Originally published at: https://developer.nvidia.com/blog/learn-gpu-programming-free-on-demand-gpu-training/
As CUDA Educator at NVIDIA, I work to give access to massively parallel programming education & training to everyone, whether or not they have access to GPUs in their own machines. This is why, in partnership with qwikLABS, NVIDIA has made the hands-on content we use to train thousands of developers at the Supercomputing Conference…
Cool payable stuff - looking for closing CUDA openAccess for GTX 10 GPU's... Is not still reproducted=revisited GTX780 6GB with 100$ price enough profitable?
Post Scriptum: integrate CPU with HBM2 GPU global memory - I would do check 2core ARMv7. Additionaly one will only need a 40GE QSFP+; single PCIe3.0x16 slot for external RAID controler, few USB's and other standard=typical peripherals. Highly - complicated locksmithing for vaseline oil cooling will be necessary. It should do the standard - puzzle idea job with comparision to next-generation few-years-buggy hybrid processor.
Post Post Scriptum: CUDA is great, publically closing it, wil automate OpenCL switching instead Microsoft-like SDK's enforcing. Why STL container vector<vector<float>> is not runnable on GPU?
P.P.P.S.: Java is fast because it is written in C++. But I am only an amateur.