Introduction to High-Performance Computing

Why use an HPC System?

Overview

Teaching: 15 min
Exercises: 5 min
Questions

  • Why would I be interested in High Performance Computing (HPC)?

  • What can I expect to learn from this course?

Objectives

  • Be able to describe what an HPC system is

  • Identify how an HPC system could benefit you.

Frequently, research problems that use computing can outgrow the capabilities of the desktop or laptop computer where they started:

In all these cases, access to more (and larger) computers is needed. Those computers should be usable at the same time, solving many researchers’ problems in parallel.

Break the Ice

Talk to your neighbour, office mate or rubber duck about your research.

  • How does computing help you do your research?
  • How could more computing help you do more or better research?

A Standard Laptop for Standard Tasks

Today, people coding or analysing data typically work with laptops.

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Let’s dissect what resources programs running on a laptop require:

Schematically, this can be reduced to the following:

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When Tasks Take Too Long

When the task to solve becomes heavy on computations, the operations are typically out-sourced from the local laptop or desktop to elsewhere. Take for example the task to find the directions for your next vacation. The capabilities of your laptop are typically not enough to calculate that route spontaneously: finding the shortest path through a network runs on the order of (v log v) time, where v (vertices) represents the number of intersections in your map. Instead of doing this yourself, you use a website, which in turn runs on a server, that is almost definitely not in the same room as you are.

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Note here, that a server is mostly a noisy computer mounted into a rack cabinet which in turn resides in a data center. The internet made it possible that these data centers do not require to be nearby your laptop. What people call the cloud is mostly a web-service where you can rent such servers by providing your credit card details and requesting remote resources that satisfy your requirements. This is often handled through an online, browser-based interface listing the various machines available and their capacities in terms of processing power, memory, and storage.

The server itself has no direct display or input methods attached to it. But most importantly, it has much more storage, memory and compute capacity than your laptop will ever have. In any case, you need a local device (laptop, workstation, mobile phone or tablet) to interact with this remote machine, which people typically call ‘a server’.

When One Server Is Not Enough

If the computational task or analysis to complete is daunting for a single server, larger agglomerations of servers are used. These go by the name of “clusters” or “super computers”.

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The methodology of providing the input data, configuring the program options, and retrieving the results is quite different to using a plain laptop. Moreover, using a graphical interface is often discarded in favor of using the command line. This imposes a double paradigm shift for prospective users asked to

  1. work with the command line interface (CLI), rather than a graphical user interface (GUI)
  2. work with a distributed set of computers (called nodes) rather than the machine attached to their keyboard & mouse

I’ve Never Used a Server, Have I?

Take a minute and think about which of your daily interactions with a computer may require a remote server or even cluster to provide you with results.

Some Ideas

  • Checking email: your computer (possibly in your pocket) contacts a remote machine, authenticates, and downloads a list of new messages; it also uploads changes to message status, such as whether you read, marked as junk, or deleted the message. Since yours is not the only account, the mail server is probably one of many in a data center.
  • Searching for a phrase online involves comparing your search term against a massive database of all known sites, looking for matches. This “query” operation can be straightforward, but building that database is a monumental task! Servers are involved at every step.
  • Searching for directions on a mapping website involves connecting your (A) starting and (B) end points by traversing a graph in search of the “shortest” path by distance, time, expense, or another metric. Converting a map into the right form is relatively simple, but calculating all the possible routes between A and B is expensive.

Checking email could be serial: your machine connects to one server and exchanges data. Searching by querying the database for your search term (or endpoints) could also be serial, in that one machine receives your query and returns the result. However, assembling and storing the full database is far beyond the capability of any one machine. Therefore, these functions are served in parallel by a large, “hyperscale” collection of servers working together.

Key Points

  • High Performance Computing (HPC) typically involves connecting to very large computing systems elsewhere in the world.

  • These other systems can be used to do work that would either be impossible or much slower on smaller systems.

  • The standard method of interacting with such systems is via a command line interface called Bash.

Working on a remote HPC system

Overview

Teaching: 25 min
Exercises: 10 min
Questions

  • What is an HPC system?

  • How does an HPC system work?

  • How do I log on to a remote HPC system?

Objectives

  • Connect to a remote HPC system.

  • Understand the general HPC system architecture.

What Is an HPC System?

The words “cloud”, “cluster”, and the phrase “high-performance computing” or “HPC” are used a lot in different contexts and with various related meanings. So what do they mean? And more importantly, how do we use them in our work?

The cloud is a generic term commonly used to refer to computing resources that are a) provisioned to users on demand or as needed and b) represent real or virtual resources that may be located anywhere on Earth. For example, a large company with computing resources in Brazil, Zimbabwe and Japan may manage those resources as its own internal cloud and that same company may also utilize commercial cloud resources provided by Amazon or Google. Cloud resources may refer to machines performing relatively simple tasks such as serving websites, providing shared storage, providing web services (such as e-mail or social media platforms), as well as more traditional compute intensive tasks such as running a simulation.

The term HPC system, on the other hand, describes a stand-alone resource for computationally intensive workloads. They are typically comprised of a multitude of integrated processing and storage elements, designed to handle high volumes of data and/or large numbers of floating-point operations (FLOPS) with the highest possible performance. For example, all of the machines on the Top-500 list are HPC systems. To support these constraints, an HPC resource must exist in a specific, fixed location: networking cables can only stretch so far, and electrical and optical signals can travel only so fast.

The word “cluster” is often used for small to moderate scale HPC resources less impressive than the Top-500. Clusters are often maintained in computing centers that support several such systems, all sharing common networking and storage to support common compute intensive tasks.

Logging In

The first step in using a cluster is to establish a connection from our laptop to the cluster. When we are sitting at a computer (or standing, or holding it in our hands or on our wrists), we have come to expect a visual display with icons, widgets, and perhaps some windows or applications: a graphical user interface, or GUI. Since computer clusters are remote resources that we connect to over often slow or laggy interfaces (WiFi and VPNs especially), it is more practical to use a command-line interface, or CLI, in which commands and results are transmitted via text, only. Anything other than text (images, for example) must be written to disk and opened with a separate program.

If you have ever opened the Windows Command Prompt or macOS Terminal, you have seen a CLI. If you have already taken The Carpentries’ courses on the UNIX Shell or Version Control, you have used the CLI on your local machine somewhat extensively. The only leap to be made here is to open a CLI on a remote machine, while taking some precautions so that other folks on the network can’t see (or change) the commands you’re running or the results the remote machine sends back. We will use the Secure SHell protocol (or SSH) to open an encrypted network connection between two machines, allowing you to send & receive text and data without having to worry about prying eyes.

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Make sure you have a SSH client installed on your laptop. Refer to the setup section for more details. SSH clients are usually command-line tools, where you provide the remote machine address as the only required argument. If your username on the remote system differs from what you use locally, you must provide that as well. If your SSH client has a graphical front-end, such as PuTTY or MobaXterm, you will set these arguments before clicking “connect.” From the terminal, you’ll write something like ssh userName@hostname, where the “@” symbol is used to separate the two parts of a single argument.

Go ahead and open your terminal or graphical SSH client, then log in to the cluster using your username and the remote computer you can reach from the outside world, EPCC, The University of Edinburgh.

[user@laptop ~]$ ssh userid@login.archer2.ac.uk

Remember to replace userid with your username or the one supplied by the instructors. You may be asked for your password. Watch out: the characters you type after the password prompt are not displayed on the screen. Normal output will resume once you press Enter.

Where Are We?

Very often, many users are tempted to think of a high-performance computing installation as one giant, magical machine. Sometimes, people will assume that the computer they’ve logged onto is the entire computing cluster. So what’s really happening? What computer have we logged on to? The name of the current computer we are logged onto can be checked with the hostname command. (You may also notice that the current hostname is also part of our prompt!)

userid@uan01:~> hostname

uan01

What’s in Your Home Directory?

The system administrators may have configured your home directory with some helpful files, folders, and links (shortcuts) to space reserved for you on other filesystems. Take a look around and see what you can find.

Hint: The shell commands pwd and ls may come in handy.

Home directory contents vary from user to user. Please discuss any differences you spot with your neighbors:

It’s a Beautiful Day in the Neighborhood

The deepest layer should differ: userid is uniquely yours. Are there differences in the path at higher levels?

If both of you have empty directories, they will look identical. If you or your neighbor has used the system before, there may be differences. What are you working on?

