A laptop and a desktop computer: you sit at the keyboard & mouse and log in to the computer directly, often as the sole user.

Good at performing local and personal tasks: familiar but limited resources.

Computation at larger scales typically involves changes in how things are done, and connection to remote computer systems and transfer of data between systems.

There are many ways to do it, and the terminology can be confusing.

This presentation will step through some scale-up scenarios to help frame the problem.

First obvious way to scale, has more CPUs and memory, and more or faster storage. Typically shared, run commercial or open-source operating systems, may be called a "compute server". Typical tasks are operations of large user-facing programs, like scientific models or visualizations.

Cloud systems physically resemble clusters or supercomputers, but with greater heterogeneity among components, and less coupling, often without an interconnect at all. Cloud servers are generally accessed by connecting to their services over the network. Multiple virtual machines may reside on individual physical machines. Typical tasks are driven by user connections from the outside to the various "front end" services (e.g. web sites) which pass traffic on to the "back end" servers (e.g. databases) to present results to users.

A collection of standalone computers that are networked together. They will frequently have software installed that allow the coordinated running of other software across all of these computers. This allows these networked computers to work together to accomplish computing tasks faster.

In the usual HPC workflow, users do not communicate directly with compute nodes, but work through a scheduling system to distribute tasks.