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	Hi, I'm Stephen Jones and I'm one of the
	And one of the ways that that comes out is that there are a key performance elements.
	be this one and a half terabytes per second data, right?
	do the division, that's 194 billion double versus values per second.
	giving me a peak performance based on memory of just 190.
	for gigaflops. Right now I'm only beating the 1996
	computer in the world.
	So let's have a look at this memory thing. Let's have a closer look at.
	how it works because it's so important in the performance.
	machine. A single bit of memory is a capacitor.
	And now the holes are fucked for one bit on the left or it's empty for a server.
	The memory is read by switching on the transistor which can be...
	you need to take into account. Even while the bulk of your program can be pretty naive, see, plus,
	exit to a wire, the bit line. The wire then carries a
	based on the charge in that capacitor so it's the wired record either.
	and honor and offer one or a certain.
	DRAM chip consists of millions of these cells all connected together.
	a big 2D matrix. This matrix layout lets me access any row, any call.
	And this is why it's called random access memory. That's the random access.
	as again say a magnetic tape which has not in your access.
	Data is addressed by a row and a column index.
	We are taken from the requested address.
	First, the row is accessed. All the cells in the row...
	plus I'm not going to teach you could today there's not enough time for that.
	activated and their state is copied up to these things called the sensor.
	and the sense amplifiers read the tiny chart
	on each of the capacitors in the cells and turn them into well-defined bolts.
	to do that can much more easily be read in the next set. The problem is...
	the charge and the capacitor is drained as this happens right, I'm connecting a wide.
	to the capacitor's drainable the electrons out. And so the data in the row is destroyed.
	I'll come to that in a moment. Next.
	the column access takes place. Instead of reading from memory cells,
	row is already in the amplifier so it reads the data held in the amplifier.
	much quicker, much easier to read than a row because the amplifiers will produce a strong clear
	But I'll teach you a few things that I think are vital to think.
	signal and so I can read more quickly.
	You can read repeatedly from the amplifiers because they hold that voltage. You can read as many
	times you like from the Festrow. So if you can open a row and use it repeatedly.
	then you will not have it deal with the capacitor.
	Because it's so common in fact to read adjacent memory locations in a
	There's this thing called burst mode, where a single request returned multiple weather.
	date. This is a huge deal because it means I don't have to pay for the individual request.
	over again and pretty much every processor in the world uses this because the
	system of the processor is always going to go and read multiple bites at a time.
	And then GPU the Cassistum is 128 bytes a time. I'll talk about the Cassistum.
	about when you're programming the GPU. I think the most
	of that. The problem is the way
	I need to read another row. I first have to write back the
	data which was held in the amplifiers. If you remember, the row was drained when it was...
	it was copied into the amplifiers because the capacitor is discharged. So we now have to...
	rewrite it to avoid memory corruption. So this makes a page
	which expensive could involve both the right back and then a new road low.
	up the new road load into the amplifier.
	Hardware, course things, roads or pages pretty much interchangeably, so if you hear the term.
	page, then this is what they mean. I mean a row of your memory switching page.
	It was about three times as expensive as switching column within a page because of this look.
	important thing when doing any engineering is to have an accurate mental
	store operation, sorry store and load operation. So, put in your couple of
	model of the system they are using.
	So today, a mental model, I really think the best way to understand the how of something is to know.
	why it's that way. So this talk is really about why could
	is the way that it is, not just how.
	architects of CUDA. I've been working on the CUDA programming model in DB.
	That's a good question. Why is Coup de la Weyrt is?
	Right. It's the way it is because.
	of the laws of physics, quite literally. So, what
	I mean by that. Well, if you're using a GPU
	because you want performance of some kind. Curivest designed in part to allow.
	I would get maximum performance on the GPU. Right, it's obviously, as I said, also designed to make it.
	programmable. Performance is limited by the laws.
	physics and I'll get to that in a moment. And so Kudra is designed.
	do it best to help you work with the hardware.
	than loads of physics to get good performance.
	computing since Scotch 2008 and why
	So this is actually a really interesting point to make. You see what's special about
	is that we make both programming language for the hardware and the hardware
	programming language. This means what only do we get to adjust the programming
	language to match what the hard way can do. But we also get to adjust the hardware.
	So there's more programmable the hardware designers come up with really
	have a staff to overcome limitations like speed of electricity and silicon.
	and could have evolved to allow this clever stuff to be programmable. Literally speaking.
	could it is shaped by the laws of physics?
	So I made another possible
	contentious statement that I want to look at more closely for women.
	One of the best things about this job is that it could really be a co-design between hardware.
	whole talk basically about the CTC last year and I put the link below.
	low as a seamless plunge for my talk, but also because if you're interested.
	gives you a lot more detail and then I'm going to get into right here about the hardware.
	and overcoming physical constraints. Anyway, I won't repeat the whole thing.
	but I will bring up the main points. Let's start with
	system, though, because presumably you paid money and...
	investing time in GPU computing because you want performance.
	from it. So let's look at what that means.
	make up, I hope his lung controversial statement that's getting the best performance.
	is about using all the GPU resources that you can.
	software. Since CUDA is the way you program the DPU directly,
	In other words, the more threads I'm running, the more memory I'm moving,
	calculations I'm making, the better I'm probably doing.
	So these are the feeds and speeds of the Ampede view.
	and the obvious performance metric to look at is flops.