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Updated Last
10 Months Ago
Started In
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 and threads, as well as GPUs via DaggerGPU.jl.

DTable has been moved out of this repository. You can find it in a standalone package format here.


Dagger.jl can be installed using the Julia package manager. Enter the Pkg REPL mode by typing "]" in the Julia REPL and then run:

pkg> add Dagger

Or, equivalently, via the Pkg API:

julia> import Pkg; Pkg.add("Dagger")


Once installed, the Dagger package can by used like so

using Distributed; addprocs() # get us some workers
using Dagger

# do some stuff in parallel!
a = Dagger.@spawn 1+3
b = Dagger.@spawn rand(a, 4)
c = Dagger.@spawn sum(b)
fetch(c) # some number!

Contributing Guide

PRs Welcome GitHub issues GitHub contributors

Contributions are encouraged.

There are several ways to contribute to our project:

Reporting Bugs: If you find a bug, please open an issue and describe the problem. Make sure to include steps to reproduce the issue and any error messages you receive regarding that issue.

Fixing Bugs: If you'd like to fix a bug, please create a pull request with your changes. Make sure to include a description of the problem and how your changes will address it.

Additional examples and documentation improvements are also very welcome.


List of recommended Dagger.jl resources:

Help and Discussion

For help and discussion, we suggest asking in the following places:

Julia Discourse and on the Julia Slack in the #distributed channel.


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