This lesson is in the early stages of development (Alpha version)

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 webservices (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 independent 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

Go ahead and log in to the cluster: Myriad at University College London.

[user@laptop ~]$ ssh yourUsername@myriad.rc.ucl.ac.uk

Remember to replace yourUsername with the username supplied by the instructors. You will be asked for your password. But watch out, the characters you type are not displayed on the screen.

You are logging in using a program known as the secure shell or ssh. This establishes a temporary encrypted connection between your laptop and myriad.rc.ucl.ac.uk. The word before the @ symbol, e.g. yourUsername here, is the user account name that Lola has access permissions for on the cluster.

Where do I get this ssh from ?

On Linux and/or macOS, the ssh command line utility is almost always pre-installed. Open a terminal and type ssh --help to check if that is the case.

At the time of writing, the openssh support on Microsoft is still very recent. Alternatives to this are putty, bitvise SSH, mRemoteNG or MobaXterm. Download it, install it and open the GUI. The GUI asks for your user name and the destination address or IP of the computer you want to connect to. Once provided, you will be queried for your password just like in the example above.

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!)

[yourUsername@login12 ~]$  hostname
Myriad

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 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. It should never be used for doing actual work.

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 ). 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 with the qhost command.

[yourUsername@login12 ~]$  qhost
    4 type * nodes: 36 cores, 188.4G RAM
    7 type B nodes: 36 cores,   1.5T RAM
   66 type D nodes: 36 cores, 188.4G RAM
    9 type E nodes: 36 cores, 188.4G RAM
    1 type F nodes: 36 cores, 188.4G RAM
    3 type H nodes: 36 cores, 172.7G RAM
   53 type H nodes: 36 cores, 188.4G RAM
    3 type I nodes: 36 cores,   1.5T RAM
    2 type J nodes: 36 cores, 188.4G RAM

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.

Shared file systems

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

What’s in a node?

All of a HPC system’s nodes 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.

/hpc-intro/Node%20anatomy

Explore Your Computer

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

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 be found on the command line:

  • 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 Head Node

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

Solution

[user@laptop ~]$ ssh yourUsername@myriad.rc.ucl.ac.uk
[yourUsername@login12 ~]$  nproc --all
[yourUsername@login12 ~]$  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:

[yourUsername@login12 ~]$  less /proc/meminfo

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 the worker nodes (the instructors will give you the ID of the compute node to use):

[yourUsername@login12 ~]$  qhost -h node-d00a-001

Compare Your Computer, the Head Node and the Worker 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?

Units and Language

A computer’s memory and disk are measured in units called Bytes (one Byte is 8 bits). As today’s files and memory have grown to be large given historic standards, volumes are noted using the SI prefixes. So 1000 Bytes is a Kilobyte (kB), 1000 Kilobytes is a Megabyte (MB), 1000 Megabytes is a Gigabyte (GB), etc.

History and common language have however mixed this notation with a different meaning. When people say “Kilobyte”, they mean 1024 Bytes instead. In that spirit, a Megabyte is 1024 Kilobytes.

To address this ambiguity, the International System of Quantities standardizes the binary prefixes (with base of 210=1024) by the prefixes Kibi (ki), Mibi (Mi), Gibi (Gi), etc. For more details, see here

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 file systems, etc.).

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