Solution

Use pwd to print the working directory path:

userid@uan01:~> pwd

You can run ls to list the directory contents, though it’s possible nothing will show up (if no files have been provided). To be sure, use the -a flag to show hidden files, too.

userid@uan01:~> ls -a

At a minimum, this will show the current directory as ., and the parent directory as ...

Nodes

Individual computers that compose a cluster are typically called nodes (although you will also hear people call them servers, computers and machines). On a cluster, there are different types of nodes for different types of tasks. The node where you are right now is called the head node, login node, landing pad, or submit node. A login node serves as an access point to the cluster.

As a gateway, it is well suited for uploading and downloading files, setting up software, and running quick tests. Generally speaking, the login node should not be used for time-consuming or resource-intensive tasks. You should be alert to this, and check with your site’s operators or documentation for details of what is and isn’t allowed. In these lessons, we will avoid running jobs on the head node.

Dedicated Transfer Nodes

If you want to transfer larger amounts of data to or from the cluster, some systems offer dedicated nodes for data transfers only. The motivation for this lies in the fact that larger data transfers should not obstruct operation of the login node for anybody else. Check with your cluster’s documentation or its support team if such a transfer node is available. As a rule of thumb, consider all transfers of a volume larger than 500 MB to 1 GB as large. But these numbers change, e.g., depending on the network connection of yourself and of your cluster or other factors.

The real work on a cluster gets done by the worker (or compute) nodes. Worker nodes come in many shapes and sizes, but generally are dedicated to long or hard tasks that require a lot of computational resources.

All interaction with the worker nodes is handled by a specialized piece of software called a scheduler (the scheduler used in this lesson is called Slurm). We’ll learn more about how to use the scheduler to submit jobs next, but for now, it can also tell us more information about the worker nodes.

For example, we can view all of the worker nodes by running the command sinfo.

userid@uan01:~> sinfo

PARTITION AVAIL  TIMELIMIT  NODES  STATE NODELIST 
standard     up 1-00:00:00     27 drain* nid[001029,001050,001149,001363,001366,001391,001552,001568,001620,001642,001669,001672-001675,001688,001690-001691,001747,001751,001783,001793,001812,001832-001835] 
standard     up 1-00:00:00      5  down* nid[001024,001026,001064,001239,001898] 
standard     up 1-00:00:00      8  drain nid[001002,001028,001030-001031,001360-001362,001745] 
standard     up 1-00:00:00    945  alloc nid[001000-001001,001003-001023,001025,001027,001032-001037,001040-001049,001051-001063,001065-001108,001110-001145,001147,001150-001238,001240-001264,001266-001271,001274-001334,001337-001359,001364-001365,001367-001390,001392-001551,001553-001567,001569-001619,001621-001637,001639-001641,001643-001668,001670-001671,001676,001679-001687,001692-001734,001736-001744,001746,001748-001750,001752-001782,001784-001792,001794-001811,001813-001824,001826-001831,001836-001890,001892-001897,001899-001918,001920,001923-001934,001936-001945,001947-001965,001967-001981,001984-001991,002006-002023] 
standard     up 1-00:00:00     37   resv nid[001038-001039,001109,001146,001148,001265,001272-001273,001335-001336,001638,001677-001678,001735,001891,001919,001921-001922,001935,001946,001966,001982-001983,001992-002005]

There are also specialized machines used for managing disk storage, user authentication, and other infrastructure-related tasks. Although we do not typically logon to or interact with these machines directly, they enable a number of key features like ensuring our user account and files are available throughout the HPC system.

What’s in a Node?

All of the nodes in an HPC system have the same components as your own laptop or desktop: CPUs (sometimes also called processors or cores), memory (or RAM), and disk space. CPUs are a computer’s tool for actually running programs and calculations. Information about a current task is stored in the computer’s memory. Disk refers to all storage that can be accessed like a file system. This is generally storage that can hold data permanently, i.e. data is still there even if the computer has been restarted. While this storage can be local (a hard drive installed inside of it), it is more common for nodes to connect to a shared, remote fileserver or cluster of servers.

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Explore Your Computer

Try to find out the number of CPUs and amount of memory available on your personal computer.

Note that, if you’re logged in to the remote computer cluster, you need to log out first. To do so, type Ctrl+d or exit:

userid@uan01:~> exit
[user@laptop ~]$

Solution

There are several ways to do this. Most operating systems have a graphical system monitor, like the Windows Task Manager. More detailed information can sometimes be found on the command line. For example, some of the commands used on a Linux system are:

  • Run system utilities 
    [user@laptop ~]$ nproc --all
[user@laptop ~]$ free -m
  • Read from /proc 
    [user@laptop ~]$ cat /proc/cpuinfo
[user@laptop ~]$ cat /proc/meminfo
  • Run system monitor 
    [user@laptop ~]$ htop

Explore the login node

Now compare the resources of your computer with those of the head node.

Solution

[user@laptop ~]$ ssh userid@login.archer2.ac.uk
userid@uan01:~> nproc --all
userid@uan01:~> free -m

You can get more information about the processors using lscpu, and a lot of detail about the memory by reading the file /proc/meminfo:

userid@uan01:~> less /proc/meminfo

You can also explore the available filesystems using df to show disk free space. The -h flag renders the sizes in a human-friendly format, i.e., GB instead of B. The type flag -T shows what kind of filesystem each resource is.

userid@uan01:~> df -Th

The local filesystems (ext, tmp, xfs, zfs) will depend on whether you’re on the same login node (or compute node, later on). Networked filesystems (beegfs, cifs, gpfs, nfs, pvfs) will be similar — but may include userid, depending on how it is mounted.

Shared Filesystems

This is an important point to remember: files saved on one node (computer) are often available everywhere on the cluster!

Explore a Worker Node

Finally, let’s look at the resources available on the worker nodes where your jobs will actually run. Try running this command to see the name, CPUs and memory available on one of the worker nodes:

 sinfo -n nid001053 -o "%n %c %m"

Compare Your Computer, the login node and the compute node

Compare your laptop’s number of processors and memory with the numbers you see on the cluster head node and worker node. Discuss the differences with your neighbor.

What implications do you think the differences might have on running your research work on the different systems and nodes?

Differences Between Nodes

Many HPC clusters have a variety of nodes optimized for particular workloads. Some nodes may have larger amount of memory, or specialized resources such as Graphical Processing Units (GPUs).

With all of this in mind, we will now cover how to talk to the cluster’s scheduler, and use it to start running our scripts and programs!

Key Points

  • An HPC system is a set of networked machines.

  • HPC systems typically provide login nodes and a set of worker nodes.

  • The resources found on independent (worker) nodes can vary in volume and type (amount of RAM, processor architecture, availability of network mounted filesystems, etc.).

  • Files saved on one node are available on all nodes.

Break

Overview

Teaching: min
Exercises: min
Questions
Objectives

Comfort break

Key Points

Working with the scheduler

Overview

Teaching: 50 min
Exercises: 30 min
Questions

  • What is a scheduler and why are they used?

  • How do I launch a program to run on any one node in the cluster?

  • How do I capture the output of a program that is run on a node in the cluster?

Objectives

  • Run a simple Hello World style program on the cluster.

  • Submit a simple Hello World style script to the cluster.

  • Use the batch system command line tools to monitor the execution of your job.

  • Inspect the output and error files of your jobs.

Job Scheduler

An HPC system might have thousands of nodes and thousands of users. How do we decide who gets what and when? How do we ensure that a task is run with the resources it needs? This job is handled by a special piece of software called the scheduler. On an HPC system, the scheduler manages which jobs run where and when.

The following illustration compares these tasks of a job scheduler to a waiter in a restaurant. If you can relate to an instance where you had to wait for a while in a queue to get in to a popular restaurant, then you may now understand why sometimes your job do not start instantly as in your laptop.

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The scheduler used in this lesson is Slurm. Although Slurm is not used everywhere, running jobs is quite similar regardless of what software is being used. The exact syntax might change, but the concepts remain the same.

Running a Batch Job

The most basic use of the scheduler is to run a command non-interactively. Any command (or series of commands) that you want to run on the cluster is called a job, and the process of using a scheduler to run the job is called batch job submission.

In this case, the job we want to run is just a shell script. Let’s create a demo shell script to run as a test. The landing pad will have a number of terminal-based text editors installed. Use whichever you prefer. Unsure? nano is a pretty good, basic choice.

userid@uan01:~> nano example-job.sh
userid@uan01:~> chmod +x example-job.sh
userid@uan01:~> cat example-job.sh

#!/bin/bash

echo -n "This script is running on "
hostname

Creating Our Test Job

Run the script. Does it execute on the cluster or just our login node?

