In the HPC industry, it seems that history is always doomed to repeat itself. The CPU isn’t fast enough, so we add a co-processor to handle the really serious calculations. Then process technology improves, we can fit more transistors on a chip and the co-processor is moved onto the CPU die.
For the last half-decade, we’ve been in the midst of this cycle. Researchers realized that graphics cards (GPUs) were basically huge vector processors. Why make a couple CPU cores churn away on the math when the graphics card has a couple hundred cores? Thus we have General-Purpose GPU computing (GPGPU). Some have resisted this trend, but a lot of very serious scientists and institutions are using GPUs extensively. Like many cutting-edge technologies there is constant change and it takes more effort to get everything working, but these co-processors offer significant benefits.
I wasn’t really around for the previous batch of co-processors in the 1980s, but it’s clear that this time there is more at stake. Multi-billion dollar corporations (with billion-dollar R&D budgets) are building the co-processors. Astronomers, biologists, physicists, chemists, doctors, surgeons, mathematicians, engineers, and bankers are taking advantage of the performance. The fields of data analytics and computational modelling are serious business. Some in the life-sciences fields are calling them the “computational microscope” because they offer so much potential.