A framework for out-of-core and parallel execution


The main function for using Dagger is spawn:

Dagger.spawn(f, args...; options...)

or @spawn for the more convenient macro form:

Dagger.@spawn [option=value]... f(args...)

When called, it creates an EagerThunk (also known as a "thunk" or "task") object representing a call to function f with the arguments args. If it is called with other thunks as inputs, such as in Dagger.@spawn f(Dagger.@spawn g()), then the function f gets passed the results of those input thunks. If those thunks aren't yet finished executing, then the execution of f waits on all of its input thunks to complete before executing.

The key point is that, for each argument to a thunk, if the argument is an EagerThunk, it'll be executed before this node and its result will be passed into the function f. If the argument is not an EagerThunk (instead, some other type of Julia object), it'll be passed as-is to the function f.

Thunks don't accept regular keyword arguments for the function f. Instead, the options kwargs are passed to the scheduler to control its behavior:

  • Any field in Dagger.Sch.ThunkOptions (see Scheduler and Thunk options)
  • meta::Bool – Pass the input Chunk objects themselves to f and not the value contained in them

There are also some extra kwargs that can be passed, although they're considered advanced options to be used only by developers or library authors:

  • get_result::Bool – return the actual result to the scheduler instead of Chunk objects. Used when f explicitly constructs a Chunk or when return value is small (e.g. in case of reduce)
  • persist::Bool – the result of this Thunk should not be released after it becomes unused in the DAG
  • cache::Bool – cache the result of this Thunk such that if the thunk is evaluated again, one can just reuse the cached value. If it’s been removed from cache, recompute the value.

Simple example

Let's see a very simple directed acyclic graph (or DAG) constructed with Dagger:

using Dagger

add1(value) = value + 1
add2(value) = value + 2
combine(a...) = sum(a)

p = Dagger.@spawn add1(4)
q = Dagger.@spawn add2(p)
r = Dagger.@spawn add1(3)
s = Dagger.@spawn combine(p, q, r)

@assert fetch(s) == 16

The thunks p, q, r, and s have the following structure:


The final result (from fetch(s)) is the obvious consequence of the operation:

add1(4) + add2(add1(4)) + add1(3)

(4 + 1) + ((4 + 1) + 2) + (3 + 1) == 16

Eager Execution

Dagger's @spawn macro works similarly to @async and Threads.@spawn: when called, it wraps the function call specified by the user in an EagerThunk object, and immediately places it onto a running scheduler, to be executed once its dependencies are fulfilled.

x = rand(400,400)
y = rand(400,400)
zt = Dagger.@spawn x * y
z = fetch(zt)
@assert isapprox(z, x * y)

One can also wait on the result of @spawn and check completion status with isready:

x = Dagger.@spawn sleep(10)
@assert !isready(x)
@assert isready(x)

Like @async and Threads.@spawn, Dagger.@spawn synchronizes with locally-scoped @sync blocks:

function sleep_and_print(delay, str)
@sync begin
    Dagger.@spawn sleep_and_print(3, "I print first")
wait(Dagger.@spawn sleep_and_print(1, "I print second"))

One can also safely call @spawn from another worker (not ID 1), and it will be executed correctly:

x = fetch(Distributed.@spawnat 2 Dagger.@spawn 1+2) # fetches the result of `@spawnat`
@assert fetch(x) == 3 # fetch the result of `@spawn`

This is useful for nested execution, where an @spawn'd thunk calls @spawn. This is detailed further in Dynamic Scheduler Control.


If a thunk errors while running under the eager scheduler, it will be marked as having failed, all dependent (downstream) thunks will be marked as failed, and any future thunks that use a failed thunk as input will fail. Failure can be determined with fetch, which will re-throw the error that the originally-failing thunk threw. wait and isready will not check whether a thunk or its upstream failed; they only check if the thunk has completed, error or not.

This failure behavior is not the default for lazy scheduling (Lazy API), but can be enabled by setting the scheduler/thunk option (Scheduler and Thunk options) allow_error to true. However, this option isn't terribly useful for non-dynamic usecases, since any thunk failure will propagate down to the output thunk regardless of where it occurs.

Lazy API

Alongside the modern eager API, Dagger also has a legacy lazy API, accessible via @par or delayed. The above computation can be executed with the lazy API by substituting @spawn with @par and fetch with collect:

p = @par add1(4)
q = @par add2(p)
r = @par add1(3)
s = @par combine(p, q, r)

@assert collect(s) == 16

or similarly, in block form:

s = @par begin
    p = add1(4)
    q = add2(p)
    r = add1(3)
    combine(p, q, r)

@assert collect(s) == 16

Alternatively, if you want to compute but not fetch the result of a lazy operation, you can call compute on the thunk. This will return a Chunk object which references the result (see Chunks for more details):

x = @par 1+2
cx = compute(x)
@assert collect(cx) == 3

Note that, as a legacy API, usage of the lazy API is generally discouraged for modern usage of Dagger. The reasons for this are numerous:

  • Nothing useful is happening while the DAG is being constructed, adding extra latency
  • Dynamically expanding the DAG can't be done with @par and delayed, making recursive nesting annoying to write
  • Each call to compute/collect starts a new scheduler, and destroys it at the end of the computation, wasting valuable time on setup and teardown
  • Distinct schedulers don't share runtime metrics or learned parameters, thus causing the scheduler to act less intelligently
  • Distinct schedulers can't share work or data directly


Dagger relies heavily on communication between workers to operate. To make this efficient when communicating potentially large units of data, Dagger uses a remote reference, called a Dagger.Chunk, to refer to objects which exist on another worker. Chunks are backed by a distributed refcounting mechanism provided by MemPool.jl, which ensures that the referenced data is not GC'd until all Chunks referencing that object are GC'd from all workers containing them. Conveniently, if you pass in a Chunk object as an input to a function using either API, then the thunk's payload function will get executed with the value contained in the Chunk. The scheduler also understands Chunks, and will try to schedule work close to where their Chunk inputs reside, to reduce communication overhead.

Chunks also have a cached type, a "processor", and a "scope", which are important for identifying the type of the object, where in memory (CPU RAM, GPU VRAM, etc.) the value resides, and where the value is allowed to be transferred and dereferenced. See Processors and Scopes for more details on how these properties can be used to control scheduling behavior around Chunks.

Scheduler and Thunk options

While Dagger generally "just works", sometimes one needs to exert some more fine-grained control over how the scheduler allocates work. There are two parallel mechanisms to achieve this: Scheduler options (from Dagger.Sch.SchedulerOptions) and Thunk options (from Dagger.Sch.ThunkOptions). These two options structs generally contain the same options, with the difference being that Scheduler options operate globally across an entire DAG, and Thunk options operate on a thunk-by-thunk basis. Scheduler options can be constructed and passed to collect() or compute() as the keyword argument options for lazy API usage:

t = @par 1+2
opts = Dagger.Sch.ThunkOptions(;single=1) # Execute on worker 1

compute(t; options=opts)

collect(t; options=opts)

Thunk options can be passed to @spawn/spawn, @par, and delayed similarly:

# Execute on worker 1

Dagger.@spawn single=1 1+2

Dagger.spawn(+, 1, 2; single=1)

opts = Dagger.Sch.ThunkOptions(;single=1)
delayed(+)(1, 2; options=opts)

Dagger.Sch.SchedulerOptions Dagger.Sch.ThunkOptions