Introduction to High-Performance Computing: Cheatsheets for Queuing System Quick Reference

Key Points

Why use a Cluster?

  • 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

  • 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.
Scheduling jobs

  • 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.
Accessing software

  • 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

  • wget downloads a file from the internet.

  • scp transfer files to and from your computer.
Running a parallel job

  • Parallelism is an important feature of HPC clusters.

  • MPI parallelism is a common case.

  • The queuing system facilitates executing parallel tasks.
Using resources effectively

  • The smaller your job, the faster it will schedule.
Using shared resources responsibly

  • 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.

Cheatsheets for Queuing System Quick Reference

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.

Glossary

The following list captures terms that need to be added to this glossary. This is a great way to contribute.

Accelerator
to be defined
Beowulf cluster
to be defined
Central processing unit
to be defined
Cloud computing
to be defined
Cluster
a collection of computers configured to enable collaboration on a common task by means of purposefully configured hardware (e.g., networking) and software (e.g. workload management).
Distributed memory
to be defined
Grid computing
to be defined
High availability computing
to be defined
High performance computing
to be defined
Interconnect
to be defined
Node
to be defined
Parallel
to be defined
Serial
to be defined
Server
to be defined
Shared memory
to be defined
Slurm
to be defined
Supercomputer
… “a major scientific instrument” …
Workstation
to be defined
Grid Engine
to be defined
Parallel File System
to be defined

Local resources