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So we started last time, this is where we 
left off was talking about topologies. 

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Now we went through this really fast, but 
I wanted to go into a little more detail 

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here. 
So far, we've, in this class, we've been 

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talking about buses. 
And actually buses are a type of 

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interconnection network. 
So a multi-drop bus, where everyone just 

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sort of screams, is a broadcast network. 
And it is, it is a type of 

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interconnection network. 
But it may not have the best properties. 

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It may not have the best bandwidth, 
and it might impact your clock frequency, 

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so it might even impact your latency, 
depending on how you go about 

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implementing one of these things. 
So sort of the next step away from 

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something like a, a bus is actually 
something like a pipeline bus, 

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where you start to put registers in along 
the way. 

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And now you can have nearest neighbor 
communication. 

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Let's say one is talking to two, and 
three is talking to four at the same 

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time. 
But you couldn't do that when everyone 

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was shouting to each other, as only one, 
one, entities allowed to talk on this 

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network at a time. 
We'll say. 

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So we start thinking about that. 
And we can actually make some more 

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advanced versions of these. 
We can start to think about things like 

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toruses, where we'll take the end here 
and connect it around. 

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And this is a one-dimensional torus, or 
many times is known as a ring. 

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one thing I wanted to point out about 
this is if you look at the naive ring 

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implementation, and if you think of these 
are, as wires, you would say, 'Well, this 

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is interesting. 
If I have a thousand nodes in my ring.' 

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All of let's think about that. 
One, two, three, four. 

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Yep, okay. 
If you have 1000 nodes per ring. 

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999 of'em are going to have very short 
links, and then one of the links is 

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going to be super, super long, it's 
going to go from this end all the way to 

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that end like this is drawn. 
Hm. 

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Well that's, that's not super great. 
communally, people have actually thought 

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of fancy ways to fold tauruses into the, 
let's say if this is a 1-D torus into a 

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1-D space, 
and minimize the wire lay, lengths. 

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So actually I drew a picture here to show 
this. 

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If you remember and stagger the you 
connec-, the nodes here. 

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This is the exact same 1-D torus as we 
drew here. 

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Except now all the lengths are equally 
equidistant or equal length. 

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Except they're twice the length as they 
were before. 

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So, you can actually fold a torus into 
the same oh, oh, for instance, an 

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n-dimensional torus into n-dimensional 
space, by doubling each the lengths, by 

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doing this cool inner leaving trick. 
And this also applies for 2D, 3D, 4D 

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Tauruses. 
Etc. 

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You can, you can come up with some 
ordering and numbering which will 

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actually, interleave like that. 
Now having said that, it may be 

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challenging to build a four dimensional 
Taurus in three space. 

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And fortunately, I live in three 
dimensional space, and I'm hoping you 

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guys do too. 
and if you, if you're like me, and you 

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live in three dimensional space. 
It can be hard to go build five 

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dimensional things in three dimensional 
space. 

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We'll talk about that in a minute. 
But I just want to show this cool trick 

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that you can actually. 
Full day. 

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1-dimensional Taurus into 1-dimensional 
space and not have a super long link. 

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Okay so now we can start to think about. 
Actually before, before we go on to that 

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lets, lets think about the, the limits of 
this. 

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If we start to go to a 1,000. 
Long torus. 

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Or a thousand 1d ringed or 1d torus. 
Unfortunately this does not, the amount 

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of sort of bandwidth that you cut through 
here, let's say these two links here does 

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not go up as we add nodes. 
And a good property of a network or inter 

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connection network, is that as you add 
more people communicating on the network, 

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or more entities communicating on the 
network, you probably want to be able to 

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add more bandwidth. 
And you can say, well I can make the, the 

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links wider, 
but that only helps so much. 

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I can make the link faster, but it only 
helps so much. 

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So this makes us think about having 
different topologies. 

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So we can start to go to higher 
dimensioned topologies. 

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So we can start to use 2D topologies, 
or 3D. 

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and you can see here, here as we have 
lets say 16 nodes arranged in a 

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2-dimensional mesh, 
versus, here we have a 2D torus. 

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And the difference between the 2D mesh 
and the 2D torus is that there's what's 

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called end around in our, in the torus. 
And that makes the routing from here to 

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there much, much faster, 
and effectively, we'll cut the, average 

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routing time, or average routing, excuse 
me, 

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average, hop count by a half. 
Because you can go either way now, and go 

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around the ends. 
Sometimes, these 2D toruses are called, a 

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2D mesh with end around. 
That means it's a torus. 

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just, I just wanted to throw that 
nomenclature out there, because sometimes 

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you'll see, you'll see both. 
A good example of a simple routing 

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protocol is if you're at this node here. 
And you want to communicate some data you 

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can send the data in all directions. 
And then everyone else sends it in all 

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directions. 
And everyone else sends it in all 

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directions. 
And it'll just flood the network, and 

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it'll get everywhere. 
And you're guaranteed that it's going to 

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at least get to the receiver, 
and you have some guarantee that when it 

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gets to the receiver, the receiver sees 
the, the packet and takes it off. 

