WEBVTT

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I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't

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know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know

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if I can hear you, but I don't know if I can hear you, but I don't know if I can hear you, but I don't know.

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up and do. I'm just going to ask you to be quiet and positive and we'll just see how we get along.

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It's not like the podium.

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It's like the stadiums, the lights are going to be loose.

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We both are coming.

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Okay.

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Let's think about the idea of the mechanical scene.

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It's when we use a tool with an understanding of how it operates.

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So the best of my knowledge is with action is which you don't support

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and you're going to have to be quiet and positive.

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You're going to have to be quiet and positive.

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You're going to have to be quiet and positive.

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You're going to have to be quiet and positive.

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You're going to have to be quiet and positive.

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You're going to have to be quiet and positive.

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You're going to have to be quiet and positive.

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You're going to have to be quiet and positive.

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You're going to have to be quiet and positive.

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You're going to have to be quiet and positive.

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The focus is that when you can't worry about the canvas in,

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and that's what this talks about amazing clarity of machine that will

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flow down upon hitting action and running up.

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I understand that the machine underneath

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is really capable.

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It sounds to look like a sequential attributes of maximum abstraction,

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And knowledge of where they are structured and that can help you understand why your program

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performance was possible and what you might do, like the web, this is a slight oversimplic

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touch.

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What we are actually looking at here is a bass guitar in the series with a viscous bass, and I'm

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going to make a tune with a rosary symphony function, of course what it can be

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actually is just a bottle of sweat, but it has acidity and acidity.

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Now if you do a few calculations and I hope there's no electric engine as much as we can.

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You assume that the reactions we get will, you assume that the reactions or the

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acidity are substantially greater than the reactions of the musicians in the series.

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Let me do a few calculations based on the impedance.

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You know that there is a specific power, you do a few calculations.

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You realise very quickly that the power increases what we are actively with the frequency

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of the power.

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It's double the frequency of the power, it's a quarter.

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And this unit, for every single calculation you do, it's quite such a factor.

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When you're going, so it takes twice as much power, it takes twice as much energy

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when you're going at high speed, then it does more and less.

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So, every operation is possible twice as much when you're going at high frequency.

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This is why high performance computers of selects from a five gigahertz or so or maybe 10 years

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and they've gone down because we're limited by power.

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This first sentence is again slightly untrue.

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But it's sort of similar.

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If you look at the heat transfer coefficient, that's a super-uncooler can do.

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It's not madly different from the heat exchanger in the primary cooling circuit of the nuclear air.

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The stony shing got true.

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Of course, nuclear reactors are shifting more because they're hot, why, usually.

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And a modern super-uncooler is a true main space energy.

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We can increase power in the structure in the air.

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This is particularly true in data centers where, I think every day to center, I'd have a visitor.

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There's over extra-regional design budget and power by the percentage.

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And we're making sure all of this physical limits.

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So, we can't go anywhere else.

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And there are very good physical reasons for improving that.

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We're doing the other thing to be able to go any faster, least with the spikes of computers that they have in our boxes,

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all of them, right there, just something to do.

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So, we're going to have to go and buy what we're going to have to do.

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Instead of doing things twice as fast, we're going to have to do a lot more functions in power.

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And this is something that's been going on for a very long time.

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In fact, it's very wide parallel computers that pretend to be sequential machines.

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We've got back to the end of the 1960s.

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When TJ Watson famously asked an engineer,

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why you've got hundreds of people working on the system 360,

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but it's still slower than this computer over here,

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designed by 12 people working in a shed.

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One of the answers was, well, it's because you've got people working obviously.

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The guy with 12 people in a shed was Seymour Craig.

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And that's what perhaps it is.

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But the response of the end was to invent something like the modern superstandard of very wide issues.

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This is what the computer of today actually looks like.

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And if this one thing I'd like to use to take away through this such thing,

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just thinking about what this actually is.

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I'm going to talk here.

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This is your construction.

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On every single block cycle,

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10 instructions are deployed in parallel.

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Now this is an answer to four computers.

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There are similar diagrams for Intel X86 computers.

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They're decoders are much, much more complicated,

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because Intel has variable length instructions.

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On Intel, an instruction is just the stream of bytes.

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So, you don't know where it's going to start, you don't know where it's going to end.

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Something sometimes you don't know where it's going to end until you actually get what I do.

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So, instruction you've got to really tell is very, very much harder.

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And the reason Intel is looking for is the life of this,

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is because it was my time to do this.

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When your architecture was inventing 10 modern 10 years ago,

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then you can get it.

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So, all of these instructions are decoded.

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This is interesting.

