# Tasks and Parallel Computing

## Tasks

`Task`(func)

  Create a `Task` (i.e. coroutine) to execute the given function (which must be callable with no arguments). The task exits when this function returns.

`yieldto`(task, arg = nothing)

  Switch to the given task. The first time a task is switched to, the task’s function is called with no arguments. On subsequent switches, `arg` is returned from the task’s last call to `yieldto`. This is a low-level call that only switches tasks, not considering states or scheduling in any way. Its use is discouraged.

`current_task`()

  Get the currently running `Task`.

`istaskdone`(task) → Bool

  Determine whether a task has exited.

`istaskstarted`(task) → Bool

  Determine whether a task has started executing.

`consume`(task, values...)

  Receive the next value passed to `produce` by the specified task. Additional arguments may be passed, to be returned from the last `produce` call in the producer.

`produce`(value)

  Send the given value to the last `consume` call, switching to the consumer task. If the next `consume` call passes any values, they are returned by `produce`.

`yield`()

  Switch to the scheduler to allow another scheduled task to run. A task that calls this function is still runnable, and will be restarted immediately if there are no other runnable tasks.

`task_local_storage`(key)

  Look up the value of a key in the current task’s task-local storage.

`task_local_storage`(key, value)

  Assign a value to a key in the current task’s task-local storage.

`task_local_storage`(body, key, value)

  Call the function `body` with a modified task-local storage, in which `value` is assigned to `key`; the previous value of `key`, or lack thereof, is restored afterwards. Useful for emulating dynamic scoping.

`Condition`()

  Create an edge-triggered event source that tasks can wait for. Tasks that call `wait` on a `Condition` are suspended and queued. Tasks are woken up when `notify` is later called on the `Condition`. Edge triggering means that only tasks waiting at the time `notify` is called can be woken up. For level-triggered notifications, you must keep extra state to keep track of whether a notification has happened. The `Channel` type does this, and so can be used for level-triggered events.

`notify`(condition, val=nothing; all=true, error=false)

  Wake up tasks waiting for a condition, passing them `val`. If `all` is `true` (the default), all waiting tasks are woken, otherwise only one is. If `error` is `true`, the passed value is raised as an exception in the woken tasks.

`schedule`(t::Task, [val]; error=false)

  Add a task to the scheduler’s queue. This causes the task to run constantly when the system is otherwise idle, unless the task performs a blocking operation such as `wait`.

  If a second argument `val` is provided, it will be passed to the task (via the return value of `yieldto`) when it runs again. If `error` is `true`, the value is raised as an exception in the woken task.

`@schedule`()

  Wrap an expression in a `Task` and add it to the local machine’s scheduler queue.

`@task`()

  Wrap an expression in a `Task` without executing it, and return the `Task`. This only creates a task, and does not run it.

`sleep`(seconds)

  Block the current task for a specified number of seconds. The minimum sleep time is 1 millisecond or input of `0.001`.

`Channel{T}`(sz::Int)

  Constructs a `Channel` that can hold a maximum of `sz` objects of type `T`. `put!` calls on a full channel block till an object is removed with `take!`.

  Other constructors:

  - `Channel()` - equivalent to `Channel{Any}(32)`
  - `Channel(sz::Int)` equivalent to `Channel{Any}(sz)`

## General Parallel Computing Support

`addprocs`(np::Integer; restrict=true, kwargs...) → List of process identifiers

  Launches workers using the in-built `LocalManager` which only launches workers on the local host. This can be used to take advantage of multiple cores. `addprocs(4)` will add 4 processes on the local machine. If `restrict` is `true`, binding is restricted to `127.0.0.1`.

`addprocs`(; kwargs...) → List of process identifiers

  Equivalent to `addprocs(Sys.CPU_CORES; kwargs...)`

  Note that workers do not run a `.juliarc.jl` startup script, nor do they synchronize their global state (such as global variables, new method definitions, and loaded modules) with any of the other running processes.

`addprocs`(machines; tunnel=false, sshflags=``, max_parallel=10, kwargs...) → List of process identifiers

  Add processes on remote machines via SSH. Requires `julia` to be installed in the same location on each node, or to be available via a shared file system.

