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 ...

Through parallel programming, CUDA makes use of the GPU's power to speed up computations. Consider it as a group of specialist employees managing several projects at once.

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.

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 ...

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.

Through parallel programming, CUDA makes use of the GPU's power to speed up computations. Consider it as a group of specialist employees managing several projects at once.

Since parallel programming is all about speed, we will learn ways to measure execution performance and speedup through parallelization. In terms of practical skills, high-performance (non-shared ...

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 ...

In addition to the new features, the CUDA 6 platform offers a full suite of programming tools, GPU-accelerated math libraries, documentation and programming guides. Version 6 of the CUDA Toolkit is ...