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Network Performance
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From the course by Princeton University
Computer Architecture
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From the lesson
Multiprocessor Interconnect 2
This lecture covers the design of interconnects for multiprocessor and network topology.
Meet the Instructors
David WentzlaffAssociate Professor
Electrical Engineering
Okay, so now we start talking about performance of our networks, and there's
two. Main thoughts I want to get across here,
there are two really godo ways to measure our networks, bef, when we talked about
performance here we were talking about parameters rather of the toplogy now
we're going to look at the overall network performance.
First thing is, bandwidth. So bandwidth is the rate of data that can
be transmitted over a given network link, divided by amount of time.
Okay, that sounds pretty reasonable. Latency is how long it takes to
communicate. And send a completed message between a
sender and a receiver, in seconds. So, the unit on this is seconds,
the unit on this is something like bits per second, or bytes per second,
so the amount of data per second. These two things are linked.
So, if we take a look at something like bandwidth, it can actually effect our
layency. And the reason for this is, [COUGH], if
you increase the bandwidth, you are, are going to have to send fewer
pieces of data for a long message. Because you can send it in wider chunks,
or faster chunks, or something like that.
So it can actually help with latency. It can also help with latency because it
can help reduce your congestion on your network.
Now we haven't talked about congestion yet, we'll talk about it in a few more
slides. But by having more bandwidth it will, you
can effectively reduce the load of your network and that will decrease your
will decrease the load in the network, and then increase the probability you're
actually going to have two different messages contending for a same, link in
the network. Leniency can actually affect our
bandwidth, which is, interesting,
or rather it can affect our delivered bandwidth.
It's not going to make our, if we change the latency, it's not
going to make our lengths wider or our clock speed of our latency faster,
but if you make the delivered bandwidth higher.
Now how this can happen is let's say you have something like a round-trip Your
trying to communicate from point A to point B,
back to point A. And, this is pretty common, you want to
send a message from one node to another node, it's going to do some math on it.
It's going to do some work it and it's going to send back the reply, and if you
can't cover that latency, if the latency were to get longer, the
sender will sit there and just stall more,
and it will effectively increase the it will decrease the bandwidth of the amount
of data that can be sent. [COUGH] Now if you are good at hiding
the, this latency by doing other work, that may not happen.
You may not be limited by latency. But then, another good example is if you
are worried about end to end flow control.
So a good example of this is in TCP/IP networks.
So like our ethernet networks. There's actually a round trip flow
control between the two end points, which rates limits, the, the bandwidth.
And it's actually tied to the latency. Because you need to have, more, traffic
in flight to cover the round trip lanes see, and this starts to get, in j, starts
to be called, what's called the ben with delay product, where you multiply your
been with by the delay or the latency of your network, and if you increase the
latency. The bandwidth will effectively go down if
you do not allow for more traffic in flight before you wait, before you can
hear a float control response. [COUGH].
So you'll see this if you have, let's say, two points on the internet.
And you put'em farther apart. And you have the same amount of in
flight, data, or what's called, the window is the same.
[COUGH]. The bandwidth is going to go down as you
increase the latency. But, if you were to increase the window,
it would actually, stay high. because bandwidth delayed, probably.
And the reason for that is you'd be waiting for acts to come back from the,
the receive side. Okay, so let's take a look at, an example
here, to understand these different parameters.
We have a four node omega network here, with two inputs, two output, routers.
Each of these circles here, represents input nodes,
and these are the output nodes, and they basically wrap around, they're the same
sort of thing, [COUGH]. We have little slashes here, which we'll
represent as serializers and deserializers.
So what this means is, you're transmitting some long piece of data, and
it gets sent as smaller fits, if you will.
So we're setting let's say a 32 bit word, and it gets serialized into four 8-bit
chunks across our network, across the links, because the links in the network
are only four, or excuse me, eight bits wide,
we'll say. [COUGH].
And in this network we're going to have our latencies be, non, non-unit.
So let's say our link traversal here, each link here takes two cycles,
takes L0 and L1. And our routers take three cycles.
R0, R1, and R2. And, to go from any point to any other
point in this network, you have to go through two routers and one link.
So we can draw a pipeline diagram for this.
So for a given packet, we can see, let's say, it can split into
four fits here, of the head fit, two body fits and a tail fit.
We started the source and we sent, well it takes three cycles to make a routing
decision through here, two cycles across the link, three cycles across one of
these routers here and then we get to the destination.
And if we look at this in time, it's pipelined.
We can have multiple of these things go down at the same time.
So if you have the next FLITs one cycle off, or one cycle delayed.
And the reason we want to draw this, is we want to look at what our latency is
for. This send sending this one packet.
because it's a little bit hard to reason about, because we're effectively, have a
pipeline here. We're overlapping different things.
And we'll see that one of the things you'd think would be up there doesn't
show up down here. So first let's take a look at this, we
have four cycles here at the beginning which is just our serialization of a to z
or the length of the packet divided by the bandwidth of the packet.
