Computer Graphics World

Edition 3

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e d i t i o n 3 , 2 0 1 8 | c g w 7 1 f ever there was an example of, "If you build it, they will come," the GPU is the world's best. The "they" referred to here are the applications. When graphics semiconductor suppliers such as 3Dlabs, ATI, Nvidia, TI, and others began exploiting the density, speed, and economic benefits of Moore's law for the unlimited demand of computer graphics, they had one goal: build a graphics accelerator that would get us closer to photore- alistic, real time rendering. And although a fixed program, hard-wired function accelerator would be cheaper, less expen- sive, and easier to control than a programmable one, the nature of CG is such that new algo- rithms, tricks, and functions are being developed almost weekly, and have been since the 1970s. The trade-off of LUT-based, predetermined CG functions in a chip vs. a general-purpose CG device were rapidly becoming overwhelmingly in favor of a programmable solution. TI led the parade in 1986 with the TMS34010, and in 1999, 3Dlabs introduced the geometry pro- cessor GLINT – the first GPU. Shortly thereaer, Nvidia introduced the NV10 and marketed it as the first GPU (it wasn't, but Nvidia has always been an aggressive marketing company). And right on the heels of Nvidia's announcement, ATI revealed its VPU, which was ATI's attempt to offer the same technology but with a differ- entiated name. By this time, TI had faded from the scene, being unwilling to continue to invest in R&D for graphics – a move I suspect they have regretted many times. The concept of using VLSI (very large scale integration) and lots of ALUs (arithmetic log- ic units) can be credited to Hen- ry Fuchs, who, in 1981, proposed the Pixel Planes project at the University of North Carolina. A little later, Bill Dally did founda- tional work in stream processing in 1995 at MIT on the Imagine project, and in 1996, he moved to Stanford University. He's now CTO at Nvidia. Dally's work showed that a number of applications ranging from wireless base- band processing, 3D graphics, encryption, and IP forwarding to video processing could take advantage of the efficien- cy of stream processing. This research inspired other designs, such as GPUs from ATI Technologies as well as the Cell microprocessor from Sony, Toshiba, and IBM. Stream processors, SIMDs, parallel processors, and GPUs are all closely-related family members. At Stanford, the notion of doing stream computing using ATI GPUs was tried and proven successful, albeit difficult to program via OpenGL. To overcome that obstacle, Nvidia developed a C-like parallel processing language known as CUDA. A year later, Apple and Khronos introduced OpenCL. By 2006, parallel processing on the GPU had been established as a new paradigm, and one with tremendous potential. Wherever an application was begging for parallel processing, that's where GPU computing took off. In the past, parallel processing was done with huge numbers of processors, such as an x86, but they were very expensive and difficult to program. The GPU as a dedicated, single-purpose processor offered much greater compute density per dollar, and it's been subsequently exploited in many math acceleration tasks – GPUs designed for gaming are now crucial to HPC, supercom- puting, and AI. A G A M E C H A N G E R But make no mistake, gaming is still king. There are tens of mil- lions of gamers (some estimates have exceeded 100 million), and every year they buy more than 50 million graphics boards and 20 million laptops for gaming. That's in addition to the 25 million consoles with powerful GPUs in them. Contrast that with the 3.5 million workstations and the less than one million GPUs bought for data centers and supercomputers in 2017. Those ratios will change as AI training increases, but it won't double. As more companies be- come more data-intensive, they THE EVOLUTION OF THE GPU IF YOU BUILD IT, THEY WILL COME BY JON PEDDIE I AERODYNAMICS OF A COMMERCIAL AIRCRAFT. VIBRATION ANALYSIS OF A JET ENGINE.

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