  `machines` is a vector of machine specifications. Workers are started for each specification.

  A machine specification is either a string `machine_spec` or a tuple - `(machine_spec, count)`.

  `machine_spec` is a string of the form `[user@]host[:port] [bind_addr[:port]]`. `user` defaults to current user, `port` to the standard ssh port. If `[bind_addr[:port]]` is specified, other workers will connect to this worker at the specified `bind_addr` and `port`.

  `count` is the number of workers to be launched on the specified host. If specified as `:auto` it will launch as many workers as the number of cores on the specific host.

  Keyword arguments:

  - `tunnel`: if `true` then SSH tunneling will be used to connect to the worker from the master process. Default is `false`.
  - `sshflags`: specifies additional ssh options, e.g. `sshflags=`-i /home/foo/bar.pem``
  - `max_parallel`: specifies the maximum number of workers connected to in parallel at a host. Defaults to 10.
  - `dir`: specifies the working directory on the workers. Defaults to the host’s current directory (as found by `pwd()`)
  - `exename`: name of the `julia` executable. Defaults to `"$JULIA_HOME/julia"` or `"$JULIA_HOME/julia-debug"` as the case may be.
  - `exeflags`: additional flags passed to the worker processes.
  - `topology`: Specifies how the workers connect to each other. Sending a message between unconnected workers results in an error.
    - `topology=:all_to_all` : All processes are connected to each other. This is the default.
    - `topology=:master_slave` : Only the driver process, i.e. `pid` 1 connects to the workers. The workers do not connect to each other.
    - `topology=:custom` : The `launch` method of the cluster manager specifies the connection topology via fields `ident` and `connect_idents` in `WorkerConfig`. A worker with a cluster manager identity `ident` will connect to all workers specified in `connect_idents`.

  Environment variables :

  If the master process fails to establish a connection with a newly launched worker within 60.0 seconds, the worker treats it as a fatal situation and terminates. This timeout can be controlled via environment variable `JULIA_WORKER_TIMEOUT`. The value of `JULIA_WORKER_TIMEOUT` on the master process specifies the number of seconds a newly launched worker waits for connection establishment.

`addprocs`(manager::ClusterManager; kwargs...) → List of process identifiers

  Launches worker processes via the specified cluster manager.

  For example Beowulf clusters are supported via a custom cluster manager implemented in the package `ClusterManagers.jl`.

  The number of seconds a newly launched worker waits for connection establishment from the master can be specified via variable `JULIA_WORKER_TIMEOUT` in the worker process’s environment. Relevant only when using TCP/IP as transport.

`nprocs`()

  Get the number of available processes.

`nworkers`()

  Get the number of available worker processes. This is one less than `nprocs()`. Equal to `nprocs()` if `nprocs() == 1`.

`procs`()

  Returns a list of all process identifiers.

`procs`(pid::Integer)

  Returns a list of all process identifiers on the same physical node. Specifically all workers bound to the same ip-address as `pid` are returned.

`workers`()

  Returns a list of all worker process identifiers.

`rmprocs`(pids...; waitfor=0.0)

  Removes the specified workers. Note that only process 1 can add or remove workers - if another worker tries to call `rmprocs`, an error will be thrown. The optional argument `waitfor` determines how long the first process will wait for the workers to shut down.

`interrupt`(pids::AbstractVector=workers())

  Interrupt the current executing task on the specified workers. This is equivalent to pressing Ctrl-C on the local machine. If no arguments are given, all workers are interrupted.

`interrupt`(pids::Integer...)

  Interrupt the current executing task on the specified workers. This is equivalent to pressing Ctrl-C on the local machine. If no arguments are given, all workers are interrupted.

`myid`()

  Get the id of the current process.

`asyncmap`(f, c...) → collection

  Transform collection `c` by applying `@async f` to each element.

  For multiple collection arguments, apply f elementwise.

`pmap`([::AbstractWorkerPool, ]f, c...; distributed=true, batch_size=1, on_error=nothing, retry_n=0, retry_max_delay=DEFAULT_RETRY_MAX_DELAY, retry_on=DEFAULT_RETRY_ON) → collection

  Transform collection `c` by applying `f` to each element using available workers and tasks.

