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if you opened one up and looked at what was actually in there—processing vertexes in particular, but for some machines, processing the fragments—it was a programmable engine. It’s just that it was not programmable by you; it was programmable by me. From an architecture standpoint, that’s a fairly subtle distinction. What we weren’t doing was selling application development. It”s a little like mobile phones now. In general, they’re not extensible except by a very small set of people, so they appear unprogrammable.

All along, those SGI machines had microcode engines that were programmable; we just weren’t exposing the programmability to the world. Frankly, part of the reason was that we didn’t have control of those components.

We went out to the market and said, “You know, the Intel 860 is the best floating-point-per-dollar solution this time, so we’ll put in one of those and build a microcode engine that runs it.”

Then the next time, we would go out and say, “Mmm, this TI 40-bit floating-point gizmo is the best one, so we’ll use that.” We couldn’t promise the same coding environment generation after generation, so we couldn’t reveal that it was programmable or else our customers would get very upset. We tried that. It actually does upset customers when you let them invest in coding and then sell them another machine that’s faster but doesn’t run their code. So for a variety of sort of tactical reasons, the programmability wasn’t exposed.

The story is more complicated now because there is less programmability in some areas. But the general notion that people woke up eight years ago and said, “Oh, it makes sense to put programmability in these things,” is definitely oversimplifying. This architectural trend has been smoother than that. TD There was, in fact—and I was here for this—an awful lot of resistance from the big players in the GPU business to exposing that programmability. KA I was part of that. TD I like to think that the transition happened because of us in the movie-quality imaging business. We pressed hard for it and demonstrated that if you were going to make high-quality images, this was the way you were going to do it. PH They always knew you were right; it’s just that it was too costly for them to consider. The market opportunity wasn’t there. But the games eventually started getting so sophisticated that there was no way of making them look better without exposing programmability to the [John] Carmacks and [Tim] Sweeneys of the world. KA Games were a big enough market that you could

afford to do it. That’s the part that’s less obvious. It cost a huge amount of engineering, and it took a lot of steps and a lot of years to build this into the marketplace, which is bigger than movies at this point. It costs a lot of money to engineer these things, so it wasn’t like you could just wake up one day and say we ought to do it. It took all these years to build up the capital expenditure capability that an Nvidia or an ATI has to actually do it.

If mistakes had been made along the way—big ones— it wouldn’t have happened. There are lots of examples of marketplaces where there was custom hardware that hasn’t beautifully evolved into the space the way graphics has. I think a lot of that is market opportunity; it’s not pure technology. Those markets just wouldn’t support it. TD If you look at the big computing machines in the world, you see that most of them are devoted to fluid dynamics and electrostatic simulation, for sort of obvious defense-related reasons. PH And n-body calculations. To me, graphics is mostly about simulation. There are basic computational building blocks that go into simulation. To the extent that graphics uses a certain set of those in certain ways, a lot of other people use other sets of those in other ways. Once you start seeing the building blocks designed for simulation in a fairly general-purpose parallel way, you can say, “Yeah, it’s not just for graphics; it could be used for other things.” That’s what other people are starting to find out.

 

TD We’ve heard that GPU performance increases faster than Moore’s law. Is that just low-hanging fruit because of the primitive state of GPU architectures, or is this trend going to continue? Are those CPU and GPU curves going to merge? KA Moore’s law, just to be clear, has to do with transistor count and is formulated, I think, as an economic law that the number of transistors on the most economically produced die size will go up exponentially—and it turned out around 50 percent a year has been the number. So 1. 5 is the compound average growth. But remember, it isn’t a performance law; it is a transistor-count law. TD Sure, but performance is related. KA Performance is related to both transistor count and clock speed, and the clock speed mattered a lot. The clock speed has been going up around 20 percent a year. PH One way to think of it is sort of as a rate-cubed effect. You get a square for the area, and if something shrinks in size by a half, you get four times as many of them, but the clock also goes up by roughly a factor of two. KA It’s not purely linear, but if you go back and look at