&quot;The most powerful GPU currently available is the NVIDIA Tesla K80 Accelerator, with up to 8.74 teraflops of compute performance and nearly 500 GB&#x2F;sec of memory bandwidth.&quot;<p>Wow, I&#x27;ve been reading, on Nvidia&#x27;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.<p>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?<p>Edit: as a beginner, I&#x27;m probably butchering the standard advice for c++&#x2F;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&#x27;m starting fresh with a compute-intense program targeting cuda, I&#x27;d like to target the best things cuda can do.<p>Edit2: While the article is fascinating for the possibilities it&#x27;s talking about, it&#x27;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?