Do not ask for a lot more memory than you need
A job that asks for many CPUs but little memory will be billed for its number of CPUs, while a job that asks for a lot of memory but few CPUs, may be billed for its memory requirement.
If you ask for a lot more memory than you need:
You might be surprised that your job will bill your project a lot more than you expected.
Your job may queue much longer than it would asking for less memory.
The possibility to parallelize your job/workflow may be more limited.
Table of Contents
Please read the above box, please share this with your colleagues and students. A significant portion of resources risks to be wasted if you ask for too much memory.
This is because memory and CPU are not completely independent. If I ask for too much memory, I also block the compute resources which carry that memory resource at the same time.
A too generous “better safe than sorry” approach to memory allocation leads to these problems:
Your compute account gets charged too much (problem for you and your allocation group, as well as for the tax payer financing these compute resources)
Other users have to wait longer with their jobs (problem for us and for other users)
Your jobs may queue much longer (problem for you) because the scheduler does not know that you might use a lot less memory than you ask it to reserve for you
The possibility to parallelize your job/workflow may be severely limited if you ask for excessive amount of unused memory since there may not be enough memory left for parallel jobs running at the same time
It is important to make sure that your jobs use the right amount of memory (below we show how to find out) and the right number of CPUs in order to help you and others using the HPC machines utilize these resources more efficiently, and in turn get work done more speedily.
If you ask for too little memory, your job will be stopped and it might be stopped late in the run.
We recommend users to run a test job before submitting many similar runs to the queue system and find out how much memory is used (see below for examples on how to do that). Once you know, add perhaps 20% extra memory (and runtime) for the job compared to what your representative test case needed.
Speaking of right partition, one way to get more memory if a node is not enough is to spread the job over several nodes by asking for more cores than needed. But this comes at the price of paying for more resources, queuing longer, and again blocking others. A good alternative for jobs that need a lot of memory is often to get access to “highmem” nodes which are designed for jobs with high memory demand.
You can test some of the approaches with the following example code (
which allocates 3210 MB (you can adapt that value if you want it to consume
more or less memory):
program example implicit none integer(8), parameter :: size_mb = 3210 integer(8) :: size_mw real(8), allocatable :: a(:) print *, 'will try to allocate', size_mb, 'MB' size_mw = int(1000000*size_mb/7.8125d0) allocate(a(size_mw)) ! this is here so that the allocated memory gets actually used and the ! compiler does not skip allocating the array a = 1.0d0 print *, 'first element:', a(1) ! wait for 35 seconds ! because slurm only samples every 30 seconds call sleep(35) deallocate(a) print *, 'ok all went fine' end program
Compile it like this and later we can examine
mybinary and check whether we can find out that
it really allocated 3210 MB:
$ gfortran example.f90 -o mybinary
While the job is running, find out on which node(s) it runs using
squeue -u $USER,
ssh into one of the listed compute nodes and run
top -u $USER.
We can use the following example script (adapt
--account=nn____k; this is tested on Saga):
#!/bin/bash #SBATCH --account=nn____k #SBATCH --qos=devel #SBATCH --job-name='mem-profiling' #SBATCH --time=0-00:01:00 #SBATCH --mem-per-cpu=3500M #SBATCH --ntasks=1 # we could also compile it outside of the job script gfortran example.f90 -o mybinary ./mybinary
Slurm generates an output for each job you run. For instance job number
This output contains the following:
Task and CPU usage stats: JobID JobName AllocCPUS NTasks MinCPU MinCPUTask AveCPU Elapsed ExitCode ------------ ---------- ---------- -------- ---------- ---------- ---------- ---------- -------- 4200691 mem-profi+ 1 00:00:37 0:0 4200691.bat+ batch 1 1 00:00:01 0 00:00:01 00:00:37 0:0 4200691.ext+ extern 1 1 00:00:00 0 00:00:00 00:00:37 0:0 Memory usage stats: JobID MaxRSS MaxRSSTask AveRSS MaxPages MaxPagesTask AvePages ------------ ---------- ---------- ---------- -------- -------------- ---------- 4200691 4200691.bat+ 3210476K 0 3210476K 0 0 0 4200691.ext+ 0 0 0 0 0 0 Disk usage stats: JobID MaxDiskRead MaxDiskReadTask AveDiskRead MaxDiskWrite MaxDiskWriteTask AveDiskWrite ------------ ------------ --------------- -------------- ------------ ---------------- -------------- 4200691 4200691.bat+ 1.11M 0 1.11M 0.02M 0 0.02M 4200691.ext+ 0.00M 0 0.00M 0 0 0
From this (see below
Memory usage stats) we can find out that the job needed
3210476K memory (3210476 KB).
