| title | teaching | exercises | questions | objectives | keypoints | |||||
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Using resources effectively |
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20 |
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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.
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.
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. {: .callout}
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
impact using {{ site.sched.hist }}. 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 pi.py to run on the cluster, we can query the
scheduler to see how long our job took and what resources were used. We will
use {{ site.sched.hist }} to get statistics about parallel-pi.sh.
{{ site.remote.prompt }} {{ site.sched.hist }}
{: .language-bash}
{% include {{ site.snippets }}/resources/account-history.snip %}
This shows all the jobs we ran recently (note that there are multiple entries per job). To get info about a specific job, we change command slightly.
{{ site.remote.prompt }} {{ site.sched.hist }} {{ site.sched.flag.histdetail }} 1965
{: .language-bash}
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
information to less to make it easier to view (use the left and right arrow
keys to scroll through fields).
{{ site.remote.prompt }} {{ site.sched.hist }} {{ site.sched.flag.histdetail }}
{: .language-bash}
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? {: .discussion}
From the job history, we see that pi.py 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 pi.py 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.
Edit
parallel_pi.shto set a better time estimate. How close can you get?Hint: use
{{ site.sched.flag.time }}.The following line tells {{ site.sched.name }} that our job should finish within 2 minutes:
{{ site.sched.comment }} {{ site.sched.flag.time }}{% if site.sched.name == "Slurm" %} {% else %}={% endif %}00:02:00{: .language-bash} {: .solution} {: .challenge}
{% include links.md %}