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.

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.

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...