Nvidia's release of its CUDA (Compute Unified Device Architecture) SDK in 2007 helped usher in an era of explosive growth for general-purpose programming on GPUs (often referred to as GPGPU).

As the programmability and performance of modern graphics processing units (GPUs) increase, many application developers are looking to graphics hardware to solve computationally intensive problems ...

What looked like general purpose in the past looks like a limited ASIC by today’s standards, and this is as true for GPUs and FPGAs as it is for CPUs. There is much talk about the era of general ...

The introduction of graphics processing units (GPUs) for general-purpose computing is perhaps the most important recent development for computing, and if you want to develop some new Python skills ...

It’s been a big part of the push to use GPUs for general purpose computing, and in some ways, competitor AMD has thusly been left out in the cold. However, with more demand for GPU computation ...

The versatile Nema™ Platform for General-Purpose Computing on an embedded GPU (GPGPU) is designed by Think Silicon for excellent performance with ultra-low energy consumption and silicon footprint, ...

No longer would GPUs be restricted to graphics processing. With CUDA, anything that could run on a CPU could also run on a GPU. Those applications with sufficiently exposed parallelism could ...

Graphical-processing unit (GPU)-accelerated computing has reached maturity for professional, scientific computing applications. One example of this is the recent GPU-accelerated thermal application ...

Nvidia's release of its CUDA (Compute Unified Device Architecture) SDK in 2007 helped usher in an era of explosive growth for general-purpose programming on GPUs (often referred to as GPGPU).

Over the past 10 years, graphics processing units have become ubiquitous in desktop computers. GPUs feature enormous computing power and high memory bandwidth-both essential to sustaining high ...