A framework for out-of-core and parallel execution
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10 Months Ago
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December 2015


A framework for out-of-core and parallel computing.

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At the core of Dagger.jl is a scheduler heavily inspired by Dask. It can run computations represented as directed-acyclic-graphs (DAGs) efficiently on many Julia worker processes.


You can install Dagger by typing

julia> ] add Dagger


Once installed, the Dagger package can by used by typing

using Dagger

The main function for using Dagger is delayed

delayed(f; options...)

It returns a function which when called creates a Thunk object representing a call to function f with the given arguments. If it is called with other thunks as input, then they form a graph with input nodes directed at the output. The function f gets the result of the input Thunks. Thunks don't pass keyword argument to the function f. Options kwargs... to delayed are passed to the scheduler to control its behavior:

  • 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)
  • meta::Bool -- pass the input “Chunk” objects themselves to f and not the value contained in them - this is always run on the master process
  • 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.

DAG creation interface

Here is a very simple example DAG:

using Dagger

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

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

@assert collect(s) == 16

The connections between nodes p, q, r and s is represented by this dependency graph:


The final result is the obvious consequence of the operation

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

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

To compute and fetch the result of a thunk (say s), you can call collect(s). collect will fetch the result of the computation to the master process. Alternatively, if you want to compute but not fetch the result you can call compute on the thunk. This will return a Chunk object which references the result. If you pass in a Chunk objects as an input to a delayed function, then the function will get executed with the value of the Chunk -- this evaluation will likely happen where the input chunks are, to reduce communication.

The key point is that, for each argument to a node, if the argument is a Thunk, it'll be executed before this node and its result will be passed into the function f provided. If the argument is not a Thunk (just some regular Julia object), it'll be passed as-is to the function f.


Polytrees are easily supported by Dagger. To make this work, pass all the head nodes Thunks into a call to delayed as arguments, which will act as the top node for the graph.

group(x...) = [x...]
top_node = delayed(group)(head_nodes...)

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, and Thunk options can be passed to Dagger's delayed function similarly: delayed(myfunc)(arg1, arg2, ...; options=opts). Check the docstring for the two options structs to see what options are available.

Processors and Resource control

By default, Dagger uses the CPU to process work, typically single-threaded per cluster node. However, Dagger allows access to a wider range of hardware and software acceleration techniques, such as multithreading and GPUs. These more advanced (but performant) accelerators are disabled by default, but can easily be enabled by using Scheduler/Thunk options in the proctypes field. If non-empty, only the processor types contained in options.proctypes will be used to compute all or a given thunk.

GPU Processors

The DaggerGPU.jl package can be imported to enable GPU acceleration for NVIDIA and AMD GPUs, when available. The processors provided by that package are not enabled by default, but may be enabled via options.proctypes as usual.

Rough high level description of scheduling

  • First picks the leaf Thunks and distributes them to available workers. Each worker is given at most 1 task at a time. If input to the node is a Chunk, then workers which already have the chunk are preferred.
  • When a worker finishes a thunk it will return a Chunk object to the scheduler.
  • Once the worker has returned a Chunk, the scheduler picks the next task for the worker -- this is usually the task the worker immediately made available (if possible). In the small example above, if worker 2 finished p it will be given q since it will already have the result of p which is input to q.
  • The scheduler also issues "release" Commands to chunks that are no longer required by nodes in the DAG: for example, when s is computed all of p, q, r are released to free up memory. This can be prevented by passing persist or cache options to delayed.


We thank DARPA, Intel, and the NIH for supporting this work at MIT.