We all know CUDA is currently king of the hill when it comes to GPGPU & ML in particular, and that CUDA is an NVIDIA product limited to NVIDIA hardware, and that Apple & NVIDIA “don’t get along” i.e.

Over at the Nvidia blog, Mark Harris has posted a simple introduction to CUDA, the popular parallel computing platform and programming model from NVIDIA. I wrote a previous “Easy Introduction” to CUDA ...

New CUDA 4.0 Release Makes Parallel Programming Easier Unified Virtual Addressing, GPU-to-GPU Communication and Enhanced C++ Template Libraries Enable More Developers to Take Advantage of GPU ...

Everyone knows the old Einstein quote about the definition of insanity—it's doing the same thing over and over again and expecting different results. After a pretty harsh review of the Quadro FX 4800, ...

A graphics card is a hardware component in most PCs that enables you to play games with realistic-looking effects. Two companies currently design and manufacture graphics cores: NVIDIA and ATI, and ...

AMD's GPU solutions have come a long way since the company acquired ATI. The combined companies have competed very well against Nvidia for the past several years, at least at the consumer level. When ...

NVIDIA had told us it would be accelerating its CUDA program to try and get an advantage over its competitors as OpenCL brings general-purpose GPU computing to the mainstream, and it looks like that ...

This year at the International Supercomputing Conference, Nvidia has introduced the new Tesla 10-series computing solutions. Binary compatible and supporting the industry standard language of C, the ...

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 ...

Knowing how and when to use GPGPU in HPEC computing will give embedded designers a better understanding of managing power consumption and load balancing using this powerful processing technology. Two ...