  For multiple collection arguments, apply f elementwise.

  Note that `f` must be made available to all worker processes; see Code Availability and Loading Packages for details.

  If a worker pool is not specified, all available workers, i.e., the default worker pool is used.

  By default, `pmap` distributes the computation over all specified workers. To use only the local process and distribute over tasks, specify `distributed=false`. This is equivalent to `asyncmap`.

  `pmap` can also use a mix of processes and tasks via the `batch_size` argument. For batch sizes greater than 1, the collection is split into multiple batches, which are distributed across workers. Each such batch is processed in parallel via tasks in each worker. The specified `batch_size` is an upper limit, the actual size of batches may be smaller and is calculated depending on the number of workers available and length of the collection.

  Any error stops pmap from processing the remainder of the collection. To override this behavior you can specify an error handling function via argument `on_error` which takes in a single argument, i.e., the exception. The function can stop the processing by rethrowing the error, or, to continue, return any value which is then returned inline with the results to the caller.

  Failed computation can also be retried via `retry_on`, `retry_n`, `retry_max_delay`, which are passed through to `retry` as arguments `retry_on`, `n` and `max_delay` respectively. If batching is specified, and an entire batch fails, all items in the batch are retried.

  The following are equivalent:

  - `pmap(f, c; distributed=false)` and `asyncmap(f,c)`
  - `pmap(f, c; retry_n=1)` and `asyncmap(retry(remote(f)),c)`
  - `pmap(f, c; retry_n=1, on_error=e->e)` and `asyncmap(x->try retry(remote(f))(x) catch e; e end, c)`

`remotecall`(f, id::Integer, args...; kwargs...) → Future

  Call a function `f` asynchronously on the given arguments on the specified process. Returns a `Future`. Keyword arguments, if any, are passed through to `f`.

`Base.``process_messages`(r_stream::IO, w_stream::IO, incoming::Bool=true)

  Called by cluster managers using custom transports. It should be called when the custom transport implementation receives the first message from a remote worker. The custom transport must manage a logical connection to the remote worker and provide two `IO` objects, one for incoming messages and the other for messages addressed to the remote worker. If `incoming` is `true`, the remote peer initiated the connection. Whichever of the pair initiates the connection sends the cluster cookie and its Julia version number to perform the authentication handshake.

`RemoteException`(captured)

  Exceptions on remote computations are captured and rethrown locally. A `RemoteException` wraps the pid of the worker and a captured exception. A `CapturedException` captures the remote exception and a serializable form of the call stack when the exception was raised.

`Future`(pid::Integer=myid())

  Create a `Future` on process `pid`. The default `pid` is the current process.

`RemoteChannel`(pid::Integer=myid())

  Make a reference to a `Channel{Any}(1)` on process `pid`. The default `pid` is the current process.

`RemoteChannel`(f::Function, pid::Integer=myid())

  Create references to remote channels of a specific size and type. `f()` is a function that when executed on `pid` must return an implementation of an `AbstractChannel`.

  For example, `RemoteChannel(()->Channel{Int}(10), pid)`, will return a reference to a channel of type `Int` and size 10 on `pid`.

  The default `pid` is the current process.

`wait`([x])

  Block the current task until some event occurs, depending on the type of the argument:

  - `RemoteChannel` : Wait for a value to become available on the specified remote channel.
  - `Future` : Wait for a value to become available for the specified future.
  - `Channel`: Wait for a value to be appended to the channel.
  - `Condition`: Wait for `notify` on a condition.
  - `Process`: Wait for a process or process chain to exit. The `exitcode` field of a process can be used to determine success or failure.
  - `Task`: Wait for a `Task` to finish, returning its result value. If the task fails with an exception, the exception is propagated (re-thrown in the task that called `wait`).
  - `RawFD`: Wait for changes on a file descriptor (see `poll_fd` for keyword arguments and return code)

  If no argument is passed, the task blocks for an undefined period. A task can only be restarted by an explicit call to `schedule` or `yieldto`.

  Often `wait` is called within a `while` loop to ensure a waited-for condition is met before proceeding.