Solution

userid@uan01:~> ./example-job.sh

This script is running on uan01

This job runs on the login node.

If you completed the previous challenge successfully, you probably realise that there is a distinction between running the job through the scheduler and just “running it”. To submit this job to the scheduler, we use the sbatch command.

userid@uan01:~> sbatch --partition=standard --qos=standard --reservation=ta028_180 example-job.sh

sbatch: Warning: Your job has no time specification (--time=) and the default time is short. You can cancel your job with 'scancel <JOB_ID>' if you wish to resubmit.
sbatch: Warning: It appears your working directory may be on the home filesystem. It is /home2/home/ta028/ta028/userid. This is not available from the compute nodes - please check that this is what you intended. You can cancel your job with 'scancel <JOBID>' if you wish to resubmit.
Submitted batch job 286949

Ah! What went wrong here? Slurm is telling us that the file system we are currently on, /home, is not available on the compute nodes and that we are getting the default, short runtime. We will deal with the runtime later, but we need to move to a different file system to submit the job and have it visible to the compute nodes. On ARCHER2, this is the /work file system. The path is similar to home but with /work at the start. Lets move there now, copy our job script across and resubmit:

userid@uan01:~> cd /work/ta028/ta028/userid
userid@uan01:/work/ta028/ta028/userid> cp ~/example-job.sh .
userid@uan01:/work/ta028/ta028/userid> sbatch --partition=standard --qos=standard --reservation=ta028_180 example-job.sh

Submitted batch job 36855

That’s better! And that’s all we need to do to submit a job. Our work is done — now the scheduler takes over and tries to run the job for us. While the job is waiting to run, it goes into a list of jobs called the queue. To check on our job’s status, we check the queue using the command squeue -u userid.

userid@uan01:/work/ta028/ta028/userid> squeue -u userid

JOBID USER         ACCOUNT     NAME           ST REASON START_TIME         T...
36856 yourUsername yourAccount example-job.sh R  None   2017-07-01T16:47:02 ...

We can see all the details of our job, most importantly that it is in the R or RUNNING state. Sometimes our jobs might need to wait in a queue (PENDING) or have an error (E).

The best way to check our job’s status is with squeue. Of course, running squeue repeatedly to check on things can be a little tiresome. To see a real-time view of our jobs, we can use the watch command. watch reruns a given command at 2-second intervals. This is too frequent, and will likely upset your system administrator. You can change the interval to a more reasonable value, for example 15 seconds, with the -n 15 parameter. Let’s try using it to monitor another job.

userid@uan01:/work/ta028/ta028/userid> sbatch --partition=standard --qos=standard --reservation=ta028_180 example-job.sh
userid@uan01:/work/ta028/ta028/userid> watch -n 15 squeue -u userid

You should see an auto-updating display of your job’s status. When it finishes, it will disappear from the queue. Press Ctrl-c when you want to stop the watch command.

Where’s the Output?

On the login node, this script printed output to the terminal — but when we exit watch, there’s nothing. Where’d it go?

HPC job output is typically redirected to a file in the directory you launched it from. Use ls to find and read the file.

Customising a Job

The job we just ran used some of the scheduler’s default options. In a real-world scenario, that’s probably not what we want. The default options represent a reasonable minimum. Chances are, we will need more cores, more memory, more time, among other special considerations. To get access to these resources we must customize our job script.

Comments in UNIX shell scripts (denoted by #) are typically ignored, but there are exceptions. For instance the special #! comment at the beginning of scripts specifies what program should be used to run it (you’ll typically see #!/bin/bash). Schedulers like Slurm also have a special comment used to denote special scheduler-specific options. Though these comments differ from scheduler to scheduler, Slurm’s special comment is #SBATCH. Anything following the #SBATCH comment is interpreted as an instruction to the scheduler.

Let’s illustrate this by example. By default, a job’s name is the name of the script, but the --job-name option can be used to change the name of a job. Add an option to the script:

userid@uan01:/work/ta028/ta028/userid> cat example-job.sh

#!/bin/bash
#SBATCH --job-name new_name

echo -n "This script is running on "
hostname
echo "This script has finished successfully."

Submit the job and monitor its status:

userid@uan01:/work/ta028/ta028/userid> sbatch --partition=standard --qos=standard --reservation=ta028_180 example-job.sh
userid@uan01:/work/ta028/ta028/userid> squeue -u userid

JOBID USER         ACCOUNT     NAME     ST REASON   START_TIME TIME TIME_LEFT NODES CPUS
38191 yourUsername yourAccount new_name PD Priority N/A        0:00 1:00:00   1     1

Fantastic, we’ve successfully changed the name of our job!

Resource Requests

But what about more important changes, such as the number of cores and memory for our jobs? One thing that is absolutely critical when working on an HPC system is specifying the resources required to run a job. This allows the scheduler to find the right time and place to schedule our job. If you do not specify requirements (such as the amount of time you need), you will likely be stuck with your site’s default resources, which is probably not what you want.

The following are several key resource requests:

Note that just requesting these resources does not make your job run faster, nor does it necessarily mean that you will consume all of these resources. It only means that these are made available to you. Your job may end up using less memory, or less time, or fewer tasks or nodes, than you have requested, and it will still run.

It’s best if your requests accurately reflect your job’s requirements. We’ll talk more about how to make sure that you’re using resources effectively in a later episode of this lesson.

Command line options or job script options?

All of the options we specify can be supplied on the command line (as we do here for --partition=standard) or in the job script (as we have done for the job name above). These are interchangeable. It is often more convenient to put the options in the job script as it avoids lots of typing at the command line.

Submitting Resource Requests

Modify our hostname script so that it runs for a minute, then submit a job for it on the cluster. You should also move all the options we have been specifying on the command line (e.g. --partition) into the script at this point.

Solution

userid@uan01:/work/ta028/ta028/userid> cat example-job.sh

#!/bin/bash
#SBATCH --time 00:01:15
#SBATCH --partition=standard
#SBATCH --qos=standard
#SBATCH --reservation=ta028_180

echo -n "This script is running on "
sleep 60 # time in seconds
hostname
echo "This script has finished successfully."

userid@uan01:~> sbatch example-job.sh

Why are the Slurm runtime and sleep time not identical?

Job environment variables

When Slurm runs a job, it sets a number of environment variables for the job. One of these will let us check our work from the last problem. The SLURM_CPUS_PER_TASK variable is set to the number of CPUs we requested with -c. Using the SLURM_CPUS_PER_TASK variable, modify your job so that it prints how many CPUs have been allocated.

Resource requests are typically binding. If you exceed them, your job will be killed. Let’s use walltime as an example. We will request 30 seconds of walltime, and attempt to run a job for two minutes.

userid@uan01:/work/ta028/ta028/userid> cat example-job.sh

#!/bin/bash
#SBATCH --job-name long_job
#SBATCH --time 00:00:30
#SBATCH --partition=standard
#SBATCH --qos=standard
#SBATCH --reservation=ta028_180

echo "This script is running on ... "
sleep 120 # time in seconds
hostname
echo "This script has finished successfully."

Submit the job and wait for it to finish. Once it is has finished, check the log file.

userid@uan01:/work/ta028/ta028/userid> sbatch example-job.sh
userid@uan01:/work/ta028/ta028/userid> watch -n 15 squeue -u userid

userid@uan01:/work/ta028/ta028/userid> cat slurm-38193.out

This job is running on:
nid001147
slurmstepd: error: *** JOB 38193 ON cn01 CANCELLED AT 2017-07-02T16:35:48 DUE TO TIME LIMIT ***

Our job was killed for exceeding the amount of resources it requested. Although this appears harsh, this is actually a feature. Strict adherence to resource requests allows the scheduler to find the best possible place for your jobs. Even more importantly, it ensures that another user cannot use more resources than they’ve been given. If another user messes up and accidentally attempts to use all of the cores or memory on a node, Slurm will either restrain their job to the requested resources or kill the job outright. Other jobs on the node will be unaffected. This means that one user cannot mess up the experience of others, the only jobs affected by a mistake in scheduling will be their own.

But how much does it cost?

Although your job will be killed if it exceeds the selected runtime, a job that completes within the time limit is only charged for the time it actually used. However, you should always try and specify a wallclock limit that is close to (but greater than!) the expected runtime as this will enable your job to be scheduled more quickly. If you say your job will run for an hour, the scheduler has to wait until a full hour becomes free on the machine. If it only ever runs for 5 minutes, you could have set a limit of 10 minutes and it might have been run earlier in the gaps between other users’ jobs.

