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<h1>Using shared resources responsibly</h1>
<p>Last updated on 2026-04-14 |
<a href="https://github.com/carpentries-incubator/hpc-intro/edit/main/episodes/19-responsibility.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
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<button role="button" aria-pressed="false" tabindex="0" id="expand-code" class="pull-right" data-expand="Expand All Solutions " data-collapse="Collapse All Solutions "> Expand All Solutions <i aria-hidden="true" data-feather="plus"></i></button>
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<div class="overview card">
<h2 class="card-header">Overview</h2>
<div class="row g-0">
<div class="col-md-4">
<div class="card-body">
<div class="inner">
<h3 class="card-title">Questions</h3>
<ul><li>How can I be a responsible user?</li>
<li>How can I protect my data?</li>
<li>How can I best get large amounts of data off an HPC system?</li>
</ul></div>
</div>
</div>
<div class="col-md-8">
<div class="card-body">
<div class="inner bordered">
<h3 class="card-title">Objectives</h3>
<ul><li>Describe how the actions of a single user can affect the experience
of others on a shared system.</li>
<li>Discuss the behaviour of a considerate shared system citizen.</li>
<li>Explain the importance of backing up critical data.</li>
<li>Describe the challenges with transferring large amounts of data off
HPC systems.</li>
<li>Convert many files to a single archive file using tar.</li>
</ul></div>
</div>
</div>
</div>
</div>
<p>One of the major differences between using remote HPC resources and
your own system (e.g. your laptop) is that remote resources are shared.
How many users the resource is shared between at any one time varies
from system to system, but it is unlikely you will ever be the only user
logged into or using such a system.</p>
<p>The widespread usage of scheduling systems where users submit jobs on
HPC resources is a natural outcome of the shared nature of these
resources. There are other things you, as an upstanding member of the
community, need to consider.</p>
<section><h2 class="section-heading" id="be-kind-to-the-login-nodes">Be Kind to the Login Nodes<a class="anchor" aria-label="anchor" href="#be-kind-to-the-login-nodes"></a></h2>
<hr class="half-width"><p>The login node is often busy managing all of the logged in users,
creating and editing files and compiling software. If the machine runs
out of memory or processing capacity, it will become very slow and
unusable for everyone. While the machine is meant to be used, be sure to
do so responsibly – in ways that will not adversely impact other users’
experience.</p>
<p>Login nodes are always the right place to launch jobs. Cluster
policies vary, but they may also be used for proving out workflows, and
in some cases, may host advanced cluster-specific debugging or
development tools. The cluster may have modules that need to be loaded,
possibly in a certain order, and paths or library versions that differ
from your laptop, and doing an interactive test run on the head node is
a quick and reliable way to discover and fix these issues.</p>
<div id="login-nodes-are-a-shared-resource" class="callout">
<div class="callout-square">
<i class="callout-icon" data-feather="bell"></i>
</div>
<span class="callout-header">Callout</span>
<div id="login-nodes-are-a-shared-resource" class="callout-inner">
<h3 class="callout-title">Login Nodes Are a Shared Resource</h3>
<div class="callout-content">
<p>Remember, the login node is shared with all other users and your
actions could cause issues for other people. Think carefully about the
potential implications of issuing commands that may use large amounts of
resource.</p>
<p>Unsure? Ask your friendly systems administrator (“sysadmin”) if the
thing you’re contemplating is suitable for the login node, or if there’s
another mechanism to get it done safely.</p>
</div>
</div>
</div>
<p>You can always use the commands <code>top</code> and
<code>ps ux</code> to list the processes that are running on the login
node along with the amount of CPU and memory they are using. If this
