CUDA-enabled applications use the GPU for fine grained data-intensive processing, and the multi-core CPUs for complicated coarse grained tasks such as control and data management.

A CUDA core is a SIMD (Single Instruction, Multiple Data) processing unit found inside your Nvidia graphics card that handles parallel computing tasks; with more CUDA cores, comes the ability to ...

Some other learning tools for GPU programming we’ve seen in the past include this puzzle-based tool which illustrates some of the specific problems GPUs excel at.

The GPU maker correctly foresaw that libraries and algorithms needed to be in place, with a consistent programming model, if GPU compute was to become normal even if not perfectly easy. Quantum ...

You can also use nvprof — included with the CUDA software — to get a lot of detailed information about things running on the GPU. Try putting nvprof in front of the two example gocuda lines above.