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Looking at this.

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What it's telling you is that although the architecture of the 64-bit architecture

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only has 32 or 64-bit,

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only has 32 or 64 registers.

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The critical, what?

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You didn't expect to do that.

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Probably it.

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The physical machine has nine hundred physical registers.

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So, what happens in the front here,

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when you start decoding instruction,

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there's what's called a rename table,

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which basically says, okay,

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when the next instruction acts for register 16,

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you actually want to register 571.

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So, it's a massive look for table.

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But, of course, if you're going to execute all of these instructions

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in a different order from the art of program,

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you've got to do a tremendous amount of work

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keeping track of what all the things they're going to be in the cell.

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And that's the real one of them here, which keeps...

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So, this is what's actually going on.

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And you can see here, and you can see here,

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there are different instruction blocks

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where things that you need to do.

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So, this is what the machine actually looks like.

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Now, most people, even people do work on compilers,

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who are living, tend not to think in terms of this amazing machine

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that it really is.

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Most compilers, even sophisticated, optimised in five.

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I don't know about any of them.

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They're actually based around the model

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or a straightforward, in-line issue,

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register and choose.

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But, conditional branches are very, very common.

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Some people reckon that there's a branch

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every five or six instructions, something like that.

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So, in order to use all of that machinery there,

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you have to predict where the program is going to go.

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This is for speculation, this is for branch production.

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But, memory loads are predicted as well, not just branch,

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so sometimes a machine will simply say,

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okay, I've been reading memory sequentially.

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Let's just predict it in the absence of any other language.

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What we're actually, we're just going to keep reading things exponentially.

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And this works in a lot of the time.

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Almost all memory reads a sequentially.

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So, it's just thinking about what a clock cycle actually is

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from, it's a simply socorration you've got.

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It's logical operation, an an and an or an add or something like that,

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which is typically limited by some number of gate fields,

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which is like 16 or 20 typically.

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Memory comes from 11, one catch is a lot more than that.

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That's a whole nanosecond.

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The multiplication is multiplied, et cetera, et cetera.

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So, memory reading data, it takes forever and ever and ever.

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And it's a huge, huge latency, et cetera.

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So, what you actually want to do is try to arrange your program,

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so that it works well on that sort of thing.

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But I imagine that most people program me, I don't know,

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really be aware of it, but it's not just because the CPU design is trying to find

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what I'm trying to do.

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They really don't want me to know.

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They're trying to put people affect the notion.

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But occasionally, as we saw with spectra meltdown bugs,

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this very carefully hidden abstraction.

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But it also affects how past your program is going to run.

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So, these are three things that I've done, which affects it by this.

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So, it's about years, not how I've mentioned the earlier,

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and no job feature, but reading a step about the conceptual

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walks up the job of a stack to find a caller.

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And then your search can be stopped.

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So, we're looking at various things with the proposal.

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And then the end, I'm not really a 16-wide software catch,

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entirely software managed, which is local to each track.

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So, whenever you look for the value of a stack of value,

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rather than walking down the stack, you just look it up in this touch.

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Right?

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Now, I think you can predict everywhere,

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so the fact sure is that, and there's no really anything like,

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so I end them into the software catch.

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And it turns from being a slow look to the stack,

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into something that was literal and believable.

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Like, in many cases, there's no hope at all.

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Some of this was the thing we have at all, and it's like,

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the job is certainly important.

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And all from one of them is the combination,

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the process of just standing forward,

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and walking through the stack, I don't know what that is.

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That's just there.

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So, it was essentially, it's all the same.

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So, this idea, if you've got any look up,

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you're going to do it.

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So, what you have to do is,

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the fields are going back to China.

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Just a tiny little thing, which I mean,

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the one that's actually in the stock value source type,

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is something that's not very simple,

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and it's actually in China, because it works in Europe.

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But it works in quite a bit faster.

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It's particularly good because I spent a lot of time

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looking at the actual assembly code output,

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that the C2 compiler was producing for access

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to the stock value range.

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So, it's more cash is highly effective in many other ways.

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Okay, so last year, I think to us,

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that much of the year, doing this,

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there was a problem with the old way

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that's interface,

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membership access are done.

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The old algorithm is very fast,

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but it really started parallel in the multi-fledged line.

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So, when it was first designed,

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this was 20 year old code.

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It was perfect.

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But now, as soon as the difference

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red started asking the same question,

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the performance just died,

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because you've got a tremendous amount of cash

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to hear and strap it going between the various processes.

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And it was extremely slow.

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So, the algorithm, benchmark,

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is at 1.4 meters.

