Out-of-Memory (OOM) or Excessive Memory Usage

Problem description

A common problem on our systems is that a user's job causes a node out of memory or uses more than its allocated memory if the node is shared with other jobs.

If a job exhausts both the physical memory and the swap space on a node, it causes the node to crash. With a parallel job, there may be many nodes that crash. When a node crashes, the OSC staff has to manually reboot and clean up the node. If other jobs were running on the same node, the users have to be notified that their jobs failed.

If your job requests less than a full node, for example, --ntasks-per-node=4, it may be scheduled on a node with other running jobs. In this case, your job is entitled to a memory allocation proportional to the number of cores requested. For example, if a system has 4.5 GB per core and you request one core, it is your responsibility to make sure your job uses no more than 4.5 GB. Otherwise your job will interfere with the execution of other jobs.

Example errors

# OOM in a parallel program launched through srun

slurmstepd: error: Detected 1 oom-kill event(s) in StepId=14604003.0 cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.

srun: error: o0616: task 0: Out Of Memory

# OOM in program run directly by the batch script of a job

slurmstepd: error: Detected 1 oom-kill event(s) in StepId=14604003.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.


Each node has a fixed amount of physical memory and a fixed amount of disk space designated as swap space. If your program and data don’t fit in physical memory, the virtual memory system writes pages from physical memory to disk as necessary and reads in the pages it needs. This is called swapping. 

You can find the amount of usable memory on our system at default memory limits. You can see the memory and swap values for a node by running the Linux command free on the node.

In the world of high-performance computing, swapping is almost always undesirable. If your program does a lot of swapping, it will spend most of its time doing disk I/O and won’t get much computation done. Swapping is not supported at OSC. Please consider the suggestions below.

Suggested solutions

Here are some suggestions for fixing jobs that use too much memory. Feel free to contact OSC Help for assistance with any of these options.

Some of these remedies involve requesting more processors (cores) for your job. As a general rule, we require you to request a number of processors proportional to the amount of memory you require. You need to think in terms of using some fraction of a node rather than treating processors and memory separately. If some of the processors remain idle, that’s not a problem. Memory is just as valuable a resource as processors.

Request whole node or more processors

Jobs requesting less than a whole node are those that request less than the total number of available cores. These jobs can be problematic for two reasons. First, they are entitled to use an amount of memory proportional to the cores requested; if they use more they interfere with other jobs. Second, if they cause a node to crash, it typically affects multiple jobs and multiple users.

If you’re sure about your memory usage, it’s fine to request just the number of processors you need, as long as it’s enough to cover the amount of memory you need. If you’re not sure, play it safe and request all the processors on the node.

Reduce memory usage

Consider whether your job’s memory usage is reasonable in light of the work it’s doing. The code itself typically doesn’t require much memory, so you need to look mostly at the data size.

If you’re developing the code yourself, look for memory leaks. In MATLAB look for large arrays that can be cleared.

An out-of-core algorithm will typically use disk more efficiently than an in-memory algorithm that relies on swapping. Some third-party software gives you a choice of algorithms or allows you to set a limit on the memory the algorithm will use.

Use more nodes for a parallel job

If you have a parallel job you can get more total memory by requesting more nodes. Depending on the characteristics of your code you may also need to run fewer processes per node.

Here’s an example. Suppose your job on Pitzer includes the following lines:

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=48
mpiexec mycode

This job has 2 nodes worth of memory available to it (specifically 178GB * 2 nodes of memory). The mpiexec command by default runs one process per core, which in this case is 96 copies of mycode.

If this job uses too much memory you can spread those 96 processes over more nodes. The following lines request 4 nodes, giving you a total of 712 GB of memory (4 nodes *178 GB). The -ppn 24 option on the mpiexec command says to run 24 processes per node instead of 48, for a total of 96 as before.

#SBATCH --nodes=4
#SBATCH --ntasks-per-node=48
mpiexec -ppn 24 mycode

Since parallel jobs are always assigned whole nodes, the following lines will also run 24 processes per node on 4 nodes.

#SBATCH --nodes=4
#SBATCH --ntasks-per-node=24
mpiexec mycode

Request large-memory nodes

Pitzer has 4 huge memory nodes with ~3 TB of memory and with 80 cores. Owens has 16 huge memory nodes with ~1.5 TB of memory and with 48 cores.

Since there are so few of these nodes, compared to hundreds of standard nodes, jobs requesting them will often have a long wait in the queue. The wait will be worthwhile, though, if these nodes solve your memory problem. See the batch limit pages for Owens and Pitzer to learn how to request huge or large memory nodes.

How to monitor your memory usage


If a job is currently running, or you know the timeframe that it was running, then grafana can be used to look at the individual nodes memory usage for jobs. Look for the graph that shows memory usage.


You can also view node status graphically using the OSC OnDemand Portal. Under "Jobs" select "Active Jobs." Click on "Job Status" and scroll down to see memory usage.


To view detailed metrics about jobs after waiting a day after the jobs are completed, you can use the XDMoD tool. It can show the memory usage for jobs over time as well as other metrics. Please see the job view how-to for more information on looking jobs.


Slurm command sstat can be used to obtain info for running jobs.

sstat --format=AveRSS,JobID -j <job-id> -a

During job

Query the job's cgroup which is what controls the amount of memory a job can use:

# return current memory usage
cat /sys/fs/cgroup/memory/slurm/uid_$(id -u)/job_$SLURM_JOB_ID/memory.usage_in_bytes | numfmt --to iec-i
# return memory limit
cat /sys/fs/cgroup/memory/slurm/uid_$(id -u)/job_$SLURM_JOB_ID/memory.limit_in_bytes | numfmt --to iec-i


If it appears that your job is close to crashing a node, we may preemptively delete the job.

If your job is interfering with other jobs by using more memory than it should be, we may delete the job.

In extreme cases OSC staff may restrict your ability to submit jobs. If you crash a large number of nodes or continue to submit problematic jobs after we have notified you of the situation, this may be the only way to protect the system and our other users. If this happens, we will restore your privileges as soon as you demonstrate that you have resolved the problem.

For details on retrieving files from unexpectedly terminated jobs see this FAQ.

For assistance

OSC has staff available to help you resolve your memory issues. See our client support request page for contact information.