Cancelling a Job

Sometimes we’ll make a mistake and need to cancel a job. This can be done with the scancel command. Let’s submit a job and then cancel it using its job number (remember to change the walltime so that it runs long enough for you to cancel it before it is killed!).

userid@uan01:/work/ta028/ta028/userid> sbatch example-job.sh
userid@uan01:/work/ta028/ta028/userid> squeue -u userid

Submitted batch job 38759

JOBID USER         ACCOUNT     NAME           ST REASON   START_TIME TIME TIME_LEFT NODES CPUS
38759 yourUsername yourAccount example-job.sh PD Priority N/A        0:00 1:00      1     1

Now cancel the job with its job number (printed in your terminal). Absence of any job info indicates that the job has been successfully cancelled.

userid@uan01:/work/ta028/ta028/userid> scancel 38759
# It might take a minute for the job to disappear from the queue...
userid@uan01:/work/ta028/ta028/userid> squeue -u userid

JOBID  USER  ACCOUNT  NAME  ST  REASON  START_TIME  TIME  TIME_LEFT  NODES  CPUS

Cancelling multiple jobs

We can also cancel all of our jobs at once using the -u option. This will delete all jobs for a specific user (in this case us). Note that you can only delete your own jobs.

Try submitting multiple jobs and then cancelling them all with scancel -u yourUsername.

Other Types of Jobs

Up to this point, we’ve focused on running jobs in batch mode. Slurm also provides the ability to start an interactive session.

There are very frequently tasks that need to be done interactively. Creating an entire job script might be overkill, but the amount of resources required is too much for a login node to handle. A good example of this might be building a genome index for alignment with a tool like HISAT2. Fortunately, we can run these types of tasks as a one-off with srun.

srun runs a single command in the queue system and then exits. Let’s demonstrate this by running the hostname command with srun. (We can cancel an srun job with Ctrl-c.)

 srun --partition=standard --qos=standard --reservation=ta028_180 --time=00:01:00 hostname

nid001976

srun accepts all of the same options as sbatch. However, instead of specifying these in a script, these options are specified on the command-line when starting a job.

Typically, the resulting shell environment will be the same as that for sbatch.

Interactive jobs

Sometimes, you will need a lot of resource for interactive use. Perhaps it’s our first time running an analysis or we are attempting to debug something that went wrong with a previous job. Fortunately, SLURM makes it easy to start an interactive job with srun:

 srun --partition=standard --qos=standard --reservation=ta028_180 --pty /bin/bash

You should be presented with a bash prompt. Note that the prompt may change to reflect your new location, in this case the compute node we are logged on. You can also verify this with hostname.

When you are done with the interactive job, type exit to quit your session.

Running parallel jobs using MPI

As we have already seen, the power of HPC systems comes from parallelism, i.e. having lots of processors/disks etc. connected together rather than having more powerful components than your laptop or workstation. Often, when running research programs on HPC you will need to run a program that has been built to use the MPI (Message Passing Interface) parallel library. The MPI library allows programs to exploit multiple processing cores in parallel to allow researchers to model or simulate faster on larger problem sizes. The details of how MPI work are not important for this course or even to use programs that have been built using MPI; however, MPI programs typically have to be launched in job submission scripts in a different way to serial programs and users of parallel programs on HPC systems need to know how to do this. Specifically, launching parallel MPI programs typically requires four things:

To illustrate this process, we will use a simple MPI parallel program that sharpens an image. (We will meet this example program in more detail in a later episode.) Here is a job submission script that runs the sharpen program across two compute nodes on the cluster. Create a file (e.g. called: run-sharpen.slurm) with the contents of this script in it.

#!/bin/bash

#SBATCH --partition=standard
#SBATCH --qos=standard
#SBATCH --reservation=
#SBATCH --time=00:05:00

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=16

module load epcc-job-env
module load training/sharpen/1.0

cp $SHARPEN_INPUT/fuzzy.pgm .

srun --hint=nomultithread --distribution=block:block sharpen-mpi.x

The parallel launch line for the sharpen program can be seen towards the bottom of the script:

srun --hint=nomultithread --distribution=block:block sharpen-mpi.x

and this corresponds to the four required items we described above:

  1. Parallel launch program: in this case the parallel launch program is called srun; the additional argument controls which cores are used.
  2. Number of parallel processes per node: in this case this is 16, and is specified by the option --ntasks-per-node=16 option.
  3. Total number of parallel processes: in this case this is also 16, because we specified 1 node and 16 parallel processes per node.
  4. Our program and arguments: in this case this is sharpen-mpi.x.

As for our other jobs, we launch using the sbatch command.

userid@uan01:/work/ta028/ta028/userid> sbatch run-sharpen.slurm

If your job runs correctly, you should see an output file called sharpened.pgm

userid@uan01:/work/ta028/ta028/userid> ls -l *.pgm

-rw-r--r-- 1 userid ta028 1762743 Jun 26 17:29 fuzzy.pgm
-rw------- 1 userid ta028 1678630 Jun 26 17:33 sharpened.pgm

If you only see fuzzy.pgm and not sharpened.pgm then look at the job log files to work out what went wrong.

Running parallel jobs

Modify the sharpen script that you used above to use all 128 cores on one node. Check the output to confirm that it used the correct number of cores in parallel for the calculation.

Solution

Here is a modified script

#!/bin/bash

#SBATCH --partition=standard
#SBATCH --qos=standard
#SABTCH --reservation=ta028_180
#SBATCH --time=00:00:30

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=128

module load epcc-job-env
module load training/sharpen/1.0

cp $SHARPEN_INPUT/fuzzy.pgm .

srun --hint=nomultithread --distribution=block:block sharpen-mpi.x

Configuring parallel jobs

You will see in the job output that information is displayed about where each MPI process is running, in particular which node it is on.

Modify the sharpen script that you run a total of 2 nodes and 16 processes; but to use only 8 tasks on each of two nodes. Check the output file to ensure that you understand the job distribution.

Solution

#!/bin/bash

#SBATCH --partition=standard
#SBATCH --qos=standard
#SBATCH --time=00:00:30

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=8

module load epcc-job-env
module load training/sharpen/1.0

cp $SHARPEN_INPUT/fuzzy.pgm .

srun --hint=nomultithread --distribution=block:block sharpen-mpi.x

Key Points

  • The scheduler handles how compute resources are shared between users.

  • Everything you do should be run through the scheduler.

  • A job is just a shell script.

  • If in doubt, request more resources than you will need.

Lunch

Overview

Teaching: min
Exercises: min
Questions
Objectives

Lunch break

Key Points

Accessing software via Modules

Overview

Teaching: 30 min
Exercises: 15 min
Questions

  • How do we load and unload software packages?

Objectives

  • Understand how to load and use a software package.

On a high-performance computing system, it is seldom the case that the software we want to use is available when we log in. It is installed, but we will need to “load” it before it can run.

Before we start using individual software packages, however, we should understand the reasoning behind this approach. The three biggest factors are:

Software incompatibility is a major headache for programmers. Sometimes the presence (or absence) of a software package will break others that depend on it. Two of the most famous examples are Python 2 and 3 and C compiler versions. Python 3 famously provides a python command that conflicts with that provided by Python 2. Software compiled against a newer version of the C libraries and then used when they are not present will result in a nasty 'GLIBCXX_3.4.20' not found error, for instance.

Software versioning is another common issue. A team might depend on a certain package version for their research project - if the software version was to change (for instance, if a package was updated), it might affect their results. Having access to multiple software versions allow a set of researchers to prevent software versioning issues from affecting their results.

Dependencies are where a particular software package (or even a particular version) depends on having access to another software package (or even a particular version of another software package). For example, the VASP materials science software may depend on having a particular version of the FFTW (Fastest Fourier Transform in the West) software library available for it to work.

Environment Modules

Environment modules are the solution to these problems. A module is a self-contained description of a software package — it contains the settings required to run a software package and, usually, encodes required dependencies on other software packages.

There are a number of different environment module implementations commonly used on HPC systems: the two most common are TCL modules and Lmod. Both of these use similar syntax and the concepts are the same so learning to use one will allow you to use whichever is installed on the system you are using. In both implementations the module command is used to interact with environment modules. An additional subcommand is usually added to the command to specify what you want to do. For a list of subcommands you can use module -h or module help. As for all commands, you can access the full help on the man pages with man module.

On login you may start out with a default set of modules loaded or you may start out with an empty environment; this depends on the setup of the system you are using.