`fetch`(x)

  Waits and fetches a value from `x` depending on the type of `x`. Does not remove the item fetched:

  - `Future`: Wait for and get the value of a Future. The fetched value is cached locally. Further calls to `fetch` on the same reference return the cached value. If the remote value is an exception, throws a `RemoteException` which captures the remote exception and backtrace.
  - `RemoteChannel`: Wait for and get the value of a remote reference. Exceptions raised are same as for a `Future` .
  - `Channel` : Wait for and get the first available item from the channel.

`remotecall_wait`(f, id::Integer, args...; kwargs...)

  Perform a faster `wait(remotecall(...))` in one message on the `Worker` specified by worker id `id`. Keyword arguments, if any, are passed through to `f`.

`remotecall_fetch`(f, id::Integer, args...; kwargs...)

  Perform `fetch(remotecall(...))` in one message. Keyword arguments, if any, are passed through to `f`. Any remote exceptions are captured in a `RemoteException` and thrown.

`put!`(rr::RemoteChannel, args...)

  Store a set of values to the `RemoteChannel`. If the channel is full, blocks until space is available. Returns its first argument.

`put!`(rr::Future, v)

  Store a value to a `Future` `rr`. `Future`s are write-once remote references. A `put!` on an already set `Future` throws an `Exception`. All asynchronous remote calls return `Future`s and set the value to the return value of the call upon completion.

`put!`(c::Channel, v)

  Appends an item `v` to the channel `c`. Blocks if the channel is full.

`take!`(rr::RemoteChannel, args...)

  Fetch value(s) from a remote channel, removing the value(s) in the processs.

`take!`(c::Channel)

  Removes and returns a value from a `Channel`. Blocks till data is available.

`isready`(c::Channel)

  Determine whether a `Channel` has a value stored to it. `isready` on `Channel`s is non-blocking.

`isready`(rr::RemoteChannel, args...)

  Determine whether a `RemoteChannel` has a value stored to it. Note that this function can cause race conditions, since by the time you receive its result it may no longer be true. However, it can be safely used on a `Future` since they are assigned only once.

`isready`(rr::Future)

  Determine whether a `Future` has a value stored to it.

  If the argument `Future` is owned by a different node, this call will block to wait for the answer. It is recommended to wait for `rr` in a separate task instead or to use a local `Channel` as a proxy:

      c = Channel(1)
      @async put!(c, remotecall_fetch(long_computation, p))
      isready(c)  # will not block

`close`(c::Channel)

  Closes a channel. An exception is thrown by:

  - `put!` on a closed channel.
  - `take!` and `fetch` on an empty, closed channel.

`WorkerPool`(workers)

  Create a WorkerPool from a vector of worker ids.

`CachingPool`(workers::Vector{Int})

  An implementation of an `AbstractWorkerPool`. `remote`, `remotecall_fetch`, `pmap` and other remote calls which execute functions remotely, benefit from caching the serialized/deserialized functions on the worker nodes, especially for closures which capture large amounts of data.

  The remote cache is maintained for the lifetime of the returned `CachingPool` object. To clear the cache earlier, use `clear!(pool)`.

  For global variables, only the bindings are captured in a closure, not the data. `let` blocks can be used to capture global data.

  For example:

      const foo=rand(10^8);
      wp=CachingPool(workers())
      let foo=foo
          pmap(wp, i->sum(foo)+i, 1:100);
      end

  The above would transfer `foo` only once to each worker.

`default_worker_pool`()

  WorkerPool containing idle `workers()` (used by `remote(f)`).

`remote`([::AbstractWorkerPool, ]f) → Function

  Returns a lambda that executes function `f` on an available worker using `remotecall_fetch`.

`remotecall`(f, pool::AbstractWorkerPool, args...; kwargs...)

  Call `f(args...; kwargs...)` on one of the workers in `pool`. Returns a `Future`.

`remotecall_wait`(f, pool::AbstractWorkerPool, args...; kwargs...)

  Call `f(args...; kwargs...)` on one of the workers in `pool`. Waits for completion, returns a `Future`.

`remotecall_fetch`(f, pool::AbstractWorkerPool, args...; kwargs...)

  Call `f(args...; kwargs...)` on one of the workers in `pool`. Waits for completion and returns the result.