check reveals that the login node is somewhat idle, you can safely use
it for your non-routine processing task. If something goes wrong -- the
process takes too long, or doesn’t respond – you can use the
<code>kill</code> command along with the <em>PID</em> to terminate the
process.</p>
<div id="login-node-etiquette" class="callout challenge">
<div class="callout-square">
<i class="callout-icon" data-feather="zap"></i>
</div>
<span class="callout-header">Challenge</span>
<div id="login-node-etiquette" class="callout-inner">
<h3 class="callout-title">Login Node Etiquette</h3>
<div class="callout-content">
<p>Which of these commands would be a routine task to run on the login
node?</p>
<ol style="list-style-type: decimal"><li><code>python physics_sim.py</code></li>
<li><code>make</code></li>
<li><code>create_directories.sh</code></li>
<li><code>molecular_dynamics_2</code></li>
<li><code>tar -xzf R-3.3.0.tar.gz</code></li>
</ol></div>
</div>
</div>
<div id="accordionSolution1" class="accordion challenge-accordion accordion-flush">
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</button>
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<div class="accordion-body">
<p>Building software, creating directories, and unpacking software are
common and acceptable > tasks for the login node: options #2
(<code>make</code>), #3 (<code>mkdir</code>), and #5 (<code>tar</code>)
are probably OK. Note that script names do not always reflect their
contents: before launching #3, please
<code>less create_directories.sh</code> and make sure it’s not a Trojan
horse.</p>
<p>Running resource-intensive applications is frowned upon. Unless you
are sure it will not affect other users, do not run jobs like #1
(<code>python</code>) or #4 (custom MD code). If you’re unsure, ask your
friendly sysadmin for advice.</p>
</div>
</div>
</div>
</div>
<p>If you experience performance issues with a login node you should
report it to the system staff (usually via the helpdesk) for them to
investigate.</p>
</section><section><h2 class="section-heading" id="test-before-scaling">Test Before Scaling<a class="anchor" aria-label="anchor" href="#test-before-scaling"></a></h2>
<hr class="half-width"><p>Remember that you are generally charged for usage on shared systems.
A simple mistake in a job script can end up costing a large amount of
resource budget. Imagine a job script with a mistake that makes it sit
doing nothing for 24 hours on 1000 cores or one where you have requested
2000 cores by mistake and only use 100 of them! This problem can be
compounded when people write scripts that automate job submission (for
example, when running the same calculation or analysis over lots of
different parameters or files). When this happens it hurts both you (as
you waste lots of charged resource) and other users (who are blocked
from accessing the idle compute nodes). On very busy resources you may
wait many days in a queue for your job to fail within 10 seconds of
starting due to a trivial typo in the job script. This is extremely
frustrating!</p>
<p>Most systems provide dedicated resources for testing that have short
wait times to help you avoid this issue.</p>
<div id="test-job-submission-scripts-that-use-large-amounts-of-resources" class="callout">
<div class="callout-square">
<i class="callout-icon" data-feather="bell"></i>
</div>
<span class="callout-header">Callout</span>
<div id="test-job-submission-scripts-that-use-large-amounts-of-resources" class="callout-inner">
<h3 class="callout-title">Test Job Submission Scripts That Use Large
Amounts of Resources</h3>
<div class="callout-content">
<p>Before submitting a large run of jobs, submit one as a test first to
make sure everything works as expected.</p>
<p>Before submitting a very large or very long job submit a short
truncated test to ensure that the job starts as expected.</p>
</div>
</div>
</div>
</section><section><h2 class="section-heading" id="have-a-backup-plan">Have a Backup Plan<a class="anchor" aria-label="anchor" href="#have-a-backup-plan"></a></h2>
<hr class="half-width"><p>Although many HPC systems keep backups, it does not always cover all