Listing Available Modules

To see available software modules, use module avail:

userid@uan01:/work/ta028/ta028/userid> module avail

----------- /work/y07/shared/archer2-modules/modulefiles-cse-pyvenvs -----------
tensorflow/2.3.1-py38  torch/1.6.0-py38  

----------- /work/y07/shared/archer2-modules/modulefiles-cse-pymods ------------
python-netCDF4/1.5.5.1  

------------ /work/y07/shared/archer2-modules/modulefiles-cse-utils ------------
bolt/0.7                  ncview/ncview-2.1.7-gcc-10.1.0  vmd/1.9.3-mpi-gcc10  
cmake/3.18.4              reframe/3.2                     xios/2.5-gcc10       
ed/1.16-gcc10             tcl/8.4.20-gcc10                xthi/1.0             
epcc-job-env              tcl/8.5.0-gcc10                 xthi/1.0-gcc10       
epcc-reframe/0.1          tcl/8.6.0-gcc10                 
genmaskcpu/1.0            tcl/8.6.10-gcc10(default)       
gnuplot/5.4.1-gcc-10.1.0  tk/8.5.6-gcc10                  
lzip/1.20-gcc10           tk/8.6.10-gcc10(default)        
nco/4.9.6                 visidata/2.1                    
nco/4.9.6-gcc-10.1.0      vmd/1.9.3-gcc10(default)        

------------ /work/y07/shared/archer2-modules/modulefiles-cse-libs -------------
adios/1.13.1     hypre/2.18.0             mumps/5.2.1     superlu-dist/6.1.1  
boost/1.72.0     libxml2/2.9.7-gcc-9.3.0  parmetis/4.0.3  superlu/5.2.1       
glm/0.9.9.6      matio/1.5.18             petsc/3.13.3    trilinos/12.18.1    
gmp/6.1.2-gcc10  metis/5.1.0              scotch/6.0.10   
...

Listing Currently Loaded Modules

You can use the module list command to see which modules you currently have loaded in your environment. If you have no modules loaded, you will see a message telling you so

userid@uan01:/work/ta028/ta028/userid> module list

Currently Loaded Modulefiles:
 1) cpe-cray                          8) perftools-base/20.10.0(default)                                  
 2) cce/10.0.4(default)               9) xpmem/2.2.35-7.0.1.0_1.9__gd50fabf.shasta(default)               
 3) craype/2.7.2(default)            10) cray-mpich/8.0.16(default)                                       
 4) craype-x86-rome                  11) cray-libsci/20.10.1.2(default)                                   
 5) libfabric/1.11.0.0.233(default)  12) bolt/0.7                                                         
 6) craype-network-ofi               13) /work/y07/shared/archer2-modules/modulefiles-cse/epcc-setup-env  
 7) cray-dsmml/0.1.2(default)        14) /usr/local/share/epcc-module/epcc-module-loader

Loading and Unloading Software

To load a software module, use module load. Let’s say we would like to use the NetCDF utility ncdump.

On login, ncdump is not available. We can test this by using the which command. which looks for programs the same way that Bash does, so we can use it to tell us where a particular piece of software is stored.

 which ncdump

which: no ncdump in (/usr/local/maven/bin:/lus/cls01095/work/y07/shared/bolt/0.7/bin:/work/y07/shared/utils/bin:/opt/cray/pe/perftools/20.10.0/bin:/opt/cray/pe/papi/6.0.0.4/bin:/opt/cray/libfabric/1.11.0.0.233/bin:/opt/cray/pe/craype/2.7.2/bin:/opt/cray/pe/cce/10.0.4/cce-clang/x86_64/bin:/opt/cray/pe/cce/10.0.4/binutils/x86_64/x86_64-pc-linux-gnu/bin:/opt/cray/pe/cce/10.0.4/binutils/cross/x86_64-aarch64/aarch64-linux-gnu/../bin:/opt/cray/pe/cce/10.0.4/utils/x86_64/bin:/usr/local/Modules/bin:/usr/local/bin:/usr/bin:/bin:/opt/cray/pe/bin:/usr/lib/mit/bin)

We can find the ncdump command by using module load:

 module load cray-netcdf 

/opt/cray/pe/netcdf/4.7.4.2/bin/ncdump

So, what just happened?

To understand the output, first we need to understand the nature of the $PATH environment variable. $PATH is a special environment variable that controls where a UNIX system looks for software. Specifically, $PATH is a list of directories (separated by :) that the OS searches through for a command before giving up and telling us it can’t find it. As with all environment variables we can print it out using echo.

 echo $PATH

/opt/cray/pe/netcdf/4.7.4.2/bin:/opt/cray/pe/python/3.8.5.0/bin:/lus/cls01095/work/z19/z19/aturner/.local/bin:/lus/cls01095/work/y07/shared/bolt/0.7/bin:/work/y07/shared/utils/bin:/usr/local/maven/bin:/opt/cray/pe/perftools/20.10.0/bin:/opt/cray/pe/papi/6.0.0.4/bin:/opt/cray/libfabric/1.11.0.0.233/bin:/opt/cray/pe/craype/2.7.2/bin:/opt/cray/pe/cce/10.0.4/cce-clang/x86_64/bin:/opt/cray/pe/cce/10.0.4/binutils/x86_64/x86_64-pc-linux-gnu/bin:/opt/cray/pe/cce/10.0.4/binutils/cross/x86_64-aarch64/aarch64-linux-gnu/../bin:/opt/cray/pe/cce/10.0.4/utils/x86_64/bin:/usr/local/Modules/bin:/home/z19/z19/aturner/bin:/usr/local/bin:/usr/bin:/bin:/opt/cray/pe/bin:/usr/lib/mit/bin

You’ll notice a similarity to the output of the which command. In this case, there’s only one difference: the different directory at the beginning. When we ran the module load command, it added a directory to the beginning of our $PATH. Let’s examine what’s there:

 ls /opt/cray/pe/netcdf/4.7.4.2/bin


nc-config  nccopy  ncdump  ncgen  ncgen3  ncxx4-config  nf-config

In summary, module load will add software to your $PATH. module load may also load additional modules with software dependencies.

To unload a module, use module unload with the relevant module name.

Unload!

Confirm you can unload the cray-netcdf module and check what happens to the PATH environment variable.

Software versioning

So far, we’ve learned how to load and unload software packages. This is very useful. However, we have not yet addressed the issue of software versioning. At some point or other, you will run into issues where only one particular version of some software will be suitable. Perhaps a key bugfix only happened in a certain version, or version X broke compatibility with a file format you use. In either of these example cases, it helps to be very specific about what software is loaded.

Let’s examine the output of module avail more closely.

 module avail cray-netcdf

--------------------------- /opt/cray/pe/modulefiles ---------------------------
cray-netcdf-hdf5parallel/4.7.4.0           cray-netcdf/4.7.4.0           
cray-netcdf-hdf5parallel/4.7.4.2(default)  cray-netcdf/4.7.4.2(default)

Note that we have two different versions of cray-netcdf (and also two versions of something else cray-netcdf-hdf5parallel which match our search).

Using module swap

Load module cray-netcdf as before. Note that if we do not specifify a particular version, we load a default version.

If we wish to change versions, we can use module swap <old-module> <new-module>. Try this to obtain cray-netcdf/4.7.4.0. Check what has happened to the location of the ncdump utility.

Using Software Modules in Scripts

Create a job that is able to run ncdump --version. Running a job is just like logging on to the system (you should not assume a module loaded on the login node is loaded on a compute node).

Solution

userid@uan01:/work/ta028/ta028/userid> nano ncdump-module.sh
userid@uan01:/work/ta028/ta028/userid> cat ncdump-module.sh

#!/bin/bash
#SBATCH --partition=standard
#SBATCH --qos=standard
#SBATCH --reservation=ta028_180

module load epcc-job-env
module load cray-netcdf

ncdump --version

userid@uan01:/work/ta028/ta028/userid> sbatch python-module.sh

Key Points

  • Load software with module load softwareName.

  • Unload software with module purge

  • The module system handles software versioning and package conflicts for you automatically.

Transferring files with remote computers

Overview

Teaching: 15 min
Exercises: 15 min
Questions

  • How do I transfer files to (and from) the cluster?

Objectives

  • Be able to transfer files to and from a computing cluster.

Computing with a remote computer offers very limited use if we cannot get files to or from the cluster. There are several options for transferring data between computing resources, from command line options to GUI programs, which we will cover here.

Download Files From the Internet

One of the most straightforward ways to download files is to use either curl or wget, one of these is usually installed in most Linux shells, on Mac OS terminal and in GitBash. Any file that can be downloaded in your web browser through a direct link can be downloaded using curl -O or wget. This is a quick way to download datasets or source code.

