# [Multi-Threading](@id man-multithreading)

Visit this [blog post](https://julialang.org/blog/2019/07/multithreading/) for a presentation
of Julia multi-threading features.

## Starting Julia with multiple threads

By default, Julia starts up with a single thread of execution. This can be verified by using the
command [`Threads.nthreads()`](@ref):

```jldoctest
julia> Threads.nthreads()
1
```

The number of execution threads is controlled either by using the
`-t`/`--threads` command line argument or by using the
[`JULIA_NUM_THREADS`](@ref 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](@ref command-line-interface) for more details).

!!! compat "Julia 1.5"
    The `-t`/`--threads` command line argument requires at least Julia 1.5.
    In older versions you must use the environment variable instead.

!!! compat "Julia 1.7"
    Using `auto` as value of the environment variable [`JULIA_NUM_THREADS`](@ref JULIA_NUM_THREADS) requires at least Julia 1.7.
    In older versions, this value is ignored.
Lets start Julia with 4 threads:

```bash
$ julia --threads 4
```

Let's verify there are 4 threads at our disposal.

```julia-repl
julia> Threads.nthreads()
4
```

But we are currently on the master thread. To check, we use the function [`Threads.threadid`](@ref)

```jldoctest
julia> Threads.threadid()
1
```

!!! note
    If you prefer to use the environment variable you can set it as follows in
    Bash (Linux/macOS):
    ```bash
    export JULIA_NUM_THREADS=4
    ```
    C shell on Linux/macOS, CMD on Windows:
    ```bash
    set JULIA_NUM_THREADS=4
    ```
    Powershell on Windows:
    ```powershell
    $env:JULIA_NUM_THREADS=4
    ```
    Note that this must be done *before* starting Julia.

!!! note
    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`](@ref) and pass `-t`/`--threads` as `exeflags`.

### Multiple GC Threads

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`](@ref JULIA_NUM_GC_THREADS) environment variable.

!!! compat "Julia 1.10"
    The `--gcthreads` command line argument requires at least Julia 1.10.

## [Threadpools](@id man-threadpools)

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`](@ref) it:

```julia
using Base.Threads
@spawn :interactive f()
```

Interactive tasks should avoid performing high latency operations, and if they
are long duration tasks, should yield frequently.

Julia may be started with one or more threads reserved to run interactive tasks:

```bash
$ julia --threads 3,1
```

The environment variable [`JULIA_NUM_THREADS`](@ref JULIA_NUM_THREADS) can also be used similarly:
```bash
export JULIA_NUM_THREADS=3,1
```

This starts Julia with 3 threads in the `:default` threadpool and 1 thread in
the `:interactive` threadpool:

```julia-repl
julia> using Base.Threads

julia> nthreadpools()
2

julia> threadpool() # the main thread is in the interactive thread pool
:interactive

julia> nthreads(:default)
3

julia> nthreads(:interactive)
1

julia> nthreads()
3
```

!!! note
    The zero-argument version of `nthreads` returns the number of threads
    in the default pool.

!!! note
    Depending on whether Julia has been started with interactive threads,
    the main thread is either in the default or interactive thread pool.

Either or both numbers can be replaced with the word `auto`, which causes
Julia to choose a reasonable default.

## The `@threads` Macro

Let's work a simple example using our native threads. Let us create an array of zeros:

```jldoctest
julia> a = zeros(10)
10-element Vector{Float64}:
 0.0
 0.0
 0.0
 0.0
 0.0
 0.0
 0.0
 0.0
 0.0
 0.0
```

Let us operate on this array simultaneously using 4 threads. We'll have each thread write its
thread ID into each location.

Julia supports parallel loops using the [`Threads.@threads`](@ref) macro. This macro is affixed
in front of a `for` loop to indicate to Julia that the loop is a multi-threaded region:

```julia-repl
julia> Threads.@threads for i = 1:10
           a[i] = Threads.threadid()
       end
```

The iteration space is split among the threads, after which each thread writes its thread ID
to its assigned locations:

```julia-repl
julia> a
10-element Vector{Float64}:
 1.0
 1.0
 1.0
 2.0
 2.0
 2.0
 3.0
 3.0
 4.0
 4.0
```

Note that [`Threads.@threads`](@ref) does not have an optional reduction parameter like [`@distributed`](@ref).