the file systems available and may only be for disaster recovery
purposes (i.e. for restoring the whole file system if lost rather than
an individual file or directory you have deleted by mistake). Protecting
critical data from corruption or deletion is primarily your
responsibility: keep your own backup copies.</p>
<p>Version control systems (such as Git) often have free, cloud-based
offerings (e.g., GitHub and GitLab) that are generally used for storing
source code. Even if you are not writing your own programs, these can be
very useful for storing job scripts, analysis scripts and small input
files.</p>
<p>If you are building software, you may have a large amount of source
code that you compile to build your executable. Since this data can
generally be recovered by re-downloading the code, or re-running the
checkout operation from the source code repository, this data is also
less critical to protect.</p>
<p>For larger amounts of data, especially important results from your
runs, which may be irreplaceable, you should make sure you have a robust
system in place for taking copies of data off the HPC system wherever
possible to backed-up storage. Tools such as <code>rsync</code> can be
very useful for this.</p>
<p>Your access to the shared HPC system will generally be time-limited
so you should ensure you have a plan for transferring your data off the
system before your access finishes. The time required to transfer large
amounts of data should not be underestimated and you should ensure you
have planned for this early enough (ideally, before you even start using
the system for your research).</p>
<p>In all these cases, the helpdesk of the system you are using should
be able to provide useful guidance on your options for data transfer for
the volumes of data you will be using.</p>
<div id="your-data-is-your-responsibility" class="callout">
<div class="callout-square">
<i class="callout-icon" data-feather="bell"></i>
</div>
<span class="callout-header">Callout</span>
<div id="your-data-is-your-responsibility" class="callout-inner">
<h3 class="callout-title">Your Data Is Your Responsibility</h3>
<div class="callout-content">
<p>Make sure you understand what the backup policy is on the file
systems on the system you are using and what implications this has for
your work if you lose your data on the system. Plan your backups of
critical data and how you will transfer data off the system throughout
the project.</p>
</div>
</div>
</div>
</section><section><h2 class="section-heading" id="transferring-data">Transferring Data<a class="anchor" aria-label="anchor" href="#transferring-data"></a></h2>
<hr class="half-width"><p>As mentioned above, many users run into the challenge of transferring
large amounts of data off HPC systems at some point (this is more often
in transferring data off than onto systems but the advice below applies
in either case). Data transfer speed may be limited by many different
factors so the best data transfer mechanism to use depends on the type
of data being transferred and where the data is going.</p>
<p>The components between your data’s source and destination have
varying levels of performance, and in particular, may have different
capabilities with respect to <strong>bandwidth</strong> and
<strong>latency</strong>.</p>
<p><strong>Bandwidth</strong> is generally the raw amount of data per
unit time a device is capable of transmitting or receiving. It’s a
common and generally well-understood metric.</p>
<p><strong>Latency</strong> is a bit more subtle. For data transfers, it
may be thought of as the amount of time it takes to get data out of
storage and into a transmittable form. Latency issues are the reason
it’s advisable to execute data transfers by moving a small number of
large files, rather than the converse.</p>
<p>Some of the key components and their associated issues are:</p>
<ul><li>
<strong>Disk speed</strong>: File systems on HPC systems are often
highly parallel, consisting of a very large number of high performance
disk drives. This allows them to support a very high data bandwidth.
