Earlier quoted context omitted.
The interesting aspect of scaling up is that it doesn't matter how fast you are at individual single-core computation. Fast single core computation, or even SIMD GPU processing, is largely an "easy" problem: get a stream of data going, or get a chunk of data into the system, and work away on it. What makes scaling up hard is moving data around. Once you have more than a single computer, there is no way you can easily…
> Before Erlang, Tandem systems built hardware/software with many of the same ideas in them. Indeed, Jim-gray's (from tandem) paper 'why computers stop and what we can do about it' is an quite good. It contains a detailed report of machine failure including s/w and h/w and details techniques for reducing the mtbf by these. Erlang's language and runtime seems to have picked seminal ideas from here...
For your convenience: http://www.hpl.hp.com/techreports/tandem/TR-85.7.pdf