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Now that may not be what you want to do. 
[LAUGH]. 

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That's, that's probably not low power, 
and it's probably going to cost a lot of 

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congestion on your network, 
but you may want to think about a 

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flooding protocol. 
Okay, so wherever there is 2D we can 

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start to go to 3D. 
So here we have a 3-dimensional cube. 

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Sort of. 
It's the best I could draw. 

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It's, it's hard to draw 3-dimensional 
things on 2-dimensional space. 

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And if, if we had 3D space I could have 
drawn this much cooler. 

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so this is a hypercube. 
Hope these are actually hypercubes. 

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So, all the hypercube is, is it's saying 
that the number of the mentions you have 

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[COUGH]. 
is equal to the number, or the degree or 

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the outbound links of a, of a, of a node 
in the, in here. 

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So, if you look at this node, we have a 
three dimensional hypercube. 

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So it can go this direction, that 
direction, or that direction. 

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Every node has a out degree of three, or 
a connectivity of three. 

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Here, we have a four dimens, a four 
dimensional cube, but this is, by 

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definition, a hyper cube. 
So, because, if you look at any given 

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point here. 
So, we're going to define a hyper cube as 

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the out degree, of the nodes is equal to 
the dimension of, of the, the links. 

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And, you can't go build here a, if you 
were to add one more node to the system, 

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[COUGH] or some other. 
Let's say you were to scale this out in 

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some direction, 
you would actually be increasing, the 

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degree of a node by adding more, more 
nodes. 

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But not to everybody, equally, 
so not be a, a hypercube. 

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So if you were to build on a, something 
like this network here, this is, this is 

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not strictly a hypercube because the 
degree of I'll say this note here is not 

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equal to the degree of, well actually you 
could just stick with that. 

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The degree is equal to the degree of the 
other places, but we've effectively 

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increased the diameter of one of the 
dimensions such that there's not the 

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routing distance is longer now for this 
number of nodes. 

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If you were to go to a higher dimensional 
design, you could actually reduce the, 

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the latency for each one of these nodes 
in something that's got three 

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dimensional, three area three q, which 
we'll talk about in a minute. 

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But here we have a four dimensional cube, 
and what's nice about this is the number 

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of hops to get from here to anywhere else 
is quite low. 

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So let's, let's, let's think where the 
farthest point in, in this. 

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Cube is going to be. 
So it's going to be, let's think, is it 

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here? 
Or is it here? 

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One, two, three. 
No, it's the other one. 

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Okay, so it's going to be one, two, 
three, four to get to there. 

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Is going to be the farthest number of 
hops. 

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So we can think about having these higher 
dimensional systems. 

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Now, as may be readily apparent but I'll 
say it anyway, if you try to build a five 

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dimensional hyper cube in three 
dimensional space some of the wires are 

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going to get long. 
Because you can't fold five dimensional 

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space into three dimensional space. 
You might be able to span a 4-dimensional 

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space into a 3-dimensional space with 
not, not too bad. 

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But there's kind of this good rule of 
thumb when you're building networks that 

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you probably don't want to be building a 
N, 

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you probably want to a map let's say an 
N-dimensional network into N-dimensional 

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space. 
Trying to map higher is painful, 

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trying to map much, much higher is very 
painful. 

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The benefits though is that, you have to 
have fewer routing, hops to get to be 

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able to get wherever you're going, in the 
higher dimension cube. 

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Now, I want to just throw this one up 
here because this is an interesting 

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topology. 
Just connect everything to everything. 

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This seems great, we should all build 
these networks, [COUGH], unfortunately 

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let's think about trying to put this 
network in three space if we have 1000 

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nodes. 
Well, what that means is, if you have 

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1000 nodes. 
You're going to, each node is need, 

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you're going to need to have 999 outbound 
connections. 

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And 999 inbound connections. 
So when you go to build this, you might 

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be able to build this, sort of, in a, 
outside of a sphere. 

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Or, sort of, a big wiring mess on the 
inside. 

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that's pretty hard to do at, for, for 
large numbers of nodes. 

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For smalls numbers of nodes, something 
like a fully connected crossbar, or 

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what's also known as a star topology, 
will probably work fine. 

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But 
And in fact if you cut it sort of across 

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the middle of the network here, which 
we'll talk about in a second. 

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It has very good bandwidth. 
Okay, a few other things you guys should 

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know about from a terminology 
perspective. 

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[COUGH]. 
The networks that I've drawn to this 

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point. 
On what are called direct networks. 

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Which means that the nodes have some type 
of router built into 'em. 

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You could also, and people, plenty of 
people build this, is they build networks 

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where the nodes do not have routers built 
into them, but there's a multistage 

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network or some sort of network in 
between the nodes. 

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So a good example of this actually is 
something like your Internet if you will. 

