Modern software takes computational speed for granted. But modern microprocessors can only speed up by increasing the number of cores. To take full advantage of multiple cores, software developers ...

Recently, I had the good fortune to present a class at the ACM Conference for Computer Science Educators (SIGCSE). While I definitely shared my enthusiasm for parallel programming, I had two key goals ...

Nvidia has released a public beta of CUDA 1.1, an update to the company's C-compiler and software development kit. CUDA stands for "Compute Unified Device Architecture." It's used for developing ...

AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented ...

NVIDIA’s CUDA is a general purpose parallel computing platform and programming model that accelerates deep learning and other compute-intensive apps by taking advantage of the parallel processing ...

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 ...

From your smartphone to your laptop, today’s tech devices glean their computing power from multi-core processors. Supercomputers contain thousands of cores, and within three to four years a computer ...

Computer chips have stopped getting faster: The regular performance improvements we've come to expect are now the result of chipmakers' adding more cores, or processing units, to their chips, rather ...

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 ...