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 ...

This project implements and compares sequential and CUDA-based parallel K-means clustering algorithms to evaluate performance improvements from GPU acceleration. Initialize k centroids randomly.

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 ...

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 ...

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 ...

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 ...

Programming for multicore shared memory processors will utilize the popular existing parallel programming technique of POSIX threads, and compiler-based OpenMP, supported by the latest suite of Intel ...

SANTA CLARA, CA--(Marketwired - Nov 14, 2013) - NVIDIA today announced NVIDIA® CUDA® 6, the latest version of the world's most pervasive parallel computing platform and programming model. The CUDA 6 ...