Tasks and Parallel Computing¶

Tasks¶

Task(func)¶: Create a Task (i.e. thread, or coroutine) to execute the given function (which must be callable with no arguments). The task exits when this function returns.

yieldto(task, args...)¶: 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, args are 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.

current_task()¶: Get the currently running Task.

istaskdone(task) → Bool¶: Tell whether a task has exited.

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(symbol)¶: Look up the value of a symbol in the current task’s task-local storage.

task_local_storage(symbol, value): Assign a value to a symbol in the current task’s task-local storage.

task_local_storage(body, symbol, value): Call the function body with a modified task-local storage, in which value is assigned to symbol; the previous value of symbol, 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 RemoteRef 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 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 scheduler’s queue.

@task()¶: Wrap an expression in a Task 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.

General Parallel Computing Support¶

addprocs(n; cman::ClusterManager=LocalManager()) → List of process identifiers¶

addprocs(4) will add 4 processes on the local machine. This can be used to take advantage of multiple cores.

Keyword argument cman can be used to provide a custom cluster manager to start workers. For example Beowulf clusters are supported via a custom cluster manager implemented in package ClusterManagers.

See the documentation for package ClusterManagers for more information on how to write a custom cluster manager.

addprocs(machines; tunnel=false, dir=JULIA_HOME, sshflags::Cmd=``) → 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 host definitions of the form [user@]host[:port] [bind_addr]. user defaults to current user, port to the standard ssh port. Optionally, in case of multi-homed hosts, bind_addr may be used to explicitly specify an interface.

Keyword arguments:

tunnel : if true then SSH tunneling will be used to connect to the worker.

dir : specifies the location of the julia binaries on the worker nodes.

sshflags : specifies additional ssh options, e.g. sshflags=`-i /home/foo/bar.pem` .

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.

workers()¶: Returns a list of all worker process identifiers.

rmprocs(pids...)¶: Removes the specified workers.

interrupt([pids...])¶: 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.

pmap(f, lsts...; err_retry=true, err_stop=false)¶

Transform collections lsts by applying f to each element in parallel. If nprocs() > 1, the calling process will be dedicated to assigning tasks. All other available processes will be used as parallel workers.

If err_retry is true, it retries a failed application of f on a different worker. If err_stop is true, it takes precedence over the value of err_retry and pmap stops execution on the first error.

remotecall(id, func, args...)¶: Call a function asynchronously on the given arguments on the specified process. Returns a RemoteRef.

wait([x])¶

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

RemoteRef: Wait for a value to become available for the specified remote reference.
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.
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. If the task’s state is set to :waiting, it can only be restarted by an explicit call to schedule or yieldto. If the task’s state is :runnable, it might be restarted unpredictably.

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

fetch(RemoteRef)¶: Wait for and get the value of a remote reference.

remotecall_wait(id, func, args...)¶: Perform wait(remotecall(...)) in one message.

remotecall_fetch(id, func, args...)¶: Perform fetch(remotecall(...)) in one message.

put!(RemoteRef, value)¶: Store a value to a remote reference. Implements “shared queue of length 1” semantics: if a value is already present, blocks until the value is removed with take!. Returns its first argument.

take!(RemoteRef)¶: Fetch the value of a remote reference, removing it so that the reference is empty again.

isready(r::RemoteRef)¶

Determine whether a RemoteRef 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. It is recommended that this function only be used on a RemoteRef that is assigned once.

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

rr = RemoteRef()
@async put!(rr, remotecall_fetch(p, long_computation))
isready(rr)  # will not block

RemoteRef()¶: Make an uninitialized remote reference on the local machine.

RemoteRef(n): Make an uninitialized remote reference on process n.

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()¶: Execute an expression on an automatically-chosen process, returning a RemoteRef to the result.

@spawnat()¶: Accepts two arguments, p and an expression, and runs the expression asynchronously on process p, returning a RemoteRef to the result.

@fetch()¶: Equivalent to fetch(@spawn expr).

@fetchfrom()¶: Equivalent to fetch(@spawnat p expr).

@async()¶: Schedule an expression to run on the local machine, also adding it to the set of items that the nearest enclosing @sync waits for.

@sync()¶: Wait until all dynamically-enclosed uses of @async, @spawn, @spawnat and @parallel are complete.

@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

Distributed Arrays¶

DArray(init, dims[, procs, dist])¶

Construct a distributed array. The parameter init is a function that accepts a tuple of index ranges. This function should allocate a local chunk of the distributed array and initialize it for the specified indices. dims is the overall size of the distributed array. procs optionally specifies a vector of process IDs to use. If unspecified, the array is distributed over all worker processes only. Typically, when runnning in distributed mode, i.e., nprocs() > 1, this would mean that no chunk of the distributed array exists on the process hosting the interactive julia prompt. dist is an integer vector specifying how many chunks the distributed array should be divided into in each dimension.

For example, the dfill function that creates a distributed array and fills it with a value v is implemented as:

dfill(v, args...) = DArray(I->fill(v, map(length,I)), args...)

dzeros(dims, ...)¶: Construct a distributed array of zeros. Trailing arguments are the same as those accepted by DArray().

dones(dims, ...)¶: Construct a distributed array of ones. Trailing arguments are the same as those accepted by DArray().

dfill(x, dims, ...)¶: Construct a distributed array filled with value x. Trailing arguments are the same as those accepted by DArray().

drand(dims, ...)¶: Construct a distributed uniform random array. Trailing arguments are the same as those accepted by DArray().

drandn(dims, ...)¶: Construct a distributed normal random array. Trailing arguments are the same as those accepted by DArray().

distribute(a)¶: Convert a local array to distributed.

localpart(d)¶: Get the local piece of a distributed array. Returns an empty array if no local part exists on the calling process.

localindexes(d)¶: A tuple describing the indexes owned by the local process. Returns a tuple with empty ranges if no local part exists on the calling process.

procs(d): Get the vector of processes storing pieces of d.

Shared Arrays (Experimental, UNIX-only feature)¶

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 workers on the current host.

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

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