How can I run many similar computations conveniently?

It is often necessary to run the same application on many input files, or with many different parameter values. You can of course manage such jobs by hand, or write scripts to do that for you. However, this is a very common scenario, so we developed software to do that for you.

Two general purpose software packages are available:

Both are designed to handle this use case, but each has its own strengths and weaknesses.

What features do atools and worker have?

Both software packages will do the bookkeeping for you, i.e.,

  • keep track of the computations that were completed;
  • monitor the progress of a running job;
  • allow you to resume computations in case you underestimated the walltime requirements;
  • provide an overview of the computations that succeed, failed, or were not completed;
  • aggregate output of individual computations;
  • analyze the efficiency of a finished job.

Both software packages have been designed with simplicity in mind, one of the design goals is to make them as easy to use as possible.

For a detailed overview of the features, see the atools documentation and the worker documentation.

What to use: atools or worker?

That depends on a number of issues, and you have to consider them all to make the correct choice.

Type of computation

In worker and atools terminology, an individual computation is referred to as a work item. Depending on the implementation of the work item, worker or atools may be a better match. The following table summarizes this.

work item type worker atools
sequential yes yes
multi-threaded yes yes
MPI-based no yes


Although this might seems to suggest that since atools can deal with all types of work items, it is the best choice, this is definitely not true.

The table makes it clear that MPI applications can not be used in work items for worker. worker itself is implemented using MPI, and hence things would get terminally confused if it executes work items that contain calls to the MPI API.

Walltime per work item

When work items take only a short time to complete, the overhead for starting new work items will be considerable for atools since it relies on the scheduler to start individual work items. This is much more efficient for worker since all work items are executed by a single job, so the scheduler is not involved.

On the other side of the spectrum, i.e., work items that take a very long time complete, atools may be the better choice since work items are executed independently. This however depends on the reliability of the infrastructure.

The following table summarizes this.

single work item walltime worker atools
< 1 second -
< 1 minute + -
1 minute to 24 hours ++ ++
> 24 hours + ++

Number of work items

If you need to do many individual computations (work items), say more than 500, worker is the better choice. It will be run as a single job, rather than many individual jobs, hence lightening the load on the scheduler considerably.

Job policies

The following job policies are currently in effect on various VSC clusters.

Multiple jobs from multiple users are allowed to run concurrently on a node.
Single user
Multiple jobs from a single user are allowed to run concurrently on a node.
Single job
Only a single job can run on a compute node.

On some clusters, credits are required to run jobs, and that policy may also influence your choice.

The table below provides an overview of the policies in effect on the various cluster/partitions.

VSC hub cluster partition policy accounting
Antwerp any any single user no
Brussels any any shared no
Ghent any any shared no
Leuven genius default shared yes
Leuven genius bigmem shared yes
Leuven genius gpu shared yes
Leuven genius superdome shared yes
Leuven breniac (Tier-1) default single job yes
Clusters with accounting enabled:
If you use atools on a cluster where accounting is active, make sure a work item uses all resources of that node. If multiple work items run on the same node concurrently, you will be charged for each work item individually, making that a very expensive computation. In this situation, use worker.
Clusters with single user policy:
Ensure that the load balance is as good as possible. If a few work items require much more time than others, they may block the nodes they are running on from running other jobs. This is the case for both atools and worker. However, since worker is an MPI application, it will keep all nodes involved in the job blocked, aggravating the problem.
Clusters with shared policy
Here atools allows the scheduler the most flexibility, but keep in mind the considerations on work item walltime and the number of work items.