NVIDIA’s CUDA is a general purpose parallel computing platform and programming model that accelerates deep learning and other compute-intensive apps by taking advantage of the parallel processing ...

Support for unified memory across CPUs and GPUs in accelerated computing systems is the final piece of a programming puzzle that we have been assembling for about ten years now. Unified memory has a ...

CUDA is a parallel computing programming model for Nvidia GPUs. With the proliferation over the past decade of GPU usage for speeding up applications across HPC, AI and beyond, the ready availability ...

Graphics processing units (GPUs) are traditionally designed to handle graphics computational tasks, such as image and video processing and rendering, 2D and 3D graphics, vectoring, and more.

In Nvidia’s decade and a half push to make GPU acceleration core to all kinds of high performance computing, a key component has been the CUDA parallel computing platform that made it easier for ...

Why it matters: Nvidia introduced CUDA in 2006 as a proprietary API and software layer that eventually became the key to unlocking the immense parallel computing power of GPUs. CUDA plays a major role ...

Today Allinea Software announces availability of its new software release, version 6.1, which offers full support for programming parallel code on the Pascal GPU architecture, CUDA 8 from Nvidia. The ...

In this video from the ECSS Symposium, Abe Stern from NVIDIA presents: CUDA-Python and RAPIDS for blazing fast scientific computing. We will introduce Numba and RAPIDS for GPU programming in Python.