic{Int}(7)
    Base.Threads.Atomic{Int64}(7)

    julia> Threads.atomic_min!(x, 5)
    7

    julia> x[]
    5

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`Base.Threads.atomic_fence` — Function

    Threads.atomic_fence()

Insert a sequential-consistency memory fence

Inserts a memory fence with sequentially-consistent ordering semantics. There are algorithms where this is needed, i.e. where an acquire/release ordering is insufficient.

This is likely a very expensive operation. Given that all other atomic operations in Julia already have acquire/release semantics, explicit fences should not be necessary in most cases.

For further details, see LLVM's `fence` instruction.

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## ccall using a threadpool (Experimental)

`Base.@threadcall` — Macro

    @threadcall((cfunc, clib), rettype, (argtypes...), argvals...)

The `@threadcall` macro is called in the same way as `ccall` but does the work in a different thread. This is useful when you want to call a blocking C function without causing the main `julia` thread to become blocked. Concurrency is limited by size of the libuv thread pool, which defaults to 4 threads but can be increased by setting the `UV_THREADPOOL_SIZE` environment variable and restarting the `julia` process.

Note that the called function should never call back into Julia.

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## Low-level synchronization primitives

These building blocks are used to create the regular synchronization objects.

`Base.Threads.SpinLock` — Type

    SpinLock()

Create a non-reentrant, test-and-test-and-set spin lock. Recursive use will result in a deadlock. This kind of lock should only be used around code that takes little time to execute and does not block (e.g. perform I/O). In general, `ReentrantLock` should be used instead.

Each `lock` must be matched with an `unlock`.

Test-and-test-and-set spin locks are quickest up to about 30ish contending threads. If you have more contention than that, different synchronization approaches should be considered.

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Theme

This document was generated with Documenter.jl on Friday 24 September 2021. Using Julia version 1.6.3.