Wow, I've been reading, on Nvidia's site and elsewhere, about cuda programming on gpus and a lot of the advice involves avoiding a lot of transfers between main memory and the gpu. However, at the transfer rate quoted above, it seems like you have a device that can read all of main memory in a few seconds.
This is great ... however, does this mean all the advice about avoiding memory transfers and the programming style went with that, goes out the window?
Edit: as a beginner, I'm probably butchering the standard advice for c++/cuda programming. However, could someone summarize how things change as you get to more capable processors. A lot of the Nvidia blog posts are geared to stuff valid for all cuda version but since I'm starting fresh with a compute-intense program targeting cuda, I'd like to target the best things cuda can do.
Edit2: While the article is fascinating for the possibilities it's talking about, it's mostly a puff-piece for some (apparently) closed-source software. Anyone know an open source equivalent or some more detailed sample code for doing stuff similar to what MapD claims to do?