Note that Slurm samples the memory every 30 seconds. This means that if your job is shorter than 30 seconds, it will show that your calculation consumed zero memory which is probably wrong. The sampling rate also means that if your job contains short peaks of high memory consumption, the sampling may completely miss these.
Slurm reports values for each job step
If you call
mpirun multiple times in your job script they will get
one line each in the
sacct output with separate entries for both
Also the job script itself (commands run in the jobscript without using
mpirun) is counted as a step, called the
This creates a short version of the above.
As an example, I want to know this for my job which had the number
$ sacct -j 4200691 --format=MaxRSS MaxRSS ---------- 3210476K 0
From this we see that the job needed
3210476K memory, same as above. The
comment above about possibly multiple steps applies also here.
seff is a nice tool which we can use on completed jobs. For example here we ask
for a summary for the job number 4200691:
$ seff 4200691
Job ID: 4200691 Cluster: saga User/Group: user/user State: COMPLETED (exit code 0) Cores: 1 CPU Utilized: 00:00:01 CPU Efficiency: 2.70% of 00:00:37 core-walltime Job Wall-clock time: 00:00:37 Memory Utilized: 3.06 GB Memory Efficiency: 89.58% of 3.42 GB
In your job script instead of running
./mybinary directly, prepend it with
#!/bin/bash #SBATCH --account=nn____k #SBATCH --qos=devel #SBATCH --job-name='mem-profiling' #SBATCH --time=0-00:01:00 #SBATCH --mem-per-cpu=3500M #SBATCH --ntasks=1 # instead of this: # ./mybinary # we do this: /usr/bin/time -v ./mybinary
Then in the Slurm output we find:
Command being timed: "./mybinary" User time (seconds): 0.32 System time (seconds): 0.67 Percent of CPU this job got: 2% Elapsed (wall clock) time (h:mm:ss or m:ss): 0:36.00 Average shared text size (kbytes): 0 Average unshared data size (kbytes): 0 Average stack size (kbytes): 0 Average total size (kbytes): 0 Maximum resident set size (kbytes): 3210660 Average resident set size (kbytes): 0 Major (requiring I/O) page faults: 2 Minor (reclaiming a frame) page faults: 2559 Voluntary context switches: 19 Involuntary context switches: 3 Swaps: 0 File system inputs: 1073 File system outputs: 0 Socket messages sent: 0 Socket messages received: 0 Signals delivered: 0 Page size (bytes): 4096 Exit status: 0
The relevant information in this context is
Maximum resident set size (kbytes), in this case 3210660 kB which is what we expected to find. Note
that it has to be
/usr/bin/time -v and
time -v alone will not do it.
You can profile your job using Arm Performance Reports.
Here is an example script (adapt
--account=nn____k; this is tested on Saga):
#!/bin/bash #SBATCH --account=nn____k #SBATCH --qos=devel #SBATCH --job-name='mem-profiling' #SBATCH --time=0-00:01:00 #SBATCH --mem-per-cpu=3500M #SBATCH --ntasks=1 module purge module load Arm-Forge/21.1 perf-report ./mybinary
This generates a HTML and text summary. These reports contain lots of interesting information. Here showing the relevant part of the text report for the memory:
Memory: Per-process memory usage may also affect scaling: Mean process memory usage: 1.39 GiB Peak process memory usage: 3.07 GiB Peak node memory usage: 27.0% |==|
This is not an elegant approach but can be an OK approach to calibrate one script before submitting 300 similar jobs.
What you can do is to start with a generous memory setting:
And gradually reduce it until your job fails with
oom-kill (“oom” is short for “out of memory”):
slurm_script: line 11: 33333 Killed ./mybinary slurmstepd: error: Detected 1 oom-kill event(s) in step 997857.batch cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
Or you start with a very conservative estimate and you gradually increase until the job is not stopped.
Then you also know. But there are more elegant ways to find this out (see options above).