`timedwait`(testcb::Function, secs::Float64; pollint::Float64=0.1)

  Waits till `testcb` returns `true` or for `secs` seconds, whichever is earlier. `testcb` is polled every `pollint` seconds.

`@spawn`()

  Creates a closure around an expression and runs it on an automatically-chosen process, returning a `Future` to the result.

`@spawnat`()

  Accepts two arguments, `p` and an expression. A closure is created around the expression and run asynchronously on process `p`. Returns a `Future` to the result.

`@fetch`()

  Equivalent to `fetch(@spawn expr)`.

`@fetchfrom`()

  Equivalent to `fetch(@spawnat p expr)`.

`@async`()

  Like `@schedule`, `@async` wraps an expression in a `Task` and adds it to the local machine’s scheduler queue. Additionally it adds the task to the set of items that the nearest enclosing `@sync` waits for. `@async` also wraps the expression in a `let x=x, y=y, ...` block to create a new scope with copies of all variables referenced in the expression.

`@sync`()

  Wait until all dynamically-enclosed uses of `@async`, `@spawn`, `@spawnat` and `@parallel` are complete. All exceptions thrown by enclosed async operations are collected and thrown as a `CompositeException`.

`@parallel`()

  A parallel for loop of the form :

      @parallel [reducer] for var = range
          body
      end

  The specified range is partitioned and locally executed across all workers. In case an optional reducer function is specified, `@parallel` performs local reductions on each worker with a final reduction on the calling process.

  Note that without a reducer function, `@parallel` executes asynchronously, i.e. it spawns independent tasks on all available workers and returns immediately without waiting for completion. To wait for completion, prefix the call with `@sync`, like :

      @sync @parallel for var = range
          body
      end

`@everywhere expr`

  Execute an expression under Main everywhere. Equivalent to calling `eval(Main, expr)` on all processes. Errors on any of the processes are collected into a `CompositeException` and thrown. For example :

      @everywhere bar=1

  will define `Main.bar` on all processes.

  Unlike `@spawn` and `@spawnat`, `@everywhere` does not capture any local variables. Prefixing `@everywhere` with `@eval` allows us to broadcast local variables using interpolation :

      foo = 1
      @eval @everywhere bar=$foo

  The expression is evaluated under `Main` irrespective of where `@everywhere` is called from. For example :

      module FooBar
          foo() = @everywhere bar()=myid()
      end
      FooBar.foo()

  will result in `Main.bar` being defined on all processes and not `FooBar.bar`.

`clear!`(pool::CachingPool) → pool

  Removes all cached functions from all participating workers.

`Base.``remoteref_id`(r::AbstractRemoteRef) → RRID

  `Future`s and `RemoteChannel`s are identified by fields:

  `where` - refers to the node where the underlying object/storage referred to by the reference actually exists.

  `whence` - refers to the node the remote reference was created from. Note that this is different from the node where the underlying object referred to actually exists. For example calling `RemoteChannel(2)` from the master process would result in a `where` value of 2 and a `whence` value of 1.

  `id` is unique across all references created from the worker specified by `whence`.

  Taken together, `whence` and `id` uniquely identify a reference across all workers.

  `Base.remoteref_id` is a low-level API which returns a `Base.RRID` object that wraps `whence` and `id` values of a remote reference.

`Base.``channel_from_id`(id) → c

  A low-level API which returns the backing `AbstractChannel` for an `id` returned by `Base.remoteref_id()`. The call is valid only on the node where the backing channel exists.

`Base.``worker_id_from_socket`(s) → pid

  A low-level API which given a `IO` connection or a `Worker`, returns the `pid` of the worker it is connected to. This is useful when writing custom `serialize` methods for a type, which optimizes the data written out depending on the receiving process id.

`Base.``cluster_cookie`() → cookie

  Returns the cluster cookie.

`Base.``cluster_cookie`(cookie) → cookie

  Sets the passed cookie as the cluster cookie, then returns it.

## Shared Arrays

`SharedArray`(T::Type, dims::NTuple; init=false, pids=Int[])

  Construct a `SharedArray` of a bitstype `T` and size `dims` across the processes specified by `pids` - all of which have to be on the same host.

