Programmers have been interested in leveraging the highly parallel processing power of video cards to speed up applications that are not graphic in nature for a long time. Here, I explain how to do ...

Getting started with parallel programming is easier than ever. In fact, now you can develop right on your Macbook Pro using its built-in Nvidia GeForce GPU. Over at QuantStart, Valerio Restocchi has ...

This book is very focused on one thing: teaching readers how to develop parallel applications that perform well on NVIDIA’s GPUs using NVIDIA’s CUDA language. The authors do a good job explaining ...

NVIDIA today announced NVIDIA® CUDA® 6, the latest version of the world's most pervasive parallel computing platform and programming model.

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 ...

With that state come several parallel programming approaches; from OpenMP, OpenACC, OpenCL, CUDA, and others. The trick is choosing the right framework for maximum performance and efficiency—but also ...

Nvidia has released a public beta of CUDA 1.1, an update to the company's C-compiler and SDK for developing multi-core and parallel processing applications on GPUs, specifically Nvidia's 8-series GPUs ...

Most notably, the chipmaker announced a compiler source code enabling software developers to add new languages and architecture support to Nvidia’s CUDA parallel programming model.

SUPERCOMPUTING 2007—RENO, NEVADA—NOVEMBER 14, 2007—As the computing industry rapidly moves to multi-core and parallel processing architectures, tomorrow’s ...

Parallel programming looks to level the playing field by leveraging multicore hardware. One size does not fit all, and it never will. ... (CUDA) to handle its SIMT-based GPUs ...