srmojuze :
I am not familiar with Xeon Phi and similar "80-core" or so CPUs but the Wikipedia artcile above mentions Xeon Phi is for HPC and Nvidia Tesla is a competitor.
So in terms of HPC and certainly in terms of enthusiast applications that require so many "cores" - I would put forward that GPGPU is where the real action is at, because (I'm guessing) the increase in "cores" and parallelism in OpenCL, CUDA, etc. is far more efficient/easier than Intel's x86/x64 or similar architectures.
So in terms of HPC and certainly in terms of enthusiast applications that require so many "cores" - I would put forward that GPGPU is where the real action is at, because (I'm guessing) the increase in "cores" and parallelism in OpenCL, CUDA, etc. is far more efficient/easier than Intel's x86/x64 or similar architectures.
The benefit is ease of programmability. Choose your language and your OS. Use existing binaries and libraries (on the new, second gen series).
The disadvantage is that GPUs still have higher raw performance (and higher performance per Watt), since they lack the legacy and single-thread performance optimizations of x86 cores.
So, people who can effectively rewrite their code in CUDA or OpenCL will do better with GPUs, whereas those with legacy code and even some server-oriented apps can easily use Xeon Phi (Knights Landing).
If you're interested in knowing more, there's a ton of info out there.