### Using `@threads` without data-races

The concept of a data-race is elaborated on in ["Communication and data races between threads"](@ref man-communication-and-data-races). For now, just known that a data race can result in incorrect results and dangerous errors.

Lets say we want to make the function `sum_single` below multithreaded.
```julia-repl
julia> function sum_single(a)
           s = 0
           for i in a
               s += i
           end
           s
       end
sum_single (generic function with 1 method)

julia> sum_single(1:1_000_000)
500000500000
```

Simply adding `@threads` exposes a data race with multiple threads reading and writing `s` at the same time.
```julia-repl
julia> function sum_multi_bad(a)
           s = 0
           Threads.@threads for i in a
               s += i
           end
           s
       end
sum_multi_bad (generic function with 1 method)

julia> sum_multi_bad(1:1_000_000)
70140554652
```

Note that the result is not `500000500000` as it should be, and will most likely change each evaluation.

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`. The input vector `a` is split into at most `nthreads()`
chunks for parallel work. We then use `Threads.@spawn` to create tasks that individually sum each chunk. Finally, we sum the results from each task using `sum_single` again:
```julia-repl
julia> function sum_multi_good(a)
           chunks = Iterators.partition(a, cld(length(a), Threads.nthreads()))
           tasks = map(chunks) do chunk
               Threads.@spawn sum_single(chunk)
           end
           chunk_sums = fetch.(tasks)
           return sum_single(chunk_sums)
       end
sum_multi_good (generic function with 1 method)

julia> sum_multi_good(1:1_000_000)
500000500000
```
!!! note
    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](@ref man-task-migration).

Another option is the use of atomic operations on variables shared across tasks/threads, which may be more performant
depending on the characteristics of the operations.

## [Communication and data-races between threads](@id man-communication-and-data-races)

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
[`Channel`](@ref)s are thread-safe and may be used to communicate safely. There are also sections below that explain how to use [locks](@ref man-using-locks) and [atomics](@ref man-atomic-operations) to avoid data-races.

In certain cases, Julia is able to detect a detect safety violations, in particular in regards to deadlocks or other known-unsafe operations such as yielding
to the currently running task. In these cases, a [`ConcurrencyViolationError`](@ref) is thrown.

### Data-race freedom

You are entirely responsible for ensuring that your program is data-race free,
and nothing promised here can be assumed if you do not observe that
requirement. The observed results may be highly unintuitive.

If data-races are introduced, Julia is not memory safe. **Be very
careful about reading _any_ data if another thread might write to it, as it could result in segmentation faults or worse**. Below are a couple of unsafe ways to access global variables from different threads:
```julia
Thread 1:
global b = false
global a = rand()
global b = true

Thread 2:
while !b; end
bad_read1(a) # it is NOT safe to access `a` here!

Thread 3:
while !@isdefined(a); end
bad_read2(a) # it is NOT safe to access `a` here
```

### [Using locks to avoid data-races](@id man-using-locks)
An important tool to avoid data-races, and thereby write thread-safe code, is the concept of a "lock". A lock can be locked and unlocked. If a thread has locked a lock, and not unlocked it, it is said to "hold" the lock. If there is only one lock, and we write code the requires holding the lock to access some data, we can ensure that multiple threads will never access the same data simultaneously. Note that the link between a lock and a variable is made by the programmer, and not the program.

For example, we can create a lock `my_lock`, and lock it while we mutate a variable `my_variable`. This is done most simply with the `@lock` macro:

```julia-repl
julia> my_lock = ReentrantLock();

julia> my_variable = [1, 2, 3];

julia> @lock my_lock my_variable[1] = 100
100
```

By using a similar pattern with the same lock and variable, but on another thread, the operations are free from data-races.

