Earlier quoted context omitted.
This question comes up constantly. Anybody doing anything with more than 16GB, especially data science (think all the R and Python people, all scientists), anybody running a memory cached data store (all server side people on mongo, redis, etc), and anybody doing finance or engineering, wants ECC. Basically anybody doing anything where data persistence is important, and/or where even the slightest chance of silent co…
> Anybody doing anything with more than 16GB, especially data science (think all the R and Python people, all scientists) Not sure I agree. Most science datasets (both from simulations and experiments) are sufficiently noisy that if your scientific end results and conclusions change as a result of even thousands of bitflips in your 16 GB of data, you're Doing It Wrong and your article isn't worth the paper it's print…
There is way too much overreach in this statement. Imagine doing Finite Element or Computational Fluid Dynamics analyses; bitflips of the floating-point values in the field solutions, which could easily make those values completely unphysical, are not the kinds of errors the solvers are written to guard against. In order to do so, you'd need to sanity-check every value, and if you had to use a "guardrail" value, it could easily take a significant number of iterations to recover to the more correct value. Solvers can be easily crashed by corruption of numbers. Sure, if you're lucky enough to have bitflips in low-order mantissa bits, no real harm done. Just don't expect the bitflips to cooperate in this way.
Maybe the "big data" and machine learning crowd don't care about some corrupt values, but most numerical/scientific computing is not so sanguine about corruption.
It is a little frightening how we are moving into significantly larger computational solutions, but are simultaneously increasing our exposure to the fragility and lack of guarantees regarding enormous quantities of perfect bits at all times.