ClusterMQ: send R function calls as cluster jobs

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This package will allow you to send function calls as jobs on a computing cluster with a minimal interface provided by the Q function:

# load the library and create a simple function
fx = function(x) x * 2

# queue the function call on your scheduler
Q(fx, x=1:3, n_jobs=1)
# list(2,4,6)

Computations are done entirely on the network and without any temporary files on network-mounted storage, so there is no strain on the file system apart from starting up R once per job. All calculations are load-balanced, i.e. workers that get their jobs done faster will also receive more function calls to work on. This is especially useful if not all calls return after the same time, or one worker has a high load.

Browse the vignettes here:


Install the clustermq package in R from CRAN (including the bundled ZeroMQ system library):


Alternatively you can use the remotes package to install directly from Github. Note that this version needs autoconf/automake and CMake for compilation:

# install.packages('remotes')
# remotes::install_github('mschubert/clustermq@develop') # dev version

[!TIP] For installation problems, see the FAQ


An HPC cluster’s scheduler ensures that computing jobs are distributed to available worker nodes. Hence, this is what clustermq interfaces with in order to do computations.

We currently support the following schedulers (either locally or via SSH):

[!TIP] Follow the links above to configure your scheduler in case it is not working out of the box and check the FAQ if your job submission errors or gets stuck


The most common arguments for Q are:

The documentation for other arguments can be accessed by typing ?Q. Examples of using const and export would be:

# adding a constant argument
fx = function(x, y) x * 2 + y
Q(fx, x=1:3, const=list(y=10), n_jobs=1)
# exporting an object to workers
fx = function(x) x * 2 + y
Q(fx, x=1:3, export=list(y=10), n_jobs=1)

clustermq can also be used as a parallel backend for foreach. As this is also used by BiocParallel, we can run those packages on the cluster as well:

register_dopar_cmq(n_jobs=2, memory=1024) # see `?workers` for arguments
foreach(i=1:3) %dopar% sqrt(i) # this will be executed as jobs
register(DoparParam()) # after register_dopar_cmq(...)
bplapply(1:3, sqrt)

More examples are available in the User Guide.

Comparison to other packages

There are some packages that provide high-level parallelization of R function calls on a computing cluster. We compared clustermq to BatchJobs and batchtools for processing many short-running jobs, and found it to have approximately 1000x less overhead cost.

Overhead comparison
Overhead comparison

In short, use clustermq if you want:

Use batchtools if you:

Use Snakemake or targets if:

Don’t use batch (last updated 2013) or BatchJobs (issues with SQLite on network-mounted storage).


Contributions are welcome and they come in many different forms, shapes, and sizes. These include, but are not limited to:

[!TIP] Check the User Guide and the FAQ first, maybe your query is already answered there


This project is part of my academic work, for which I will be evaluated on citations. If you like me to be able to continue working on research support tools like clustermq, please cite the article when using it for publications:

M Schubert. clustermq enables efficient parallelisation of genomic analyses. Bioinformatics (2019). doi:10.1093/bioinformatics/btz284