The syntax for these commands is: curl -O https://some/link/to/a/file and wget https://some/link/to/a/file. Try it out by downloading some material we’ll use later on, from a terminal on your local machine.

[user@laptop ~]$ curl -O https://epcced.github.io/2021-06-03-hpc-intro-online/files/hpc-intro-data.tar.gz

or

[user@laptop ~]$ wget https://epcced.github.io/2021-06-03-hpc-intro-online/files/hpc-intro-data.tar.gz

tar.gz?

This is an archive file format, just like .zip, commonly used and supported by default on Linux, which is the operating system the majority of HPC cluster machines run. You may also see the extension .tgz, which is exactly the same. We’ll talk more about “tarballs,” since “tar-dot-g-z” is a mouthful, later on.

Transferring Single Files and Folders With scp

To copy a single file to or from the cluster, we can use scp (“secure copy”). The syntax can be a little complex for new users, but we’ll break it down.

To upload to another computer:

[user@laptop ~]$ scp path/to/local/file.txt userid@login.archer2.ac.uk:/path/on/ARCHER2

To download from another computer:

[user@laptop ~]$ scp userid@login.archer2.ac.uk:/path/on/ARCHER2/file.txt path/to/local/

Note that everything after the : is relative to our home directory on the remote computer. We can leave it at that if we don’t care where the file goes.

[user@laptop ~]$ scp local-file.txt userid@login.archer2.ac.uk:

Upload a File

Copy the file you just downloaded from the Internet to your home directory on ARCHER2.

Solution

[user@laptop ~]$ scp hpc-intro-data.tar.gz userid@login.archer2.ac.uk:~/

Why Not Download on ARCHER2 Directly?

Some computer clusters are behind firewalls set to only allow transfers initiated from the outside. This means that the curl command will fail, as an address outside the firewall is unreachable from the inside. To get around this, run the curl or wget command from your local machine to download the file, then use the scp command (just below here) to upload it to the cluster.

curl -O from login.archer2.ac.uk

or

wget from login.archer2.ac.uk

Try downloading the file directly. Note that it may well fail, and that’s OK!

Commands

[user@laptop ~]$ ssh userid@login.archer2.ac.uk
userid@uan01:~> curl -O https://epcced.github.io/2021-06-03-hpc-intro-online/files/hpc-intro-data.tar.gz
or
userid@uan01:~> wget https://epcced.github.io/2021-06-03-hpc-intro-online/files/hpc-intro-data.tar.gz

Did it work? If not, what does the terminal output tell you about what happened?

To copy a whole directory, we add the -r flag, for “recursive”: copy the item specified, and every item below it, and every item below those… until it reaches the bottom of the directory tree rooted at the folder name you provided.

[user@laptop ~]$ scp -r some-local-folder userid@login.archer2.ac.uk:target-directory/

Caution

For a large directory — either in size or number of files — copying with -r can take a long time to complete.

What’s in a /?

When using scp, you may have noticed that a : always follows the remote computer name; sometimes a / follows that, and sometimes not, and sometimes there’s a final /. On Linux computers, / is the root directory, the location where the entire filesystem (and others attached to it) is anchored. A path starting with a / is called absolute, since there can be nothing above the root /. A path that does not start with / is called relative, since it is not anchored to the root.

If you want to upload a file to a location inside your home directory — which is often the case — then you don’t need a leading /. After the :, start writing the sequence of folders that lead to the final storage location for the file or, as mentioned above, provide nothing if your home directory is the destination.

A trailing slash on the target directory is optional, and has no effect for scp -r, but is important in other commands, like rsync.

A Note on rsync

As you gain experience with transferring files, you may find the scp command limiting. The rsync utility provides advanced features for file transfer and is typically faster compared to both scp and sftp (see below). It is especially useful for transferring large and/or many files and creating synced backup folders.

The syntax is similar to scp. To transfer to another computer with commonly used options:

[user@laptop ~]$ rsync -avzP path/to/local/file.txt userid@login.archer2.ac.uk:directory/path/on/ARCHER2/

The a (archive) option preserves file timestamps and permissions among other things; the v (verbose) option gives verbose output to help monitor the transfer; the z (compression) option compresses the file during transit to reduce size and transfer time; and the P (partial/progress) option preserves partially transferred files in case of an interruption and also displays the progress of the transfer.

To recursively copy a directory, we can use the same options:

[user@laptop ~]$ rsync -avzP path/to/local/dir userid@login.archer2.ac.uk:directory/path/on/ARCHER2/

As written, this will place the local directory and its contents under the specified directory on the remote system. If the trailing slash is omitted on the destination, a new directory corresponding to the transferred directory (‘dir’ in the example) will not be created, and the contents of the source directory will be copied directly into the destination directory.

The a (archive) option implies recursion.

To download a file, we simply change the source and destination:

[user@laptop ~]$ rsync -avzP userid@login.archer2.ac.uk:path/on/ARCHER2/file.txt path/to/local/

A Note on Ports

All file transfers using the above methods use SSH to encrypt data sent through the network. So, if you can connect via SSH, you will be able to transfer files. By default, SSH uses network port 22. If a custom SSH port is in use, you will have to specify it using the appropriate flag, often -p, -P, or --port. Check --help or the man page if you’re unsure.

Rsync Port

Say we have to connect rsync through port 768 instead of 22. How would we modify this command?

[user@laptop ~]$ rsync test.txt userid@login.archer2.ac.uk:

Solution

[user@laptop ~]$ rsync --help | grep port
     --port=PORT             specify double-colon alternate port number
See http://rsync.samba.org/ for updates, bug reports, and answers
[user@laptop ~]$ rsync --port=768 test.txt userid@login.archer2.ac.uk:

Archiving Files

One of the biggest challenges we often face when transferring data between remote HPC systems is that of large numbers of files. There is an overhead to transferring each individual file and when we are transferring large numbers of files these overheads combine to slow down our transfers to a large degree.

The solution to this problem is to archive multiple files into smaller numbers of larger files before we transfer the data to improve our transfer efficiency. Sometimes we will combine archiving with compression to reduce the amount of data we have to transfer and so speed up the transfer.

The most common archiving command you will use on a (Linux) HPC cluster is tar. tar can be used to combine files into a single archive file and, optionally, compress it.

Let’s start with the file we downloaded from the lesson site, hpc-lesson-data.tar.gz. The “gz” part stands for gzip, which is a compression library. Reading this file name, it appears somebody took a folder named “hpc-lesson-data,” wrapped up all its contents in a single file with tar, then compressed that archive with gzip to save space. Let’s check using tar with the -t flag, which prints the “table of contents” without unpacking the file, specified by -f <filename>, on the remote computer. Note that you can concatenate the two flags, instead of writing -t -f separately.

[user@laptop ~]$ ssh userid@login.archer2.ac.uk
userid@uan01:~> tar -tf hpc-lesson-data.tar.gz
hpc-intro-data/
hpc-intro-data/north-pacific-gyre/
hpc-intro-data/north-pacific-gyre/NENE01971Z.txt
hpc-intro-data/north-pacific-gyre/goostats
hpc-intro-data/north-pacific-gyre/goodiff
hpc-intro-data/north-pacific-gyre/NENE02040B.txt
hpc-intro-data/north-pacific-gyre/NENE01978B.txt
hpc-intro-data/north-pacific-gyre/NENE02043B.txt
hpc-intro-data/north-pacific-gyre/NENE02018B.txt
hpc-intro-data/north-pacific-gyre/NENE01843A.txt
hpc-intro-data/north-pacific-gyre/NENE01978A.txt
hpc-intro-data/north-pacific-gyre/NENE01751B.txt
hpc-intro-data/north-pacific-gyre/NENE01736A.txt
hpc-intro-data/north-pacific-gyre/NENE01812A.txt
hpc-intro-data/north-pacific-gyre/NENE02043A.txt
hpc-intro-data/north-pacific-gyre/NENE01729B.txt
hpc-intro-data/north-pacific-gyre/NENE02040A.txt
hpc-intro-data/north-pacific-gyre/NENE01843B.txt
hpc-intro-data/north-pacific-gyre/NENE01751A.txt
hpc-intro-data/north-pacific-gyre/NENE01729A.txt
hpc-intro-data/north-pacific-gyre/NENE02040Z.txt

This shows a folder containing another folder, which contains a bunch of files. If you’ve taken The Carpentries’ Shell lesson recently, these might look familiar. Let’s see about that compression, using du for “disk usage”.

userid@uan01:~> du -sh hpc-lesson-data.tar.gz
36K     hpc-intro-data.tar.gz

Files Occupy at Least One “Block”

If the filesystem block size is larger than 36 KB, you’ll see a larger number: files cannot be smaller than one block.