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So, what I've said earlier,

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you can see that there's no way you can possibly check anything

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at 1.4 meters.

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That's essentially nothing.

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I mean, if you're looking at the speed of light,

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it's about that.

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So, it was never going to be like that.

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So, I thought, at this point, I got interested.

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I don't believe that.

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What is actually, I don't know.

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And then it is this,

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to simply, you will always inspect it

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with the chance to ask for a speed.

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Because, simply, you notice that it always does,

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thank you.

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So, unless the guarded code uses all of the simply,

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you will, which is very likely,

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especially in that monster,

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so check that's for so much nothing.

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And here it is.

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Now, you probably cannot see that back,

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and there probably can't see it from either.

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But this, this first part,

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is the check-out.

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It's that.

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It's, I think, nine instructions.

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And you see, these, this is a, this isn't the arm process

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that I showed you,

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because I can't get a pipeline model for it.

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So, you can see that the actual code

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that's guarded by the check code

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actually begins to cycle

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after the check-out code is first-ish.

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Now, going to the check-out code

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takes until there.

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So, it actually takes 30 seconds.

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What it doesn't matter,

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because the process of executing it's always.

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And if the check-out file was,

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it will throw away all of this.

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And rewind every three.

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Throw away all that work at the start again.

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So, this is the failure.

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I wanted to do more of the same by improving

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the other of the use of the invoke interface.

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I thought we could do something similar.

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I thought we could do a nice hash table look up.

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This is a benchmark in open JDK.

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And when it runs on the machine,

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I was testing it on it.

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It runs in less than four nanoseconds.

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That is completely impossible.

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Clearly, it's doing something.

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And what it's actually doing here,

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it's predicting memory loads.

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So, I changed the benchmark into scrambled

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in order to be tested into a different order.

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And all of a sudden,

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or who this is what happened.

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Yeah.

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This is actually executing 75 instructions.

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So, I changed it.

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And all of a sudden,

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the performance went right, right down.

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So, it was all about production.

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So, I'm running out of time,

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and this is perhaps a long at all.

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But the point is that invoke interface

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is legendarily in Java,

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supposed to be a slow operation.

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It really isn't a slow operation.

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If it's well predicted,

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if it's poorly predicted,

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then not only is the code that does to knock up

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like it to be predicted badly,

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but all of the target method is going

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to be predicted badly too.

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And simply scrambling that,

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it used the performance so badly

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that it was doing six instructions

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like down to less than one.

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Because it is always executing

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the whole net in twice.

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Once with the right in operation,

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once with the wrong information,

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spectacular.

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Once again.

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So, if you're wanting to take away from this,

24:09.760 --> 24:13.760
invoke interface is nothing to the scale.

24:13.760 --> 24:17.760
Interpaces are a very hard and solid general.

24:17.760 --> 24:21.760
But if you want them to be executed quickly,

24:21.760 --> 24:25.760
then that will only happen if prediction works accurately.

24:25.760 --> 24:28.760
Now, we don't know exactly how prediction really does work.

24:28.760 --> 24:29.760
But it tells you,

24:29.760 --> 24:31.760
keep it simple.

24:31.760 --> 24:32.760
Keep all of your stuff simple.

24:33.760 --> 24:34.760
If you try and do your lab work,

24:34.760 --> 24:36.760
long-term tests and so on,

24:36.760 --> 24:38.760
you're probably going to lose the prediction,

24:38.760 --> 24:39.760
which you need.

24:39.760 --> 24:41.760
And if it's quite what happens,

24:41.760 --> 24:43.760
I like my advantage here,

24:43.760 --> 24:45.760
everything I try was worse

24:45.760 --> 24:47.760
and simple as a pencil search.

24:47.760 --> 24:49.760
Keep it simple, Steven.

24:50.760 --> 24:51.760
Thank you.

24:51.760 --> 24:52.760
Thank you.

24:52.760 --> 24:53.760
Thank you.

24:53.760 --> 24:54.760
Thank you.

24:54.760 --> 24:55.760
Thank you.

24:55.760 --> 24:56.760
Thank you.

24:56.760 --> 24:57.760
Thank you.

24:57.760 --> 24:58.760
Thank you.

24:58.760 --> 24:59.760
Thank you.

24:59.760 --> 25:00.760
Thank you.

25:00.760 --> 25:01.760
Thank you.

25:01.760 --> 25:02.760
Thank you.

25:02.760 --> 25:03.760
Thank you.

25:03.760 --> 25:04.760
Thank you.

25:04.760 --> 25:05.760
Thank you.

25:05.760 --> 25:06.760
Thank you.