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At the core of Dagger.jl is a scheduler heavily inspired byDask. It can run computations represented asdirected-acyclic-graphs (DAGs) efficiently on many Julia worker processes and threads, as well as GPUs viaDaggerGPU.jl.

TheDTable has been moved out of this repository. You can now find ithere.

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

pkg> add Dagger

Or, equivalently, install Dagger via the Pkg API:

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

Once installed, theDagger package can be loaded withusing Dagger, or ifyou want to use Dagger for distributed computing, it can be loaded as:

using Distributed;addprocs()# Add one Julia worker per CPU coreusing Dagger

You can run the following example to see how Dagger exposes easy parallelism:

# This runs first:a= Dagger.@spawnrand(100,100)# These run in parallel:b= Dagger.@spawnsum(a)c= Dagger.@spawnprod(a)# Finally, this runs:wait(Dagger.@spawnprintln("b:", b,", c:", c))

Dagger can support a variety of use cases that benefit from easy, automaticparallelism, such as:

• Parallelizing Nested Loops

This isn't an exhaustive list of the use cases that Dagger supports. There aremore examples in the docs, and more use cases examples are welcome (just filean issue or PR).

Please see the roadmap for missing features or known bugs:

Dagger Features and Roadmap

Other resources:

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:

• Docs
• Videos
  • Distributed Computing with Dagger.jl
  • Easy, Featureful Parallelism with Dagger.jl
  • Easier parallel Julia workflow with Dagger.jl
  • Dagger.jl Development and Roadmap

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

Julia Discourse and on theJulia Slack in the#dagger channel.

@inproceedings{alomairy2024dynamic,title={Dynamic Task Scheduling with Data Dependency Awareness Using Julia},author={Alomairy, Rabab and Tome, Felipe and Samaroo, Julian and Edelman, Alan},booktitle={2024 IEEE High Performance Extreme Computing Conference (HPEC)},pages={1--7},year={2024},organization={IEEE}}

@article{dagger2,title={Efficient Dynamic Task Scheduling in Heterogeneous Environments with Julia},author={Samaroo, Julian and Alomairy, Rabab and  and Giordano, Mose and Edelman, Alan},year={2024},publisher={MIT Open Access Articles}}

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