  If `pids` is left unspecified, the shared array will be mapped across all processes on the current host, including the master. But, `localindexes` and `indexpids` will only refer to worker processes. This facilitates work distribution code to use workers for actual computation with the master process acting as a driver.

  If an `init` function of the type `initfn(S::SharedArray)` is specified, it is called on all the participating workers.

`SharedArray`(filename::AbstractString, T::Type, dims::NTuple, [offset=0]; mode=nothing, init=false, pids=Int[])

  Construct a `SharedArray` backed by the file `filename`, with element type `T` (must be a `bitstype`) and size `dims`, across the processes specified by `pids` - all of which have to be on the same host. This file is mmapped into the host memory, with the following consequences:

  - The array data must be represented in binary format (e.g., an ASCII format like CSV cannot be supported)
  - Any changes you make to the array values (e.g., `A[3] = 0`) will also change the values on disk

  If `pids` is left unspecified, the shared array will be mapped across all processes on the current host, including the master. But, `localindexes` and `indexpids` will only refer to worker processes. This facilitates work distribution code to use workers for actual computation with the master process acting as a driver.

  `mode` must be one of `"r"`, `"r+"`, `"w+"`, or `"a+"`, and defaults to `"r+"` if the file specified by `filename` already exists, or `"w+"` if not. If an `init` function of the type `initfn(S::SharedArray)` is specified, it is called on all the participating workers. You cannot specify an `init` function if the file is not writable.

  `offset` allows you to skip the specified number of bytes at the beginning of the file.

`procs`(S::SharedArray)

  Get the vector of processes that have mapped the shared array.

`sdata`(S::SharedArray)

  Returns the actual `Array` object backing `S`.

`indexpids`(S::SharedArray)

  Returns the index of the current worker into the `pids` vector, i.e., the list of workers mapping the SharedArray

`localindexes`(S::SharedArray)

  Returns a range describing the “default” indexes to be handled by the current process. This range should be interpreted in the sense of linear indexing, i.e., as a sub-range of `1:length(S)`. In multi-process contexts, returns an empty range in the parent process (or any process for which `indexpids` returns 0).

  It’s worth emphasizing that `localindexes` exists purely as a convenience, and you can partition work on the array among workers any way you wish. For a SharedArray, all indexes should be equally fast for each worker process.

## Multi-Threading

This experimental interface supports Julia’s multi-threading capabilities. Types and function described here might (and likely will) change in the future.

`Threads.``threadid`()

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

`Threads.``nthreads`()

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

`Threads.``@threads`()

  A macro to parallelize a for-loop to run with multiple threads. This spawns `nthreads()` number of threads, splits the iteration space amongst them, and iterates in parallel. A barrier is placed at the end of the loop which waits for all the threads to finish execution, and the loop returns.

`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 bitstypes integer and float-point types. These are `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:

      x::Atomic{Int}
      x[] = 1
      val = x[]

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

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

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

`Threads.``atomic_add!{T}`(x::Atomic{T}, val::T)

  Atomically add `val` to `x`

  Performs `x[] += val` atomically. Returns the old (!) value.

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

`Threads.``atomic_sub!{T}`(x::Atomic{T}, val::T)

  Atomically subtract `val` from `x`

  Performs `x[] -= val` atomically. Returns the old (!) value.

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

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

  Atomically bitwise-and `x` with `val`

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

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

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

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

  Atomically bitwise-or `x` with `val`

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

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

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

`Threads.``atomic_max!{T}`(x::Atomic{T}, val::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 min` instruction.

`Threads.``atomic_min!{T}`(x::Atomic{T}, val::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 max` instruction.

`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.

## ccall using a threadpool (Experimental)

`@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.

## Synchronization Primitives

`AbstractLock`

  Abstract supertype describing types that implement the thread-safe synchronization primitives: `lock`, `trylock`, `unlock`, and `islocked`

`lock`(the_lock)

  Acquires the lock when it becomes available. If the lock is already locked by a different task/thread, it waits for it to become available.

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

`unlock`(the_lock)

  Releases ownership of the lock.

  If this is a recursive lock which has been acquired before, it just decrements an internal counter and returns immediately.