We could have performed the operation above with the functional version of `lock`, in the following two ways:
```julia-repl
julia> lock(my_lock) do
           my_variable[1] = 100
       end
100

julia> begin
           lock(my_lock)
           try
               my_variable[1] = 100
           finally
               unlock(my_lock)
           end
       end
100
```

All three options are equivalent. Note how the final version requires an explicit `try`-block to ensure that the lock is always unlocked, whereas the first two version do this internally. One should always use the lock pattern above when changing data (such as assigning
to a global or closure variable) accessed by other threads. Failing to do this could have unforeseen and serious consequences.

### [Atomic Operations](@id man-atomic-operations)

Julia supports accessing and modifying values *atomically*, that is, in a thread-safe way to avoid
[race conditions](https://en.wikipedia.org/wiki/Race_condition). A value (which must be of a primitive
type) can be wrapped as [`Threads.Atomic`](@ref) to indicate it must be accessed in this way.
Here we can see an example:

```julia-repl
julia> i = Threads.Atomic{Int}(0);

julia> ids = zeros(4);

julia> old_is = zeros(4);

julia> Threads.@threads for id in 1:4
           old_is[id] = Threads.atomic_add!(i, id)
           ids[id] = id
       end

julia> old_is
4-element Vector{Float64}:
 0.0
 1.0
 7.0
 3.0

julia> i[]
 10

julia> ids
4-element Vector{Float64}:
 1.0
 2.0
 3.0
 4.0
```

Had we tried to do the addition without the atomic tag, we might have gotten the
wrong answer due to a race condition. An example of what would happen if we didn't
avoid the race:

```julia-repl
julia> using Base.Threads

julia> Threads.nthreads()
4

julia> acc = Ref(0)
Base.RefValue{Int64}(0)

julia> @threads for i in 1:1000
          acc[] += 1
       end

julia> acc[]
926

julia> acc = Atomic{Int64}(0)
Atomic{Int64}(0)

julia> @threads for i in 1:1000
          atomic_add!(acc, 1)
       end

julia> acc[]
1000
```


#### [Per-field atomics](@id man-atomics)

We can also use atomics on a more granular level using the [`@atomic`](@ref
Base.@atomic), [`@atomicswap`](@ref Base.@atomicswap),
[`@atomicreplace`](@ref Base.@atomicreplace) macros, and
[`@atomiconce`](@ref Base.@atomiconce) macros.

Specific details of the memory model and other details of the design are written
in the [Julia Atomics
Manifesto](https://gist.github.com/vtjnash/11b0031f2e2a66c9c24d33e810b34ec0),
which will later be published formally.

Any field in a struct declaration can be decorated with `@atomic`, and then any
write must be marked with `@atomic` also, and must use one of the defined atomic
orderings (`:monotonic`, `:acquire`, `:release`, `:acquire_release`, or
`:sequentially_consistent`). Any read of an atomic field can also be annotated
with an atomic ordering constraint, or will be done with monotonic (relaxed)
ordering if unspecified.

!!! compat "Julia 1.7"
    Per-field atomics requires at least Julia 1.7.


## Side effects and mutable function arguments

When using multi-threading we have to be careful when using functions that are not
[pure](https://en.wikipedia.org/wiki/Pure_function) as we might get a wrong answer.
For instance functions that have a
[name ending with `!`](@ref bang-convention)
by convention modify their arguments and thus are not pure.


## @threadcall

External libraries, such as those called via [`ccall`](@ref), pose a problem for
Julia's task-based I/O mechanism.
If a C library performs a blocking operation, that prevents the Julia scheduler
from executing any other tasks until the call returns.
(Exceptions are calls into custom C code that call back into Julia, which may then
yield, or C code that calls `jl_yield()`, the C equivalent of [`yield`](@ref).)

The [`@threadcall`](@ref) macro provides a way to avoid stalling execution in such
a scenario.
It schedules a C function for execution in a separate thread. A threadpool with a
default size of 4 is used for this. The size of the threadpool is controlled via environment variable
`UV_THREADPOOL_SIZE`. While waiting for a free thread, and during function execution once a thread
is available, the requesting task (on the main Julia event loop) yields to other tasks. Note that
`@threadcall` does not return until the execution is complete. From a user point of view, it is
therefore a blocking call like other Julia APIs.

