nodes vs tasks vs cpus vs cores

A combination of raw technical detail, Slurm’s loose usage of the terms core and cpu and multiple models of parallel computing require establishing a bit of background to fully explain how to make efficient use of multiple cores on SCG. Before we delve into that…


For the impatient reader, in 99.9% of the cases SCG was designed to support,

#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=N

is the correct way to request N cores for a job. Just replace N in that config with the number of cores you need and optionally inside job scripts use the ${SLURM_CPUS_PER_TASK} variable to pass the number of cores in the job to commands that accept an argument for number of core, cpus or threads, e.g.,

mycommand -arg1 -arg2 --threads=${SLURM_CPUS_PER_TASK} ...

Computer architecture

The parts of a modern computer we need to understand to apply to running jobs are listed here. (Note: This is way oversimplified and intended to give a basic overview for the purposes of understanding how to request resources from Slurm, there are a lot of resources out there to dig deeper into computer architecture.)

A physical motherboard which contains one or more of each of Socket, Memory bus and PCI bus.
A physical socket on a motherboard which accepts a physical CPU part.
A physical part that is plugged into a socket.
A physical CPU core, one of many possible cores, that are part of a CPU.
A virtual CPU thread, associated with a specific Core. This can be enabled or disabled on a system. SCG typically disabled hyperthreading.
Memory Bus
A communication bus between system memory and a Socket/CPU.
A communication bus between a Socket/CPU and I/O controllers (disks, networking, graphics,...) in the server.

Slurm complicates this, however, by using the terms core and cpu interchangeably depending on the context and Slurm command. --cpus-per-taks= for example is actually specifying the number of cores per task.

Parallel Computing

A very simplified description of parallel computing which is sufficient for most computing is to break parallel workloads into these three categories:

Fine grained
Each process does a lot of communication with the other processes. Think of a model which uses a grid of cells, and at each iteration all the cells exchange information with their neighboring cells. These types of applications are typically limited by interprocess communication bandwidth.
Coarse grained
Each process occasionally comunicates with other processes. A model which has independent units that occasionally exchange information globally would fall into this category. These types of processes are typically CPU or I/O limited.
Embarrassingly Parallel
Processes do not communicate with each other and run completely independently, possibly including a step to merge results when the last process finishes. These types of processes are typically CPU or I/O limited.

Many bioinformatics tasks fall into the last category. Alignment, for instance, can be easily parallelized by breaking input sequences into subsets which are then individualy aligned to a reference and the results merged/sorted after all subsets are complete. Some tools do this internally, e.g. running BLAST with some number of threads allows BLAST to place subsets of input sequence into each thread and then align against a shared in-memory reference, thus giving a pretty good speedup to the overall alignment task.

As the above example implies, when a task is broken up to be parallelized, the subtasks can be ran in more than one way. Models we care about for this are:


MPI (Message Passing Interface) is not very common in bioinformatics tools as it is (in the opinion of this sysadmin) difficult to debug and use correctly and is overkill for breaking up embarassingly parallel tasks. It’s so rare, in fact, that SCG by default rejects jobs that request more than one node on the assumption that doing so is almost always an error in the job submission. A special hidden partition is available for running MPI jobs upon request. MPI is really a special case of using Tasks (see below) to parallelze work but it has a complicated enough wrapper to deserve its own category.


Most bioinformatics tools that include a parallel option in the application use threading, with the most commonly used implementation being OpenMP. Applications with take an argument specifying cores, threads or CPUs are very likely to be using this model to parallelize the work. Threads can make efficient use of memory for things like holding reference data used by multiple threads/processes while running.


The most common example of parallelizing this way on SCG is to take a directory with many different sequence files and use a job script or job array to run the same command on all files, placing each command into its own job. This is what most bioinformatics workloads look like, at some point during a project when there are too many tasks or the tasks are too large to handle on a laptop or workstation, they can easily be broken into jobs to run in parallel on the SCG cluster resources.