`trylock`(the_lock) → Success (Boolean)

  Acquires the lock if it is available, returning `true` if successful. If the lock is already locked by a different task/thread, returns `false`.

  Each successful `trylock` must be matched by an `unlock`.

`islocked`(the_lock) → Status (Boolean)

  Check whether the lock is held by any task/thread. This should not be used for synchronization (see instead `trylock`).

`ReentrantLock`()

  Creates a reentrant lock for synchronizing Tasks. The same task can acquire the lock as many times as required. Each `lock` must be matched with an `unlock`.

  This lock is NOT threadsafe. See `Threads.Mutex` for a threadsafe lock.

`Mutex`()

  These are standard system mutexes for locking critical sections of logic.

  On Windows, this is a critical section object, on pthreads, this is a `pthread_mutex_t`.

  See also SpinLock for a lighter-weight lock.

`SpinLock`()

  Creates a non-reentrant lock. Recursive use will result in a deadlock. 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, perhaps a lock is the wrong way to synchronize.

  See also RecursiveSpinLock for a version that permits recursion.

  See also Mutex for a more efficient version on one core or if the lock may be held for a considerable length of time.

`RecursiveSpinLock`()

  Creates a reentrant lock. The same thread can acquire the lock as many times as required. Each `lock` must be matched with an `unlock`.

  See also SpinLock for a slightly faster version.

  See also Mutex for a more efficient version on one core or if the lock may be held for a considerable length of time.

`Semaphore`(sem_size)

  Creates a counting semaphore that allows at most `sem_size` acquires to be in use at any time. Each acquire must be mached with a release.

  This construct is NOT threadsafe.

`acquire`(s::Semaphore)

  Wait for one of the `sem_size` permits to be available, blocking until one can be acquired.

`release`(s::Semaphore)

  Return one permit to the pool, possibly allowing another task to acquire it and resume execution.

## Cluster Manager Interface

This interface provides a mechanism to launch and manage Julia workers on different cluster environments. LocalManager, for launching additional workers on the same host and SSHManager, for launching on remote hosts via ssh are present in Base. TCP/IP sockets are used to connect and transport messages between processes. It is possible for Cluster Managers to provide a different transport.

`launch`(manager::ClusterManager, params::Dict, launched::Array, launch_ntfy::Condition)

  Implemented by cluster managers. For every Julia worker launched by this function, it should append a `WorkerConfig` entry to `launched` and notify `launch_ntfy`. The function MUST exit once all workers, requested by `manager` have been launched. `params` is a dictionary of all keyword arguments `addprocs` was called with.

`manage`(manager::ClusterManager, id::Integer, config::WorkerConfig. op::Symbol)

  Implemented by cluster managers. It is called on the master process, during a worker’s lifetime, with appropriate `op` values:

  - with `:register`/`:deregister` when a worker is added / removed from the Julia worker pool.
  - with `:interrupt` when `interrupt(workers)` is called. The `ClusterManager` should signal the appropriate worker with an interrupt signal.
  - with `:finalize` for cleanup purposes.

`kill`(manager::ClusterManager, pid::Int, config::WorkerConfig)

  Implemented by cluster managers. It is called on the master process, by `rmprocs`. It should cause the remote worker specified by `pid` to exit. `Base.kill(manager::ClusterManager.....)` executes a remote `exit()` on `pid`

`init_worker`(cookie::AbstractString, manager::ClusterManager=DefaultClusterManager())

  Called by cluster managers implementing custom transports. It initializes a newly launched process as a worker. Command line argument `--worker` has the effect of initializing a process as a worker using TCP/IP sockets for transport. `cookie` is a `cluster_cookie()`.

`connect`(manager::ClusterManager, pid::Int, config::WorkerConfig) → (instrm::IO, outstrm::IO)

  Implemented by cluster managers using custom transports. It should establish a logical connection to worker with id `pid`, specified by `config` and return a pair of `IO` objects. Messages from `pid` to current process will be read off `instrm`, while messages to be sent to `pid` will be written to `outstrm`. The custom transport implementation must ensure that messages are delivered and received completely and in order. `Base.connect(manager::ClusterManager.....)` sets up TCP/IP socket connections in-between workers.