CPU-GPU based cluster computing in today's modern world encompasses the domain of complex and high-intensity computation. To exploit the efficient resource utilization of a cluster, traditional ...

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 ...

"Programmers have always found it hard to program GPUs," Sumit Gupta, the general manager of Nvidia's HPC-focused Tesla biz told The Reg, "and one of the biggest reasons for that – in fact, this is ...

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 ...

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.

Graphics processing units (GPUs) originally designed for computer video cards have emerged as the most powerful chip in a high-performance workstation. Unlike multicore CPU architectures, which ...