Parallel Computing on Slurm Clusters

Updated: June 11, 2020

Edit this Page via GitHub       Comment by Filing an Issue      Have Questions? Ask them here.

Parallel computing is an approach to computing where many tasks are done simultaneously- either on a single device or on multiple independent devices. These tasks can be dependent or independent of each other requiring varying degrees of ordering and orchestration. Parallel computing can be quite complicated to set up but can improve job throughput when done correctly.

Parallel computing starts with breaking a larger task into smaller steps- the “size” and relationship of those steps is highly dependent on the task at hand but determines much about how the job can be “parallelized”. Because of the variety of approaches to large tasks, often there can be multiple strategies to consider using to identify the most effective approach to use for the particular task at hand.

When steps are highly dependent on each other (e.g. the output of one step is used for input into the next) that job is said to be “serial” and it won’t benefit greatly from parallel processing. At the other end, “embarrassingly” or “pleasantly” parallel work has individual steps that do not depend on each other and can occur in at the same time, often in great numbers.

Once you’ve determined how your work can be parallelized, there are two ways to distribute those steps. The first uses the capabilities of multi-core CPUs- modern CPUs have multiple cores, each of which are capable of processing independent steps. This technique is typically referred to as “threading.”

Another approach to using multiple processors is to spread the work over multiple different computers. This approach has the advantage of being able to scale up the amount of computation being done concurrently. This approach is often described as “distributed”.

Note: It is also possible to combine those techniques- using multiple cores on multiple computers. This can add a little complexity, but many tools will handle this neatly.

Choosing an Approach

The primary drivers for choosing between the two approaches is how much communication between individual steps is necessary and how many steps there are. Communication between steps is computationally expensive, and if that communication needs to cross a network (as in a distributed solution) there can be a degradation in performance compared to keeping all of the steps on the same system (as in the threaded solution). However, if there are many steps the resources on a single system will be a bottleneck, which makes a distributed solution more appealing.

An Atlas of Parallel Workloads

Pleasantly Parallel

“Pleasantly parallel” work (AKA “embarrassingly parallel”) is typically made up of many completely independent steps. By independent we mean that:

  • any one step does not depend on the output or completion of any other step
  • steps do not need to exchange information with other steps

Examples: simulations, GWAS, chromosome by chromosome analyses


This is one opposite of pleasantly parallel. In sequential workloads each step is dependent on another step- step “B” cannot proceed until step “A” is complete. This kind of workload is nearly impossible to speed up with additional processors.

Highly Connected

Another opposite of the pleasantly parallel workload is workload where steps communicate information between other steps. Weather and climate forecasts are notorious for this kind of workload- each step represents a block of atmosphere which is affected by its neighbors, thus step needs to look at the state of another step and vice-versa.

These problems require very low-latency, high-speed communication between steps and are typically better served when run on a single system (or one of the exotic supercomputers). That said, modern networks are fairly good and can provide usable service for this communication if the number of steps greatly exceeds the number of cores available on a single system.

Techniques for Parallel Computation

Workflow Managers

If desired, one way to manage jobs, environments, and data transfers particularly in a series of linked tasks or jobs is to use a workflow manager. Workflow managers allow you to describe a workflow as a series of individual tasks. Then the workflow manager software does the work of:

  • sending the jobs to the compute resources,
  • deciding what tasks can be done in parallel,
  • staging data for use and keeping track of inputs and outputs,
  • environment management (via docker containers or environment modules)
  • monitoring jobs and providing you with metadata about them and the workflow itself.

Two workflow managers in use on the Fred Hutch campus are Nextflow and Cromwell and users are actively curating more shared support and resources at those pages as well as in GitHub. Workflow manager related information is collected as a GitHub Workflow Manager Project as well as specific Nextflow repos or Cromwell/WDL repos which often contain shared workflows or configuration information.

Task Marshaling and Coordination

For pleasantly parallel work its primarily necessary to track the independent steps within the job. If you divide your work into 1000 steps, you need some way to track and ensure each step was completed prior to proceeding. These techniques sometimes require integration with the workload manager depending on the level of tracking required.

R has libraries such as Rslurm which can provide back ends to the parallel library, enabling you to write simple loops with Rslurm managing the jobs which are sent to cluster nodes. make has also been used to track tasks, though this requires that you write a little more code for job submission


“Threading” is an approach where tasks or computations are divided amongst the compute resources available on a computer- this allows a single process to use multiple compute cores. More information on threads is available here.

Threading is very useful for highly-connected tasks that communicate with each other or share memory- as all of the threads are on the same system there is little delay in communications between thread (commonly referred to as “low latency communication”. Threading typically involves fairly low-level programming skills though both Python and R have tools and libraries that make threading some tasks easier. If you run a software that is multi-threaded please ensure that it only uses the number of cpu cores that are assigned to your job on the current node. This number is kept in environment variable SLURM_JOB_CPUS_PER_NODE and you can use it like this :

    bowtie2 -p ${SLURM_JOB_CPUS_PER_NODE} ...

Message Passing

Message passing allows processes to share memory and communicate with each other across multiple computers using a network. The most common standard for message passing is the Message Passing Interface (MPI) which defines the high-level interfaces used to communicate between different hosts. OpenMPI is the most common implementation of MPI and the one used here.

MPI is well-suited to scaling up highly-connected algorithms to run across computers. MPI can also be used for marshaling independent (i.e. pleasantly parallel) work though this approach is overkill in most cases.

Parallel Operations in Slurm

Slurm allows a single job to request multiple CPUs both on a single host and across multiple hosts. The fundamental unit is the task which can use one or many CPUs but cannot span multiple nodes.

The simplest method of running parallel computations in Slurm is srun. With srun, you can run multiple copies of an indicated program on multiple hosts by specifying the number of tasks to run:

srun --ntasks=6 myprogram

This will run six independent copies of myprogram on six different CPUs (though the assigned CPUs may be on one or more nodes.

If your task uses threading, you will want a single node, but multiple CPUs on that node. In these cases, you’ll need a single task that uses multiple CPUs per task.

srun --ntasks=1 --cpus-per-task=6 myprogram

will run a single copy of myprogram, but will allocate 6 CPUs to myprogram. Note that myprogram is “responsible” for figuring out how many CPUs are available and running within that allocation.

If your task uses message passing you can use specify multiple tasks each using one or more CPUs (one CPU per task is most typical):

srun --ntasks=6 --cpus-per-task=1 mpirun myprogram

In this case myprogram needs to be compiled using the MPI compilers or use MPI aware libraries. Since OpenMPI and Slurm support each other well, it’s not necessary to tell the mpirun about the nodes and CPUs assigned. Note that 1 is the default for cpus-per-task and can be safely omitted.


We have begun consolidating examples of parallel computing approaches in the FredHutch/slurm-examples GitHub repository. Please refer to that repository for more community curated example approaches and associated documentation to see if someone has approached a problem similar to yours so you don’t have to start from scratch.

Updated: June 11, 2020

Edit this Page via GitHub       Comment by Filing an Issue      Have Questions? Ask them here.