If you were to increase the bandwidth here, the serialization latency would go
down. [COUGH] Then we have, time in the router,
which is our, router pipeline latency. So it's three cycles here,
and another three cycles in the second, router.
And if we have more hops, this will go up.
And then two cycles here for the channel latency which we'll tall, call t c.
So, you can see that it's the summation of all of these different latencies,
is our latency, but what is interesting to see is that there is no
deserialization latency here. So, that's the one that's missing, and
it's because we've overlapped that because it's pipelined, we're counting
that in the serialization latency. Questions about that so far.
Okay, so now let's take a look at our message latency, and go into a little
more detail here. If you look at our overall latency which
we'll denote as t, it's the latency for the head to get to the receiver, so
that's all of this stuff here, plus the serialization latency.
Now, T head has our TC and our TR, and a number of hops,
but it also has something here that is a contention,
which we haven't shown. So, in this number here, there was no
contention. This is an unloaded network.
There was not multiple nodes or multiple messages trying to use one outbound link
or use any one given link in this design. But it can happen.
Let's say these two nodes here send at the same time and they both need to use
this link. You're going to get contention,
and that will increase our latency. But if we, if we rule out the contention
for a little bit of time, we'll start to see the unloaded latency
here. And we just decompose this into sort of
sub-components here that we have the [COUGH] routing time, times the number of
router hops that we need to go, plus the channel latency times the number
of channel links we need to hop across, plus the serialization latency.
And the reason we decomposed this, is this lets us reason about how to make
networks faster. So we can see that there's a couple
different ways to make our networks faster.
First thing we do is we make shorter routes.
That'll decrease both these upper case H's here.
The reason I have two different upper case H's is it's.
As you can see here, In this example, we basically went two
router hops and one link hop. usually, they're connected, though.
If you have to go farther, you need more links, and you need more router hops.
You can make the routers faster. So you can either increase the clock
frequency of the routers, you can make them wider if they take multiple cycles.
[COUGH]. Now, if they're already, sort of, as fast
as you can go, it may be hard. You might be able to increase the clock
frequency somehow, but it is, it could start to get hard at some point,
if there are already wide channels, and wide muxes, and have a fast clock rate.
Faster channels. So if you go in between multiple chips,
usually you're limited sort of by the signal integrity of the communication
links between the different chips. And this sometimes even happens on chip.
So you have to think about that, that going to a higher clock frequency could
be, could be problematic. But if you make a faster channel, your
latency's going to go down, and then, finally,
this is our serialization sort of cost here.
And we bake into it here either wider channels or shorter messages.
Maybe you have a lot of overhead on each message.
You have a, a, a really big header for the message.
If you try to shrink that, that'll make your network go faster and reduce your
latency. Just by sending less work,
or sending less data, but that may not always be possible.
but I'll give you an example of this. If you look at something like, TCP on top
of IP networks, in sort of our internet class networks,
people have proposed a whole bunch of, revisions to that,
where they try to sort of squeeze out some bytes.
Or use some sort of encoding standards to reduce the amount of data in the headers.
Because TCP header's pretty, pretty long, for instance.
And you already see a good example of that,
they actually have an optional field in TCP headers, which you, is typically not
sent, thereby reducing that in the common case.
Okay, so now let's talk about the effects of congestion.
[COUGH], so what I, what I drew here is. A plot of our latency versus the amount
of bandwidth that is achieved, or offer bandwidth.
And this is for a given network. So it's pretty common that as you
increase the bandwidth that you're using at any given network,
the latency of the network goes up because you start to see more congestion
in the network. So the probability that any two points
are can, are contended for will go up as you get closer to the maximum amount of
maximum achieve in the bandwidth in the bandwidth.
[COUGH] Now there are some networks that people build where this is not the graph
does not look like this [COUGH] or does not look like that.
So for instance, if you have a start apology, you don't have any congestion.
So, you're going to get something that looks much more like an ideal plot here
where if you're going to have a straight line and another straight line.
Because as you increase your load to the network, everyone can send to everyone
else, so it's not going to be congestion in the network.
And I have a few lines here that sort of show interesting things that sort of hack
down at this. So, in a perfect world you'd have your
zero load latency, so this is the latency of the unloaded network, and as you
increase the bandwidth. It wouldn't change.
On our unloaded network. And with so.
And, and if you had no, no conjecture in the network.
But we start and, and but that's, that's not usually what you see on, on real
world networks. Couple of things sort of, also, decrease
or increase the latency and decrease the bandwidth of a network.
Usually, you have some routing delay that gets introduced into the network.
And that's going to basically push us away from higher bandwidth and lower
latency. So you want to be farther down in this
plot, 'cause that's lower latency. [COUGH].
And also, if you have flow control in the network.
So, local flow control. That also looks like, some form of
congestion. It'll actually slow down your network in
certain cases. But I just wanted to give you guys the
idea here that. For any, real world network it usually.
Looks something like this. And as you get closer and closer, to
using the whole network. You're using all the bits available, by
the network. The latency starts to shoot
asymptotically, through the roof.
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