Unless the remote system has a similar parallel file system you may find
your transfer speed limited by disk performance at that end.</li>
<li>
<strong>Meta-data performance</strong>: <em>Meta-data
operations</em> such as opening and closing files or listing the owner
or size of a file are much less parallel than read/write operations. If
your data consists of a very large number of small files you may find
your transfer speed is limited by meta-data operations. Meta-data
operations performed by other users of the system can also interact
strongly with those you perform so reducing the number of such
operations you use (by combining multiple files into a single file) may
reduce variability in your transfer rates and increase transfer
speeds.</li>
<li>
<strong>Network speed</strong>: Data transfer performance can be
limited by network speed. More importantly it is limited by the slowest
section of the network between source and destination. If you are
transferring to your laptop/workstation, this is likely to be its
connection (either via LAN or WiFi).</li>
<li>
<strong>Firewall speed</strong>: Most modern networks are protected
by some form of firewall that filters out malicious traffic. This
filtering has some overhead and can result in a reduction in data
transfer performance. The needs of a general purpose network that hosts
email/web-servers and desktop machines are quite different from a
research network that needs to support high volume data transfers. If
you are trying to transfer data to or from a host on a general purpose
network you may find the firewall for that network will limit the
transfer rate you can achieve.</li>
</ul><p>As mentioned above, if you have related data that consists of a large
number of small files it is strongly recommended to pack the files into
a larger <em>archive</em> file for long term storage and transfer. A
single large file makes more efficient use of the file system and is
easier to move, copy and transfer because significantly fewer metadata
operations are required. Archive files can be created using tools like
<code>tar</code> and <code>zip</code>. We have already met
<code>tar</code> when we talked about data transfer earlier.</p>
<figure><img src="/fig/responsibility-bandwidth.svg" max-width="90%" alt="Schematic of network bandwidth" class="figure mx-auto d-block"><div class="figcaption">Schematic diagram of bandwidth and latency for
disk and network I/O. Each of the components on the figure is connected
by a blue line of width proportional to the interface bandwidth. The
small mazes at the link points illustrate the latency of the link, with
more tortuous mazes indicating higher latency.</div>
</figure><div id="consider-the-best-way-to-transfer-data" class="callout challenge">
<div class="callout-square">
<i class="callout-icon" data-feather="zap"></i>
</div>
<span class="callout-header">Challenge</span>
<div id="consider-the-best-way-to-transfer-data" class="callout-inner">
<h3 class="callout-title">Consider the Best Way to Transfer Data</h3>
<div class="callout-content">
<p>If you are transferring large amounts of data you will need to think
about what may affect your transfer performance. It is always useful to
run some tests that you can use to extrapolate how long it will take to
transfer your data.</p>
<p>Say you have a “data” folder containing 10,000 or so files, a healthy
mix of small and large ASCII and binary data. Which of the following
would be the best way to transfer them to HPC Carpentry’s Cloud
Cluster?</p>
<ol style="list-style-type: decimal"><li><p><code>scp -r data yourUsername@cluster.hpc-carpentry.org:~/</code></p></li>
<li><p><code>rsync -ra data yourUsername@cluster.hpc-carpentry.org:~/</code></p></li>
<li><p><code>rsync -raz data yourUsername@cluster.hpc-carpentry.org:~/</code></p></li>
<li>
<p><code>tar -cvf data.tar data;</code></p>
<p><code>rsync -raz data.tar yourUsername@cluster.hpc-carpentry.org:~/</code></p>
</li>
<li>
<p><code>tar -cvzf data.tar.gz data;</code></p>
<p><code>rsync -ra data.tar.gz yourUsername@cluster.hpc-carpentry.org:~/</code></p>
</li>
</ol></div>
</div>
</div>
<div id="accordionSolution2" class="accordion challenge-accordion accordion-flush">
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<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
<div class="accordion-body">
<ol style="list-style-type: decimal"><li>
<code>scp</code> will recursively copy the directory. This works,
but without compression.</li>
<li>
<code>rsync -ra</code> works like <code>scp -r</code>, but preserves
file information like creation times. This is marginally better.</li>
<li>
<code>rsync -raz</code> adds compression, which will save some
bandwidth. If you have a strong CPU at both ends of the line, and you’re
on a slow network, this is a good choice.</li>
<li>This command first uses <code>tar</code> to merge everything into a
single file, then <code>rsync -z</code> to transfer it with compression.
With this large <em>number</em> of files, metadata overhead can hamper
your transfer, so this is a good idea.</li>
<li>This command uses <code>tar -z</code> to compress the archive, then
<code>rsync</code> to transfer it. This may perform similarly to #4, but
in most cases (for large datasets), it’s the best combination of high
throughput and low latency (making the most of your time and network
connection).</li>
</ol></div>
</div>
</div>
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<span class="callout-header">Key Points</span>
<div class="callout-inner">
<div class="callout-content">
<ul><li>Be careful how you use the login node.</li>
<li>Your data on the system is your responsibility.</li>
<li>Plan and test large data transfers.</li>
<li>It is often best to convert many files to a single archive file
before transferring.</li>
</ul></div>
</div>
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