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There you have computer nodes, and 
computers are not doing the routing 

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themselves. 
Instead they send it out to a what we'll 

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call an Ethernet switch, and the Ethernet 
switch is lets say one of these boxes 

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here in the middle. 
And that makes a decision and sends it on 

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further. 
It could be a, a internet router, could 

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be in the middle here also. 
And the analog in computer architecture, 

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of what we're building is, you can think 
of these, building these massively 

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parallel machines. 
Something like a, massively parallel Cray 

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machine, or something like that. 
They actually have multi stage networks 

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in the middle here, and all the nodes 
kind of sit on the outside. 

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Note the way I numbered this, this is the 
same thing as that, but I just didn't 

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want to draw all the wires going around. 
So, here, we're sort of drawing as if 

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data's flowing strictly from left to 
right. 

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[COUGH] And what we did here is we have 
eight nodes. 

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And, what you see here is there's, log 
two, number of stages here. 

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If you have. 
Two to one switches along the route here. 

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And something like an omega network 
actually has only one path between each 

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point, so if you want to get form here, 
let's say form eight to three. 

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You're going to have to go here, here. 
there, there. 

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You can't, there's no other paths you can 
take. 

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So let's say you routed straight at this 
first decision point, and went here. 

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You went up, you never really got up to 
three. 

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This is in contrast to some other 
multistage networks, where people 

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actually put in extra stages in the 
middle here. 

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00:14:15,960 --> 00:14:20,799
And this gives you some path diversity so 
it'll allow you to route around in 

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congestions or route around problem 
links. 

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00:14:23,439 --> 00:14:28,655
If you were to add, if we were to add one 
more stage it would look exactly the same 

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as the rest of these stages. 
because if you look here all these stages 

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have the same wiring in the middle, if 
you'll note. 

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00:14:35,946 --> 00:14:40,848
[COUGH] If we add an extra stage, we'd 
actually be able to have multiple links 

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or multiple paths between the end points. 
And sometimes that's good. 

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00:14:49,860 --> 00:14:55,520
Another type of network here that you can 
build is a tree. 

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I would say interesting topology. 
What, you probably want a tree though is 

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and. 
If you, if you want to be able to 

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communicate, let's say, from this half to 
that half of your machine, you're 

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going to want to somehow make the links 
wider as they go higher up in the tree 

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00:15:16,844 --> 00:15:20,846
and that's what we call a fat tree. 
So a fat tree doubles the links each 

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level up in the hierarchy and then you 
have, let's say, this node wanted to 

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communicate with that node. 
There is enough bandwidth across this 

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back plane here, to support lots of nodes 
over here, communicate with lots of nodes 

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over there. 
One other interesting about a factory is 

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if you go and try to implement this 
across a 2D space, we'll say. 

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00:15:44,647 --> 00:15:51,127
And you try to map this into 2D space, it 
actually starts to look pretty similar to 

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a mesh at some level, except it looks 
like a mesh that you removed links from. 

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00:15:58,040 --> 00:16:03,247
So, think about that if you ever go to 
sit down to build a, tree, or a fact 

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tree. 
It actually looks just like a mesh, 

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00:16:06,980 --> 00:16:12,052
except when you go to do the mapping of 
this you'll basically see that there's 

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this node and this node are very close in 
the 2-dimensional layout. 

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So you could just run on local wire, a 
sneak path between these two. 

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And then you'll also realize that this 
one and this one will be really close, so 

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you just run a sneak path between them. 
So to some extent some of these 

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topologies make not make as much sense in 
a certain packaging or a certain layout 

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in physical space. 
But if you, let's say, have multiple 

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chips, some manufacturing may make sense. 
[COUGH] So I wanted to introduce a piece 

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of nomenclature here that you'll see for 
mesh networks. 

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That you'll hear sometimes people talk 
about things as K-Ary N cubes. 

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And this using two numbers looking to 
completely describe a type of mesh 

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network. 
And we're going to use two numbers here. 

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The first one is K. 
So if you say phi vary, what that means 

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is, this is the number of. 
Nodes in any one dimension. 

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And, the n here in our n cube is the, 
number of dimensions. 

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So we can, this'll give us a way to 
describe things that are not strictly 

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hypercubes. 
So we can describe, sort of, other 

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shapes, but that are still some sort of, 
cube. 

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So, for an example here, we'll look at a 
three by three by three cube. 

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So this is kind of like a Rubik's Cube, 
or something like that. 

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And each one of the, the blocks in the 
Rubik's Cube corresponds to a node that 

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wants to communicate. 
And this is actually a three ary, three 

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cube mesh with no end around. 
And 

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we can see that the worse case path-link 
in here is going to be from here to here. 

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So it's going to be 1, 
2, 

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4, 
4, 

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5, 6, is that right? 
2, 3, 4, 5, 6. 

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That's how many links we need to traverse 
to get from here to the farthest way 

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other point in the system. 