It is very important that the called function does not call back into Julia, as it will segfault.

`@threadcall` may be removed/changed in future versions of Julia.

## Caveats

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.

There are a few specific limitations and warnings to be aware of when using
threads in Julia:

  * Base collection types require manual locking if used simultaneously by
    multiple threads where at least one thread modifies the collection
    (common examples include `push!` on arrays, or inserting
    items into a `Dict`).
  * The schedule used by [`@spawn`](@ref Threads.@spawn) is nondeterministic and should not be relied on.
  * Compute-bound, non-memory-allocating tasks can prevent garbage collection from
    running in other threads that are allocating memory. In these cases it may
    be necessary to insert a manual call to `GC.safepoint()` to allow GC to run.
    This limitation will be removed in the future.
  * Avoid running top-level operations, e.g. `include`, or `eval` of type,
    method, and module definitions in parallel.
  * Be aware that finalizers registered by a library may break if threads are enabled.
    This may require some transitional work across the ecosystem before threading
    can be widely adopted with confidence. See the section on
    [the safe use of finalizers](@ref man-finalizers) for further details.

## [Task Migration](@id man-task-migration)

After a task starts running on a certain thread it may move to a different thread if the task yields.

Such tasks may have been started with [`@spawn`](@ref Threads.@spawn) or [`@threads`](@ref Threads.@threads),
although the `:static` schedule option for `@threads` does freeze the threadid.

This means that in most cases [`threadid()`](@ref Threads.threadid) should not be treated as constant within a task,
and therefore should not be used to index into a vector of buffers or stateful objects.

!!! compat "Julia 1.7"
    Task migration was introduced in Julia 1.7. Before this tasks always remained on the same thread that they were
    started on.

## [Safe use of Finalizers](@id man-finalizers)

Because finalizers can interrupt any code, they must be very careful in how
they interact with any global state. Unfortunately, the main reason that
finalizers are used is to update global state (a pure function is generally
rather pointless as a finalizer). This leads us to a bit of a conundrum.
There are a few approaches to dealing with this problem:

1. When single-threaded, code could call the internal `jl_gc_enable_finalizers`
   C function to prevent finalizers from being scheduled
   inside a critical region. Internally, this is used inside some functions (such
   as our C locks) to prevent recursion when doing certain operations (incremental
   package loading, codegen, etc.). The combination of a lock and this flag
   can be used to make finalizers safe.

2. A second strategy, employed by Base in a couple places, is to explicitly
   delay a finalizer until it may be able to acquire its lock non-recursively.
   The following example demonstrates how this strategy could be applied to
   `Distributed.finalize_ref`:

   ```julia
   function finalize_ref(r::AbstractRemoteRef)
       if r.where > 0 # Check if the finalizer is already run
           if islocked(client_refs) || !trylock(client_refs)
               # delay finalizer for later if we aren't free to acquire the lock
               finalizer(finalize_ref, r)
               return nothing
           end
           try # `lock` should always be followed by `try`
               if r.where > 0 # Must check again here
                   # Do actual cleanup here
                   r.where = 0
               end
           finally
               unlock(client_refs)
           end
       end
       nothing
   end
   ```

3. A related third strategy is to use a yield-free queue. We don't currently
   have a lock-free queue implemented in Base, but
   `Base.IntrusiveLinkedListSynchronized{T}` is suitable. This can frequently be a
   good strategy to use for code with event loops. For example, this strategy is
   employed by `Gtk.jl` to manage lifetime ref-counting. In this approach, we
   don't do any explicit work inside the `finalizer`, and instead add it to a queue
   to run at a safer time. In fact, Julia's task scheduler already uses this, so
   defining the finalizer as `x -> @spawn do_cleanup(x)` is one example of this
   approach. Note however that this doesn't control which thread `do_cleanup`
   runs on, so `do_cleanup` would still need to acquire a lock. That
   doesn't need to be true if you implement your own queue, as you can explicitly
   only drain that queue from your thread.
