So let's talk a little bit more about what this class is about, but we'll, we'll do
it by way of a little bit of history. And to put a little bit of a Princeton
connection in here, we're gonna talk computers back in the late '40s, early
'50's, nineteen, 1940s, 1950s. So, here's actually a, a picture of the
IAS, or the Institute for Advanced Study Machine, which was built in the Institute
of Advanced Study, which is maybe about a mile and a half away from this classroom.
And it was designed by Jon Von Goyman, and, and to give a little bit of insight
here. It was first moved in Princeton in 1952,
it was actually started in the late'40's and took them a couple years to get, to
get working. And one of the interesting things here is
that this machine is actually built out of vacuum tubes.
So, everyone thinks about transistors today.
Well, before we had transistors we had little glass tubes that actually looked
like light bulbs, and inside of those there were switches that could be
switched. So, very similar idea to what our
transistor can do, but, instead you had a evacuated glass tube, and you had a little
transmitter, it was a cathode ray tube. And then you had a gate that could open
and close inside of this. So people were building computers long
before transistors, and people were thinking about computer architecture long
before transistors. And people were even thinking about
computer architecture and these sorts of technologies even before vacuum tubes.
So there was electromechanical systems, a good example of this actually was
originally in phone systems. They had these cool electromechanical
switches. So when you take your old rotary phone and
you sort of turn it and then it goes, tick, tick, tick, tick, tick, tick, tick,
tick, tick. What that's actually doing is, it's
sending pulses which are turning a mechanical, a little mechanical arm inside
of a relay system, and people built computers out of those electro-mechanical
relays. So you can have switches that turn, change
switches, similar sorts of ideas to transistors.
And even before that people had looked at building mechanical systems.
So there's mechanical adding machines for instance.
And now of days we, of course, have transistors.
So, it was computing then, in the fifties and if we fast forward to today, we have
lot, computers look very different. In this figure here, ya know, this thing
was the size of a room. Pretty big room TV scale sort of, a person
is maybe yea tall in this figure thing. It's a, sort of normal-sized room, but,
still sort of room sized. But today we have lots of different
applications. And computing looks very different.
So, we have computing, let's say, in small systems.
So we have a little sensor network-, networks, and little sensor nodes
distributed. We have fancy cameras.
We have smartphones. We have mobile audio players and iPods.
We have laptops, like my laptop here. We have self-driving cars, we have big
servers, we have Xboxes, and there's a lot of variety now in what computing systems
look like. So, the influence of this technology,
computer architecture, has been very, very broad.
And we even go on to seeing things like routers, flying, unmanned autonomous
vehicles. We have GPS's, which are little computers
that can basically fit on your wrist these days and tell you exactly where you are.
E books, tablets, set-top boxes and the list goes on and on and on.
And what, what, I want to get across here is that computer architecture has a very
rich history. And this history is continuing and it's
very relevant today. So we're not studying something which no
one cares about anymore. People are sitting there sort of ready to
get the next generation computer architecture.
People, and, and it used to be you know, you want your faster desktop computer.
And that may not be as important today, but what is important is people want their
faster smartphone. They want to enable voice recognition on
the go. They want to be able to, in scientific
applications, they want to build a model, some health system that's really complex,
that you weren't able to do before. So, it continues on and on.
It is very relevant today, and it has a very rich history.
In this course we're gonna talk a little bit about the history, we'll mostly focus
on the technology. Sometimes when people teach computer
architecture classes, they have much more emphasis on the history.
This class, we're gonna Touch on history a little bit, but more focused on the, on
the technology considerations. So here's a chart that's from Hennessy and
Patterson's Computer Architecture, or A Quantitative Approach, and what this graph
is trying to show is, is something very fundamental to computer architecture, and
it's what's been driving our industry. So what we see here is we see different
processor designs plotted on a log plot. So this is ten, 100, 1000. This is a log
plot and it says performance versus years. So, if you look at this, this plot, you'll
see that, well this roughly looks like a straight line, and a straight line on a
log plot is an exponential increase in performance.
So, we've seen computing going up exponentially faster.
And this is a, a really fundamental to driving what's been going on in our
industry and why computer architecture is so important.
And I do want to say that, you know, this exponential increase is, comes from two
things. It's not all computer architects, I'd love
to able to sit, stand here and say, "we did all this." Well, no, a fair amount of
this is from getting, better, better technology.
What I mean that is the, lower down layers and the implementation technology, like
the transistor technologies. And some of it is from having better and
better computer architecture. And what is really important here to note
is even if you have better and better transistors.
A lot of times what happens if you look at this graph is what's happening is you're
getting more transistors, but those transistors are not necessarily
exponentially faster. So, what computer architects have to do is
we need to figure out how to take buckets and buckets of more transistors and turn
them into more performance. And that's what this is many times called
is it's called Moore's Law. If you've heard that term before, what it
is, is we're trying, Moore's, Gordon Moore said that every eighteen months to two
years, you're going to get twice as many transistors which, you can have for the
same amount of dollars. That was what was originally said.
People sort of transformed that now into meaning your computer's gonna get twice as
fast every year. That's not what, Gordon Moore originally
actually said. He said he can get twice as many
transistors for a certain amount of dollars.
And people have also sometimes taken this to mean that you get twice as many
transistors on a chip every year. It's not quite what he said, but close
enough approximation. And when, I don't quite have the graph
here. But if you look at computing in general
across, this, this is all across transistor based technologies.
But if you go farther back into the past, you can actually plot other technologies,
like vacuum tube technologies, and relay based technologies.
And it also fits on this, this graph relatively well.
So sort of, if you continue down here, you're gonna see vacuum tubes show up.
And it's sort of still on this exponentially increasing curve.
Okay, so let's look at, there's two inflection points in this graph that we
wanna look at. First one's right here, you can see that
the slope changes a little bit. Well, what, what happened here?
This was the introduction of reduced instruction set computers, or risk
computers. So we got a little bit of a, a crank up
there when people came out with the first risk.
And, another thing you notice is, this graph keels over a little bit here, and,
we are gonna be talking a lot about this in this course, is, what happens or why,
did this happen? So, what, what happened here?
Well, sequential performance, so this is the performance of a single program; was
getting faster exponentially. But then, I don't know, depending on who
you ask. I like to use 2005 as the number but
somewhere between 2003 and 2007, sequential processor performance started
to, to really have a, a problem. But overall performance of your processor,
still continuous to go up today. And what happened is we had to move to
multi-core processors or multiple cores on a single chip.
And hopefully, the hope is, that this graph will continue on here with multiple
cores, If we can figure out how we can effectively paralyze our programs, versus
our sequential performance tapering, tapering off.
Cuz it would be very harmful to computer architecture and computing industry if all
of a sudden our computers stopped getting faster, no one would be buying new
computer chips.