Now let’s unpack the archive. We’ll run tar with a few common flags:

When it’s done, check the directory size with du and compare.

Extract the Archive

Using the four flags above, unpack the lesson data using tar. Then, check the size of the whole unpacked directory using du.

Hint: tar lets you concatenate flags.

Commands

userid@uan01:~> tar -xvzf hpc-lesson-data.tar.gz

hpc-intro-data/
hpc-intro-data/north-pacific-gyre/
hpc-intro-data/north-pacific-gyre/NENE01971Z.txt
hpc-intro-data/north-pacific-gyre/goostats
hpc-intro-data/north-pacific-gyre/goodiff
hpc-intro-data/north-pacific-gyre/NENE02040B.txt
hpc-intro-data/north-pacific-gyre/NENE01978B.txt
hpc-intro-data/north-pacific-gyre/NENE02043B.txt
hpc-intro-data/north-pacific-gyre/NENE02018B.txt
hpc-intro-data/north-pacific-gyre/NENE01843A.txt
hpc-intro-data/north-pacific-gyre/NENE01978A.txt
hpc-intro-data/north-pacific-gyre/NENE01751B.txt
hpc-intro-data/north-pacific-gyre/NENE01736A.txt
hpc-intro-data/north-pacific-gyre/NENE01812A.txt
hpc-intro-data/north-pacific-gyre/NENE02043A.txt
hpc-intro-data/north-pacific-gyre/NENE01729B.txt
hpc-intro-data/north-pacific-gyre/NENE02040A.txt
hpc-intro-data/north-pacific-gyre/NENE01843B.txt
hpc-intro-data/north-pacific-gyre/NENE01751A.txt
hpc-intro-data/north-pacific-gyre/NENE01729A.txt
hpc-intro-data/north-pacific-gyre/NENE02040Z.txt

Note that we did not type out -x -v -z -f, thanks to the flag concatenation, though the command works identically either way.

userid@uan01:~> du -sh hpc-lesson-data
144K    hpc-intro-data

Was the Data Compressed?

Text files compress nicely: the “tarball” is one-quarter the total size of the raw data!

If you want to reverse the process — compressing raw data instead of extracting it — set a c flag instead of x, set the archive filename, then provide a directory to compress:

[user@laptop ~]$ tar -cvzf compressed_data.tar.gz hpc-intro-data

Working with Windows

When you transfer text files to from a Windows system to a Unix system (Mac, Linux, BSD, Solaris, etc.) this can cause problems. Windows encodes its files slightly different than Unix, and adds an extra character to every line.

On a Unix system, every line in a file ends with a \n (newline). On Windows, every line in a file ends with a \r\n (carriage return + newline). This causes problems sometimes.

Though most modern programming languages and software handles this correctly, in some rare instances, you may run into an issue. The solution is to convert a file from Windows to Unix encoding with the dos2unix command.

You can identify if a file has Windows line endings with cat -A filename. A file with Windows line endings will have ^M$ at the end of every line. A file with Unix line endings will have $ at the end of a line.

To convert the file, just run dos2unix filename. (Conversely, to convert back to Windows format, you can run unix2dos filename.)

Key Points

  • wget and curl -O download a file from the internet.

  • scp transfers files to and from your computer.

Break

Overview

Teaching: min
Exercises: min
Questions
Objectives

Comfort break

Key Points

Using resources effectively

Overview

Teaching: 10 min
Exercises: 30 min
Questions

  • How do we monitor our jobs?

  • How can I get my jobs scheduled more easily?

Objectives

  • Understand how to look up job statistics and profile code.

  • Understand job size implications.

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. To do this we need to understand the basics of benchmarking. Benchmarking is essentially performing simple experiments to help understand how the performance of our work varies as we change the properties of the jobs on the cluster - including input parameters, job options and resources used.

Our example

In the rest of this episode, we will use an example parallel application that sharpens an input image. Although this is a toy problem, it exhibits all the properties of a full parallel application that we are interested in for this course.

The main resource we will consider here is the use of compute core time as this is the resource you are usually charged for on HPC resources. However, other resources - such as memory use - may also have a bearing on how you choose resources and constrain your choice.

For those that have come across HPC benchmarking before, you may be aware that people often make a distinction between strong scaling and weak scaling:

  • Strong scaling is where the problem size (i.e. the application) stays the same size and we try to use more cores to solve the problem faster.
  • Weak scaling is where the problem size increases at the same rate as we increase the core count so we are using more cores to solve a larger problem.

Both of these approaches are equally valid uses of HPC. This example looks at strong scaling.

Before we work on benchmarking, it is useful to define some terms for the example we will be using

Accessing the software and input

The sharpen program has been preinstalled on ARCHER2, you can access it with the command:

module load training/sharpen/1.0

Once you have loaded the module, you can access the program as sharpen-mpi.x. You will also need to get a copy of the input file for this application. To do this, copy it from the central install location to your directory with (note you must have loaded the sharpen module as described above for this to work):

 cp $SHARPEN_INPUT/fuzzy.pgm .

Baseline: running in serial

Before starting to benchmark an application to understand what resources are best to use, you need a baseline performance result. In more formal benchmarking, your baseline is usually the minimum number of cores or nodes you can run on. However, for understanding how best to use resources, as we are doing here, your baseline could be the performance on any number of cores or nodes that you can measure the change in performance from.

Our sharpen application is small enough that we can run a serial (i.e. using a single core) job for our baseline performance so that is where we will start

Run a single core job

Write a job submission script that runs the sharpen application on a single core. You will need to take an initial guess as to the walltime to request to give the job time to complete. Submit the job and check the contents of the STDOUT file to see if the application worked or not.

Solution

Creating a file called submit_sharpen.pbs:

#!/bin/bash

#SBATCH --partition=standard
#SBATCH --qos=standard
#SBATCH --reservation=ta028_180

#SBATCH --job-name=sharpen
#SBATCH --nodes=1
#SBATCH --tasks-per-node=1
#SBATCH --time=00:15:00

module load -s epcc-job-env
module load training/sharpen/1.0

# Run application using a single process (i.e. in serial)
srun --hint=nomultithread --distribution=block:block sharpen-mpi.x

Submit with:

userid@uan01:/work/ta028/ta028/userid> sbatch  submit_sharpen.pbs

Output in the job log should look something like:

Input file is: fuzzy.pgm
Image size is 564 x 770
Using a filter of size 17 x 17
Reading image file: fuzzy.pgm
... done
Starting calculation ...
On core 0
... finished
Writing output file: sharpened.pgm
... done
Calculation time was 5.400482 seconds
Overall run time was 5.496556 seconds

Once your job has run, you should look in the output to identify the performance. Most HPC programs should print out timing or performance information (usually somewhere near the bottom of the summary output) and sharpen is no exception. You should see two lines in the output that look something like:

Calculation time was 5.579000 seconds
Overall run time was 5.671895 seconds

You can also get an estimate of the overall run time from the final job statistics. If we look at how long the finished job ran for, this will provide a quick way to see roughly what the runtime was. This can be useful if you want to know quickly if a job was faster or not than a previous job (as you do not have to find the output file to look up the performance) but the number is not as accurate as the performance recorded by the application itself and also includes static overheads from running the job (such as loading modules and startup time) that can skew the timings. To do this on use sacct -l -j with the job ID, e.g.:

userid@uan01:/work/ta028/ta028/userid> sacct -l -j 12345

JOBID USER         ACCOUNT     NAME           ST REASON START_TIME         T...
36856 yourUsername yourAccount example-job.sh R  None   2017-07-01T16:47:02 ...

Viewing the sharpened output image

To see the effect of the sharpening algorithm, you can view the images using the display program from the ImageMagick suite.

display sharpened.pgm

Type q in the image window to close the program. To view the image you will need an X window client installed and you will have to have logged into with the ssh -Y option to export the display back to your local system. If you are using Windows, the MobaXterm program provides a login shell with X capability. If you are using macOS, then you will need to install XQuartz. If you are using Linux then X should just work!

Running in parallel and benchmarking performance

We have now managed to run the sharpen application using a single core and have a baseline performance we can use to judge how well we are using resources on the system.

Note that we also now have a good estimate of how long the application takes to run so we can provide a better setting for the walltime for future jobs we submit. Lets now look at how the runtime varies with core count.

