Not every developer who might like to learn CUDA has access to an NVIDIA GPU, so by expanding the hardware that CUDA can target to include x86, you'll be able to get your feet wet with CUDA on ...

If you can’t find CUDA library routines to accelerate your programs, you’ll have to try your hand at low-level CUDA programming. That’s much easier now than it was when I first tried it in ...

Using some system-level magic in the CUDA device driver, data allocated in this way is paged back and forth between CPU system memory and GPU device memory more or less on demand. It’s not strictly ...

In addition, multi-GPU sharing by a single CPU thread is enabled, letting a single CPU host thread access all GPUs in a system. Developers can coordinate work across multiple GPUs.

What you’ll learn: Differences between CUDA and ROCm. What are the strengths of each platform? Graphics processing units are traditionally designed to handle graphics computational tasks, such ...