On the face of it, you may be confused
as to what the difference is between these two processors. After all,
you may be familiar with words like parallel computing and come
across these two different types of processor. So what are the
differences between them? This is the question of this week’s 52Things Every Cryptography PhD Student should know. But before we get
into the nitty gritty of it, why don't we first have a look at the
concept these two different processors are part of, namely parallel
computing.

### What is parallel computing?

Before answering this question we first
need to consider the conventional “serial” model of processing. Let's do so by
imagining some problem we need to solve. The way serial
computing solves this problem is by viewing it as a number
of steps (instructions) which the processor deals with in sequential
order. The processor deals with each of the instructions and then at
the end, the answer comes out and the problem is solved. Whilst
being a wonderful way of solving the problem it does however imply a bottleneck in
the speed of solving it. Namely, the speed of the processor at executing the
individual instructions. This is fine if the problem isn’t too
large, but what happens when we have to deal with larger problems or
want to compute things faster? Is there a way of increasing the speed
of computation without the bottleneck of the speed of the processor?

The answer
as you might have guessed is yes and it comes in the form of
something called parallel computing. What parallel computing does to
the problem we are trying to solve is to break it down into smaller
problems, each of which can be computed separately at the same time.
In this way, the problem is distributed over different processing
elements which perform each of these different sub problems
simultaneously, providing a potentially significant increase in speed
of computation – the amount of speed up depends on the algorithm
and can be determined by Amdahl's law [1]. So how
does this all work? How can you process things in such a way as this?
Well two solutions to the problem are multi-core and vector
processors.

### What is a multi-core processor?

A multi-core processor is a single
computing component that carries out parallel computing by using
multiple serial processors to do different things at the same time.
The sub problems of the bigger problem discussed earlier are each
solved by a separate processor allowing programs to be computed in
parallel. It's like having multiple people working on a project where
each person is given a different task to do, but all are contributing to
the same project. This might take some extra organising to do, but the overall speed of getting the project completed is going to be faster.

### What is a vector processor?

A vector processor is a processor that
computes single instructions (as in a serial processor) but carries
them out on multiple data sets that are arranged in one dimensional
arrays (unlike a standard serial processor which operates on single
data sets). The idea here is that if you are doing the same thing many times to different data sets in a program, rather
than executing a single instruction for each piece of data why not do
the instruction to all the sets of data once? The acronym SIMD
(Single Instruction Multiple Data) is often used to denote
instructions that work in this way.

### What is the difference?

So that's the general idea, let's sum
up with an example. Let's say we want roll 4 big stones across a road
and it takes one minute to do each roll. The serial processor rolls
them one by one and so takes four minutes. The multi core processor
with two cores has two people to roll stones so each one rolls two
stones, it takes two minutes. The vector processor gets a long plank
of wood, puts it behind all four stones and pushes them all in one,
taking one minute. The multi core processor has multiple workers, the
vector processor has a way of doing the same thing to multiple things
at the same time.

[1]
http://en.wikipedia.org/wiki/Amdahl%27s_law

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