Benchmarking the parallel performance

Modify your job script to run on multiple cores and evaluate the performance of sharpen on a variety of different core counts and use multiple runs to complete a table like the one below.

If you examine the log file you will see that it contains two timings: the total time taken by the entire program (including IO) and the time taken solely by the calculation. The image input and output is not parallelised so this is a serial overhead, performed by a single processor. The calculation part is, in theory, perfectly parallel (each processor operates on different parts of the image) so this should get faster on more cores. The IO time in the table below is the difference between the calculation time and the overall run time; the Calculation core seconds is the calculation time multiplied by the number of cores.

Cores Overall run time (s) Calculation time (s) IO time (s) Calculation core seconds
1 (serial)        
2        
4        
8        
16        
32        
64        
128        
256        

Look at your results – do they make sense? Given the structure of the code, you would expect the IO time to be roughly constant, and the performance of the calculation to increase linearly with the number of cores: this would give a roughly constant figure for the Calulation core seconds. Is this what you observe?

Solution

The table below shows example timings for runs on

Cores Overall run time (s) Calculation time (s) IO time (s) Calculation core seconds
1 3.188 2.890 0.298 2.890
2 1.697 1.432 0.265 2.864
4 1.068 0.716 0.352 2.864
8 0.717 0.372 0.345 2.976
16 0.516 0.181 0.335 2.896
32 0.393 0.093 0.300 2.976
64 0.412 0.053 0.359 3.392
128 0.317 0.030 0.287 3.840
256 0.312 0.018 0.294 4.608

Understanding the performance

Now we have some data showing the performance of our application we need to try and draw some useful conclusions as to what the most efficient set of resources are to use for our jobs. To do this we introduce two metrics:

We will now use our performance results to compute these two metrics for the sharpen application and use the metrics to evaluate the performance and make some decisions about the most effective use of resources.

Computing the speedup and parallel efficiency

Use your Overall run times from above to fill in a table like the one below.

Cores Overall run time (s) Ideal speedup Actual speedup Parallel efficiency
1 (serial)        
2        
4        
8        
16        
32        
64        
128        
256        

Given your results, try to answer the following questions:

  1. What is the core count where you get the most efficient use of resources, irrespective of run time?
  2. What is the core count where you get the fastest solution, irrespective of efficiency?
  3. What do you think a good core count choice would be for this application that balances time to solution and efficiency? Why did you choose this option?

Solution

The table below gives example results for based on the example runtimes given in the solution above.

Cores Overall run time (s) Ideal speedup Actual speedup Parallel efficiency
1 3.188 1.000 1.000 1.000
2 1.697 2.000 1.879 0.939
4 1.068 4.000 2.985 0.746
8 0.717 8.000 4.446 0.556
16 0.516 16.000 6.178 0.386
32 0.393 32.000 8.112 0.253
64 0.412 64.000 7.738 0.121
128 0.317 128.000 10.057 0.079
256 0.312 256.000 10.218 0.040

What is the core count where you get the most efficient use of resources?

Just using a single core is the cheapest (and always will be unless your speedup is better than perfect – “super-linear” speedup). However, it may not be possible to run on small numbers of cores depending on how much memory you need or other technical constraints.

Note: on most high-end systems, nodes are not shared between users. This means you are charged for all the CPU-cores on a node regardless of whether you actually use them. Typically we would be running on many hundreds of CPU-cores not a few tens, so the real question in practice is: what is the optimal number of nodes to use?

What is the core count where you get the fastest solution, irrespective of efficiency?

256 cores gives the fastest time to solution.

The fastest time to solution does not often make the most efficient use of resources so to use this option, you may end up wasting your resources. Sometimes, when there is time pressure to run the calculations, this may be a valid approach to running applications.

What do you think a good core count choice would be for this application to use?

4 cores is probably a good number of cores to use with a parallel efficiency of 75%.

Usually, the best choice is one that delivers good parallel efficiency with an acceptable time to solution. Note that acceptable time to solution differs depending on circumstances so this is something that the individual researcher will have to assess. Good parallel efficiency is often considered to be 70% or greater though many researchers will be happy to run in a regime with parallel efficiency greater than 60%. As noted above, running with worse parallel efficiency may also be useful if the time to solution is an overriding factor.

Tips

Here are a few tips to help you use resources effectively and efficiently on HPC systems:

Key Points

  • The smaller your job, the faster it will schedule.

Using shared resources responsibly

Overview

Teaching: 15 min
Exercises: 5 min
Questions

  • How can I be a responsible user?

  • How can I protect my data?

  • How can I best get large amounts of data off an HPC system?

Objectives

  • Learn how to be a considerate shared system citizen.

  • Understand how to protect your critical data.

  • Appreciate the challenges with transferring large amounts of data off HPC systems.

  • Understand how to convert many files to a single archive file using tar.

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.

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.

Be Kind to the Login Nodes

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.

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.

Login Nodes Are a Shared Resource

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.

Unsure? Ask your friendly systems administrator (“sysadmin”) or service desk if the thing you’re contemplating is suitable for the login node, or if there’s another mechanism to get it done safely.

You can contact the ARCHER2 Service Desk at support@archer2.ac.uk

You can always use the commands top and ps ux 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 kill command along with the PID to terminate the process.

Login Node Etiquette

Which of these commands would be a routine task to run on the login node?

  1. python physics_sim.py
  2. make
  3. create_directories.sh
  4. molecular_dynamics_2
  5. tar -xzf R-3.3.0.tar.gz

Solution

Building software, creating directories, and unpacking software are common and acceptable > tasks for the login node: options #2 (make), #3 (mkdir), and #5 (tar) are probably OK. Note that script names do not always reflect their contents: before launching #3, please less create_directories.sh and make sure it’s not a Trojan horse.

Running resource-intensive applications is frowned upon. Unless you are sure it will not affect other users, do not run jobs like #1 (python) or #4 (custom MD code). If you’re unsure, ask your friendly sysadmin for advice.

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.

Test Before Scaling

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! Most systems provide dedicated resources for testing that have short wait times to help you avoid this issue.

Test Job Submission Scripts That Use Large Amounts of Resources

Before submitting a large run of jobs, submit one as a test first to make sure everything works as expected.

Before submitting a very large or very long job submit a short truncated test to ensure that the job starts as expected.

Have a Backup Plan

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.

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.

For larger amounts of data, you should make sure you have a robust system in place for taking copies of critical data off the HPC system wherever possible to backed-up storage. Tools such as rsync can be very useful for this.

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).

In all these cases, the service desk 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.

Your Data Is Your Responsibility

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.

On ARCHER2, the home file systems are backed up so you can restore data you deleted by mistake. A copy of the data on home file system is also kept off site for disaster recovery purposes. The work file systems are not backed up in any way.

Transferring Data

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.

The components between your data’s source and destination have varying levels of performance, and in particular, may have different capabilities with respect to bandwidth and latency.

Bandwidth 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.

Latency 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.

Some of the key components and their associated issues are:

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 archive 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 tar and zip. We have already met tar when we talked about data transfer earlier.

/2021-06-03-hpc-intro-online/Schematic%20of%20network%20bandwidth
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.

Consider the Best Way to Transfer Data

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.

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 ARCHER2?

  1. [user@laptop ~]$ scp -r data userid@login.archer2.ac.uk:~/

  2. [user@laptop ~]$ rsync -ra data userid@login.archer2.ac.uk:~/

  3. [user@laptop ~]$ rsync -raz data userid@login.archer2.ac.uk:~/

  4. [user@laptop ~]$ tar -cvf data.tar data
[user@laptop ~]$ rsync -raz data.tar userid@login.archer2.ac.uk:~/

  5. [user@laptop ~]$ tar -cvzf data.tar.gz data
[user@laptop ~]$ rsync -ra data.tar.gz userid@login.archer2.ac.uk:~/

Solution

  1. scp will recursively copy the directory. This works, but without compression.
  2. rsync -ra works like scp -r, but preserves file information like creation times. This is marginally better.
  3. rsync -raz 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.
  4. This command first uses tar to merge everything into a single file, then rsync -z to transfer it with compression. With this large number of files, metadata overhead can hamper your transfer, so this is a good idea.
  5. This command uses tar -z to compress the archive, then rsync 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).

Key Points

  • Be careful how you use the login node.

  • Your data on the system is your responsibility.

  • Plan and test large data transfers.

  • It is often best to convert many files to a single archive file before transferring.

  • Again, don’t run stuff on the login node.