Using resources effectively
OverviewTeaching: 10 min
Exercises: 20 minQuestions
How can I review past jobs?
How can I use this knowledge to create a more accurate submission script?Objectives
Look up job statistics.
Make more accurate resource requests in job scripts based on data describing past performance.
We’ve touched on all the skills you need to interact with an HPC cluster: logging in over SSH, loading software modules, submitting parallel jobs, and finding the output. Let’s learn about estimating resource usage and why it might matter.
Estimating Required Resources Using the Scheduler
Although we covered requesting resources from the scheduler earlier with the π code, how do we know what type of resources the software will need in the first place, and its demand for each? In general, unless the software documentation or user testimonials provide some idea, we won’t know how much memory or compute time a program will need.
Read the Documentation
Most HPC facilities maintain documentation as a wiki, a website, or a document sent along when you register for an account. Take a look at these resources, and search for the software you plan to use: somebody might have written up guidance for getting the most out of it.
A convenient way of figuring out the resources required for a job to run
successfully is to submit a test job, and then ask the scheduler about its
sacct -u yourUsername. You can use this knowledge to set up the
next job with a closer estimate of its load on the system. A good general rule
is to ask the scheduler for 20% to 30% more time and memory than you expect the
job to need. This ensures that minor fluctuations in run time or memory use
will not result in your job being cancelled by the scheduler. Keep in mind that
if you ask for too much, your job may not run even though enough resources are
available, because the scheduler will be waiting for other people’s jobs to
finish and free up the resources needed to match what you asked for.
Since we already submitted
amdahl to run on the cluster, we can query the
scheduler to see how long our job took and what resources were used. We will
sacct -u yourUsername to get statistics about
[yourUsername@login1 ~]$ sacct -u yourUsername
JobID JobName Partition Account AllocCPUS State ExitCode ------------ ---------- ---------- ---------- ---------- ---------- -------- 7 file.sh cpubase_b+ def-spons+ 1 COMPLETED 0:0 7.batch batch def-spons+ 1 COMPLETED 0:0 7.extern extern def-spons+ 1 COMPLETED 0:0 8 file.sh cpubase_b+ def-spons+ 1 COMPLETED 0:0 8.batch batch def-spons+ 1 COMPLETED 0:0 8.extern extern def-spons+ 1 COMPLETED 0:0 9 example-j+ cpubase_b+ def-spons+ 1 COMPLETED 0:0 9.batch batch def-spons+ 1 COMPLETED 0:0 9.extern extern def-spons+ 1 COMPLETED 0:0
This shows all the jobs we ran today (note that there are multiple entries per job). To get info about a specific job (for example, 347087), we change command slightly.
[yourUsername@login1 ~]$ sacct -u yourUsername -l -j 347087
It will show a lot of info; in fact, every single piece of info collected on
your job by the scheduler will show up here. It may be useful to redirect this
less to make it easier to view (use the left and right arrow
keys to scroll through fields).
[yourUsername@login1 ~]$ sacct -u yourUsername -l -j 347087 | less -S
This view can help compare the amount of time requested and actually used, duration of residence in the queue before launching, and memory footprint on the compute node(s).
How accurate were our estimates?
Improving Resource Requests
From the job history, we see that
amdahl jobs finished executing in
at most a few minutes, once dispatched. The time estimate we provided
in the job script was far too long! This makes it harder for the
queuing system to accurately estimate when resources will become free
for other jobs. Practically, this means that the queuing system waits
to dispatch our
amdahl job until the full requested time slot opens,
instead of “sneaking it in” a much shorter window where the job could
actually finish. Specifying the expected runtime in the submission
script more accurately will help alleviate cluster congestion and may
get your job dispatched earlier.
Narrow the Time Estimate
parallel_job.shto set a better time estimate. How close can you get?
The following line tells Slurm that our job should finish within 2 minutes:
#SBATCH -t 00:02:00
Accurate job scripts help the queuing system efficiently allocate shared resources.