A Julia package for clusterless distributed computing on Azure
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Updated Last
1 Year Ago
Started In
May 2021


AzureClusterlessHPC.jl - Simplified distributed computing


AzureClusterlessHPC.jl is a package for simplified parallal computing on Azure. AzureClusterlessHPC.jl borrows the syntax of Julia's Distributed Programming package to easily execute parallel Julia workloads in the cloud using Azure Batch. Instead of a parallel Julia session, users create one or multiple worker pools and remotely execute code on them.


AzureClusterlessHPC provides macros that let us define functions on batch workers, similar to how @everywhere works for a parallel Julia session:

# Define a function
@batchdef hello_world(name)
    print("Hello $name")
    return "Goodbye"

We can then either execute this function on our local machine or as a batch job using the @batchexec macro (which is similar to Julia's @spawn macro for parallel Julia sessions):

# Execute function on local machine
out = hello_world("Bob")

# Execute function via Azure Batch
out = @batchexec hello_world("Jane")

Using the pmap function in combination with @batchexec allows us to run a multi-task batch job:

# Execute a multi-task batch job
out = @batchexec pmap(name -> hello_world(name), ["Bob", "Jane"])


To install AzureClusterlessHPC.jl, run the following command from an interactive Julia session (press the ] key and then type the command):

] add AzureClusterlessHPC.jl

Before being able to use AzureClusterlessHPC.jl you need to create a few Azure resources. Follow the instructions here.


Follow this link to the documentation.


AzureClusterlessHPC can be used to bring various distributed computing applications in Julia to Azure. Check out the notebooks in the examples section to find tutorials for the following applications:

  • Generic batch, map-reduce and iterative map-reduce examples

  • Deep learning with AzureClusterlessHPC.jl and Flux.jl

  • Seismic imaging and inversion with COFII.jl and JUDI.jl


AzureClusterlessHPC.jl is developed and maintained by the Microsoft Research for Industries (RFI) team.

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