Operations to combat the complexity of shifting from a compute model

 Koduri describes the need for a scalable model and software for scalar, vector, and matrix (as in GPUs) operations to combat the complexity of shifting from a compute model where generality rules to one where heterogeneity does. Generalized computing is fine for keeping things simple from a software perspective when processing power is growing at a steady clip. It didn’t really make sense to design specialized hardware and write software for it if the general-purpose CPU was just going to catch up in 18 months or so. But, absent that kind of metronomic advance, chip and system makers need to optimize their hardware even if it makes life harder for developers and others.

For software makers, this means that they need to write for (and optimize for) a wider variety of hardware types. In some respects, though not others, it’s a throwback to the era before x86 hardware and a small number of operating systems computer science vs engineering the datacenter.

For users, this provides hope of performance and price/performance improvements even in the face of slowing process advances. But it also means they will have to make more choices to optimize their particular applications.