In a nutshell, NVIDIA's CUDA architecture provides developers with a way to efficiently program NVIDIA GPUs using a very easy-to-read, C-like syntax.

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 ...

Nvidia has unveiled a new compiler source code to add new languages to its parallel programming and boost the adoption of GPUs.

NVIDIA first released its CUDA programming technology in 2007, providing software developers a programming environment based on the industry-standard C language for easy creation of applications ...

A hands-on introduction to parallel programming and optimizations for 1000+ core GPU processors, their architecture, the CUDA programming model, and performance analysis. Students implement various ...

"CUDA C++ is just one of the ways you can create massively parallel applications with CUDA. It lets you use the powerful C++ programming language to develop high performance algorithms accelerated by ...

COMP_SCI 368, 468: Programming Massively Parallel Processors with CUDA VIEW ALL COURSE TIMES AND SESSIONS Prerequisites Completed CS 213 or CS/CE Graduate standing or Consent of Instructor Description ...

PGI Accelerator compilers are currently available for C99 and Fortran 95/2003. CUDA Fortran, a Fortran 95/2003 analog to NVIDIA CUDA C, was developed by PGI in cooperation with NVIDIA in 2009. CUDA ...

In this video from the University of Houston CACDS HPC Workshop, Michael Wolfe from PGI presents: OpenACC Interoperability with CUDA C and Fortran. Developed by PGI, Cray, and NVIDIA, the OpenACC ...

According to NVIDIA , the CUDA C programming environment simplifies many-core programming and enhances performance by offloading computationally-intensive activities from the CPU to the GPU.