Tasks
Core.Task
— Type.
Task(func)
Create a Task
(i.e. coroutine) to execute the given function func
(which must be callable with no arguments). The task exits when this function returns.
Examples
julia> a() = sum(i for i in 1:1000);
julia> b = Task(a);
In this example, b
is a runnable Task
that hasn’t started yet.
Base.current_task
— Function.
current_task()
Get the currently running Task
.
Base.istaskdone
— Function.
istaskdone(t::Task) -> Bool
Determine whether a task has exited.
Examples
julia> a2() = sum(i for i in 1:1000);
julia> b = Task(a2);
julia> istaskdone(b)
false
julia> schedule(b);
julia> yield();
julia> istaskdone(b)
true
Base.istaskstarted
— Function.
istaskstarted(t::Task) -> Bool
Determine whether a task has started executing.
Examples
julia> a3() = sum(i for i in 1:1000);
julia> b = Task(a3);
julia> istaskstarted(b)
false
Base.yield
— Function.
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.
yield(t::Task, arg = nothing)
A fast, unfair-scheduling version of schedule(t, arg); yield()
which immediately yields to t
before calling the scheduler.
Base.yieldto
— Function.
yieldto(t::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.
Base.task_local_storage
— Method.
task_local_storage(key)
Look up the value of a key in the current task’s task-local storage.
Base.task_local_storage
— Method.
task_local_storage(key, value)
Assign a value to a key in the current task’s task-local storage.
Base.task_local_storage
— Method.
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.
Base.Condition
— Type.
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.
Base.notify
— Function.
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.
Return the count of tasks woken up. Return 0 if no tasks are waiting on condition
.
Base.schedule
— Function.
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.
Examples
julia> a5() = sum(i for i in 1:1000);
julia> b = Task(a5);
julia> istaskstarted(b)
false
julia> schedule(b);
julia> yield();
julia> istaskstarted(b)
true
julia> istaskdone(b)
true
Base.@task
— Macro.
@task
Wrap an expression in a Task
without executing it, and return the Task
. This only creates a task, and does not run it.
Examples
julia> a1() = sum(i for i in 1:1000);
julia> b = @task a1();
julia> istaskstarted(b)
false
julia> schedule(b);
julia> yield();
julia> istaskdone(b)
true
Base.sleep
— Function.
sleep(seconds)
Block the current task for a specified number of seconds. The minimum sleep time is 1 millisecond or input of 0.001
.
Base.Channel
— Type.
Channel{T}(sz::Int)
Constructs a Channel
with an internal buffer that can hold a maximum of sz
objects of type T
. put!
calls on a full channel block until an object is removed with take!
.
Channel(0)
constructs an unbuffered channel. put!
blocks until a matching take!
is called. And vice-versa.
Other constructors:
Channel(Inf)
: equivalent toChannel{Any}(typemax(Int))
Channel(sz)
: equivalent toChannel{Any}(sz)
Base.put!
— Method.
put!(c::Channel, v)
Append an item v
to the channel c
. Blocks if the channel is full.
For unbuffered channels, blocks until a take!
is performed by a different task.
Julia 1.1
v
now gets converted to the channel’s type with convert
as put!
is called.
Base.take!
— Method.
take!(c::Channel)
Remove and return a value from a Channel
. Blocks until data is available.
For unbuffered channels, blocks until a put!
is performed by a different task.
Base.isready
— Method.
isready(c::Channel)
Determine whether a Channel
has a value stored to it. Returns immediately, does not block.
For unbuffered channels returns true
if there are tasks waiting on a put!
.
Base.fetch
— Method.
fetch(c::Channel)
Wait for and get the first available item from the channel. Does not remove the item. fetch
is unsupported on an unbuffered (0-size) channel.
Base.close
— Method.
close(c::Channel)
Close a channel. An exception is thrown by:
Base.bind
— Method.
bind(chnl::Channel, task::Task)
Associate the lifetime of chnl
with a task. Channel
chnl
is automatically closed when the task terminates. Any uncaught exception in the task is propagated to all waiters on chnl
.
The chnl
object can be explicitly closed independent of task termination. Terminating tasks have no effect on already closed Channel
objects.
When a channel is bound to multiple tasks, the first task to terminate will close the channel. When multiple channels are bound to the same task, termination of the task will close all of the bound channels.
Examples
julia> c = Channel(0);
julia> task = @async foreach(i->put!(c, i), 1:4);
julia> bind(c,task);
julia> for i in c
@show i
end;
i = 1
i = 2
i = 3
i = 4
julia> isopen(c)
false
julia> c = Channel(0);
julia> task = @async (put!(c,1);error("foo"));
julia> bind(c,task);
julia> take!(c)
1
julia> put!(c,1);
ERROR: foo
Stacktrace:
[...]
Base.asyncmap
— Function.
asyncmap(f, c...; ntasks=0, batch_size=nothing)
Uses multiple concurrent tasks to map f
over a collection (or multiple equal length collections). For multiple collection arguments, f
is applied elementwise.
ntasks
specifies the number of tasks to run concurrently. Depending on the length of the collections, if ntasks
is unspecified, up to 100 tasks will be used for concurrent mapping.
ntasks
can also be specified as a zero-arg function. In this case, the number of tasks to run in parallel is checked before processing every element and a new task started if the value of ntasks_func
is less than the current number of tasks.
If batch_size
is specified, the collection is processed in batch mode. f
must then be a function that must accept a Vector
of argument tuples and must return a vector of results. The input vector will have a length of batch_size
or less.
The following examples highlight execution in different tasks by returning the objectid
of the tasks in which the mapping function is executed.
First, with ntasks
undefined, each element is processed in a different task.
julia> tskoid() = objectid(current_task());
julia> asyncmap(x->tskoid(), 1:5)
5-element Array{UInt64,1}:
0x6e15e66c75c75853
0x440f8819a1baa682
0x9fb3eeadd0c83985
0xebd3e35fe90d4050
0x29efc93edce2b961
julia> length(unique(asyncmap(x->tskoid(), 1:5)))
5
With ntasks=2
all elements are processed in 2 tasks.
julia> asyncmap(x->tskoid(), 1:5; ntasks=2)
5-element Array{UInt64,1}:
0x027ab1680df7ae94
0xa23d2f80cd7cf157
0x027ab1680df7ae94
0xa23d2f80cd7cf157
0x027ab1680df7ae94
julia> length(unique(asyncmap(x->tskoid(), 1:5; ntasks=2)))
2
With batch_size
defined, the mapping function needs to be changed to accept an array of argument tuples and return an array of results. map
is used in the modified mapping function to achieve this.
julia> batch_func(input) = map(x->string("args_tuple: ", x, ", element_val: ", x[1], ", task: ", tskoid()), input)
batch_func (generic function with 1 method)
julia> asyncmap(batch_func, 1:5; ntasks=2, batch_size=2)
5-element Array{String,1}:
"args_tuple: (1,), element_val: 1, task: 9118321258196414413"
"args_tuple: (2,), element_val: 2, task: 4904288162898683522"
"args_tuple: (3,), element_val: 3, task: 9118321258196414413"
"args_tuple: (4,), element_val: 4, task: 4904288162898683522"
"args_tuple: (5,), element_val: 5, task: 9118321258196414413"
Note
Currently, all tasks in Julia are executed in a single OS thread co-operatively. Consequently, asyncmap
is beneficial only when the mapping function involves any I/O - disk, network, remote worker invocation, etc.
Base.asyncmap!
— Function.
asyncmap!(f, results, c...; ntasks=0, batch_size=nothing)
Like asyncmap
, but stores output in results
rather than returning a collection.