---
# System prepended metadata

title: Why use interaction data?

---

---
title: "Why use interaction data?"
teaching: 15
exercises: 5
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."
keypoints:
- "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."
---

[![hackmd-github-sync-badge](https://hackmd.io/rJEYO45zTVK5KRQ6Ql20yg/badge)](https://hackmd.io/rJEYO45zTVK5KRQ6Ql20yg)


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

* A statistics student wants to cross-validate a model. This involves running
  the model 1000 times &mdash; but each run takes an hour. Running the model on
  a laptop will take over a month! In this research problem, final results are
  calculated after all 1000 models have run, but typically only one model is
  run at a time (in **serial**) on the laptop. Since each of the 1000 runs is
  independent of all others, and given enough computers, it's theoretically
  possible to run them all at once (in **parallel**).
* A genomics researcher has been using small datasets of sequence data, but
  soon will be receiving a new type of sequencing data that is 10 times as
  large. It's already challenging to open the datasets on a computer &mdash;
  analyzing these larger datasets will probably crash it. In this research
  problem, the calculations required might be impossible to parallelize, but a
  computer with **more memory** would be required to analyze the much larger
  future data set.
* An engineer is using a fluid dynamics package that has an option to run in
  parallel. So far, this option was not utilized on a desktop. In going from 2D
  to 3D simulations, the simulation time has more than tripled. It might be
  useful to take advantage of that option or feature. In this research problem,
  the calculations in each region of the simulation are largely independent of
  calculations in other regions of the simulation. It's possible to run each
  region's calculations simultaneously (in **parallel**), communicate selected
  results to adjacent regions as needed, and repeat the calculations to
  converge on a final set of results. In moving from a 2D to a 3D model, **both
  the amount of data and the amount of calculations increases greatly**, and
  it's theoretically possible to distribute the calculations across multiple
  computers communicating over a shared network.

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](https://rubberduckdebugging.com/) about your research.
>
> * How does computing help you do your research?
> * How could more computing help you do more or better research?
{: .discussion }

## A standard Laptop for standard tasks

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

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

* the keyboard and/or touchpad is used to tell the computer what to do
  (**Input**)
* the internal computing resources **Central Processing Unit** and **Memory**
  perform calculation
* the display depicts progress and results (**Output**)

Schematically, this can be reduced to the following:


## 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](
https://en.wikipedia.org/wiki/Dijkstra's_algorithm) 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.

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".

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)
1. 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](https://en.wikipedia.org/wiki/MapReduce)! 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](
> >   https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm) 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"](
> > https://en.wikipedia.org/wiki/Hyperscale_computing) collection of servers
> > working together.
> {: .solution}
{: .challenge }

{% include links.md %}
