", launched the basics of CUDA programming by exhibiting how to write down a easy program that allocated two arrays of numbers in memory accessible to the GPU after which added them together on the GPU. To do this, I introduced you to Unified Memory, which makes it very simple to allocate and entry information that may be used by code working on any processor within the system, CPU or GPU. I finished that submit with just a few easy "exercises", considered one of which inspired you to run on a recent Pascal-based GPU to see what occurs. I hoped that readers would try it and touch upon the results, and a few of you did! I recommended this for 2 causes. First, because Pascal GPUs such because the NVIDIA Titan X and the NVIDIA Tesla P100 are the primary GPUs to include the Web page Migration Engine, which is hardware help for Unified Memory web page faulting and migration.
The second cause is that it offers a great opportunity to be taught extra about Unified Memory. Fast GPU, Quick Memory… Right! But let’s see. First, I’ll reprint the outcomes of operating on two NVIDIA Kepler GPUs (one in my laptop and one in a server). Now let’s try running on a extremely fast Tesla P100 accelerator, primarily based on the Pascal GP100 GPU. Hmmmm, that’s under 6 GB/s: slower than running on my laptop’s Kepler-based mostly GeForce GPU. Don’t be discouraged, though